Computer vision applications for the detection or analysis of tuberculosis using digitised human lung tissue images - a systematic review
Objective To conduct a systematic review of the computer vision applications that detect, diagnose, or analyse tuberculosis (TB) pathology or bacilli using digitised human lung tissue images either through automatic or semi-automatic methods. We categorised the computer vision platform into four tec...
Uložené v:
| Vydané v: | BMC medical imaging Ročník 24; číslo 1; s. 298 - 15 |
|---|---|
| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
London
BioMed Central
05.11.2024
BioMed Central Ltd Springer Nature B.V BMC |
| Predmet: | |
| ISSN: | 1471-2342, 1471-2342 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Objective
To conduct a systematic review of the computer vision applications that detect, diagnose, or analyse
tuberculosis
(TB) pathology or
bacilli
using digitised human lung tissue images either through automatic or semi-automatic methods. We categorised the computer vision platform into four technologies: image processing, object/pattern recognition, computer graphics, and deep learning. In this paper, the focus is on image processing and deep learning (DL) applications for either 2D or 3D digitised human lung tissue images. This review is useful for establishing a common practice in TB analysis using human lung tissue as well as identifying opportunities for further research in this space. The review brings attention to the state-of-art techniques for detecting TB, with emphasis on the challenges and limitations of the current techniques. The ultimate goal is to promote the development of more efficient and accurate algorithms for the detection or analysis of TB, and raise awareness about the importance of early detection.
Design
We searched five databases and Google Scholar for articles published between January 2017 and December 2022 that focus on
Mycobacterium tuberculosis
detection, or
tuberculosis
pathology using digitised human lung tissue images. Details regarding design, image processing and computer-aided techniques, deep learning models, and datasets were collected and summarised. Discussions, analysis, and comparisons of state-of-the-art methods are provided to help guide future research. Further, a brief update on the relevant techniques and their performance is provided.
Results
Several studies have been conducted to develop automated and AI-assisted methods for diagnosing
Mtb
and TB pathology from digitised human lung tissue images. Some studies presented a completely automated method of diagnosis, while other studies developed AI-assisted diagnostic methods. Low-level focus areas included the development of a novel
μ
CT scanner for soft tissue image contract, and use of multiresolution computed tomography to analyse the 3D structure of the human lung. High-level focus areas included the investigation the effects of aging on the number and size of small airways in the lungs using CT and whole lung high-resolution
μ
CT, and the 3D microanatomy characterisation of human
tuberculosis
lung using
μ
CT in conjunction with histology and immunohistochemistry. Additionally, a novel method for acquiring high-resolution 3D images of human lung structure and topology is also presented.
Conclusion
The literature indicates that post 1950s, TB was predominantly studied using animal models even though no animal model reflects the full spectrum of human pulmonary TB disease and does not reproducibly transmit
Mtb
infection to other animals (Hunter, 2011). This explains why there are very few studies that used human lung tissue for detection or analysis of
Mtb
. Nonetheless, we found 10 studies that used human tissues (predominately lung) of which five studies proposed machine learning (ML) models for the detection of
bacilli
and the other five used CT on human lung tissue scanned ex-vivo. |
|---|---|
| AbstractList | ObjectiveTo conduct a systematic review of the computer vision applications that detect, diagnose, or analyse tuberculosis (TB) pathology or bacilli using digitised human lung tissue images either through automatic or semi-automatic methods. We categorised the computer vision platform into four technologies: image processing, object/pattern recognition, computer graphics, and deep learning. In this paper, the focus is on image processing and deep learning (DL) applications for either 2D or 3D digitised human lung tissue images. This review is useful for establishing a common practice in TB analysis using human lung tissue as well as identifying opportunities for further research in this space. The review brings attention to the state-of-art techniques for detecting TB, with emphasis on the challenges and limitations of the current techniques. The ultimate goal is to promote the development of more efficient and accurate algorithms for the detection or analysis of TB, and raise awareness about the importance of early detection.DesignWe searched five databases and Google Scholar for articles published between January 2017 and December 2022 that focus on Mycobacterium tuberculosis detection, or tuberculosis pathology using digitised human lung tissue images. Details regarding design, image processing and computer-aided techniques, deep learning models, and datasets were collected and summarised. Discussions, analysis, and comparisons of state-of-the-art methods are provided to help guide future research. Further, a brief update on the relevant techniques and their performance is provided.ResultsSeveral studies have been conducted to develop automated and AI-assisted methods for diagnosing Mtb and TB pathology from digitised human lung tissue images. Some studies presented a completely automated method of diagnosis, while other studies developed AI-assisted diagnostic methods. Low-level focus areas included the development of a novel \(\upmu\)CT scanner for soft tissue image contract, and use of multiresolution computed tomography to analyse the 3D structure of the human lung. High-level focus areas included the investigation the effects of aging on the number and size of small airways in the lungs using CT and whole lung high-resolution \(\upmu\)CT, and the 3D microanatomy characterisation of human tuberculosis lung using \(\upmu\)CT in conjunction with histology and immunohistochemistry. Additionally, a novel method for acquiring high-resolution 3D images of human lung structure and topology is also presented.ConclusionThe literature indicates that post 1950s, TB was predominantly studied using animal models even though no animal model reflects the full spectrum of human pulmonary TB disease and does not reproducibly transmit Mtb infection to other animals (Hunter, 2011). This explains why there are very few studies that used human lung tissue for detection or analysis of Mtb. Nonetheless, we found 10 studies that used human tissues (predominately lung) of which five studies proposed machine learning (ML) models for the detection of bacilli and the other five used CT on human lung tissue scanned ex-vivo. To conduct a systematic review of the computer vision applications that detect, diagnose, or analyse tuberculosis (TB) pathology or bacilli using digitised human lung tissue images either through automatic or semi-automatic methods. We categorised the computer vision platform into four technologies: image processing, object/pattern recognition, computer graphics, and deep learning. In this paper, the focus is on image processing and deep learning (DL) applications for either 2D or 3D digitised human lung tissue images. This review is useful for establishing a common practice in TB analysis using human lung tissue as well as identifying opportunities for further research in this space. The review brings attention to the state-of-art techniques for detecting TB, with emphasis on the challenges and limitations of the current techniques. The ultimate goal is to promote the development of more efficient and accurate algorithms for the detection or analysis of TB, and raise awareness about the importance of early detection.OBJECTIVETo conduct a systematic review of the computer vision applications that detect, diagnose, or analyse tuberculosis (TB) pathology or bacilli using digitised human lung tissue images either through automatic or semi-automatic methods. We categorised the computer vision platform into four technologies: image processing, object/pattern recognition, computer graphics, and deep learning. In this paper, the focus is on image processing and deep learning (DL) applications for either 2D or 3D digitised human lung tissue images. This review is useful for establishing a common practice in TB analysis using human lung tissue as well as identifying opportunities for further research in this space. The review brings attention to the state-of-art techniques for detecting TB, with emphasis on the challenges and limitations of the current techniques. The ultimate goal is to promote the development of more efficient and accurate algorithms for the detection or analysis of TB, and raise awareness about the importance of early detection.We searched five databases and Google Scholar for articles published between January 2017 and December 2022 that focus on Mycobacterium tuberculosis detection, or tuberculosis pathology using digitised human lung tissue images. Details regarding design, image processing and computer-aided techniques, deep learning models, and datasets were collected and summarised. Discussions, analysis, and comparisons of state-of-the-art methods are provided to help guide future research. Further, a brief update on the relevant techniques and their performance is provided.DESIGNWe searched five databases and Google Scholar for articles published between January 2017 and December 2022 that focus on Mycobacterium tuberculosis detection, or tuberculosis pathology using digitised human lung tissue images. Details regarding design, image processing and computer-aided techniques, deep learning models, and datasets were collected and summarised. Discussions, analysis, and comparisons of state-of-the-art methods are provided to help guide future research. Further, a brief update on the relevant techniques and their performance is provided.Several studies have been conducted to develop automated and AI-assisted methods for diagnosing Mtb and TB pathology from digitised human lung tissue images. Some studies presented a completely automated method of diagnosis, while other studies developed AI-assisted diagnostic methods. Low-level focus areas included the development of a novel μ CT scanner for soft tissue image contract, and use of multiresolution computed tomography to analyse the 3D structure of the human lung. High-level focus areas included the investigation the effects of aging on the number and size of small airways in the lungs using CT and whole lung high-resolution μ CT, and the 3D microanatomy characterisation of human tuberculosis lung using μ CT in conjunction with histology and immunohistochemistry. Additionally, a novel method for acquiring high-resolution 3D images of human lung structure and topology is also presented.RESULTSSeveral studies have been conducted to develop automated and AI-assisted methods for diagnosing Mtb and TB pathology from digitised human lung tissue images. Some studies presented a completely automated method of diagnosis, while other studies developed AI-assisted diagnostic methods. Low-level focus areas included the development of a novel μ CT scanner for soft tissue image contract, and use of multiresolution computed tomography to analyse the 3D structure of the human lung. High-level focus areas included the investigation the effects of aging on the number and size of small airways in the lungs using CT and whole lung high-resolution μ CT, and the 3D microanatomy characterisation of human tuberculosis lung using μ CT in conjunction with histology and immunohistochemistry. Additionally, a novel method for acquiring high-resolution 3D images of human lung structure and topology is also presented.The literature indicates that post 1950s, TB was predominantly studied using animal models even though no animal model reflects the full spectrum of human pulmonary TB disease and does not reproducibly transmit Mtb infection to other animals (Hunter, 2011). This explains why there are very few studies that used human lung tissue for detection or analysis of Mtb. Nonetheless, we found 10 studies that used human tissues (predominately lung) of which five studies proposed machine learning (ML) models for the detection of bacilli and the other five used CT on human lung tissue scanned ex-vivo.CONCLUSIONThe literature indicates that post 1950s, TB was predominantly studied using animal models even though no animal model reflects the full spectrum of human pulmonary TB disease and does not reproducibly transmit Mtb infection to other animals (Hunter, 2011). This explains why there are very few studies that used human lung tissue for detection or analysis of Mtb. Nonetheless, we found 10 studies that used human tissues (predominately lung) of which five studies proposed machine learning (ML) models for the detection of bacilli and the other five used CT on human lung tissue scanned ex-vivo. Objective To conduct a systematic review of the computer vision applications that detect, diagnose, or analyse tuberculosis (TB) pathology or bacilli using digitised human lung tissue images either through automatic or semi-automatic methods. We categorised the computer vision platform into four technologies: image processing, object/pattern recognition, computer graphics, and deep learning. In this paper, the focus is on image processing and deep learning (DL) applications for either 2D or 3D digitised human lung tissue images. This review is useful for establishing a common practice in TB analysis using human lung tissue as well as identifying opportunities for further research in this space. The review brings attention to the state-of-art techniques for detecting TB, with emphasis on the challenges and limitations of the current techniques. The ultimate goal is to promote the development of more efficient and accurate algorithms for the detection or analysis of TB, and raise awareness about the importance of early detection. Design We searched five databases and Google Scholar for articles published between January 2017 and December 2022 that focus on Mycobacterium tuberculosis detection, or tuberculosis pathology using digitised human lung tissue images. Details regarding design, image processing and computer-aided techniques, deep learning models, and datasets were collected and summarised. Discussions, analysis, and comparisons of state-of-the-art methods are provided to help guide future research. Further, a brief update on the relevant techniques and their performance is provided. Results Several studies have been conducted to develop automated and AI-assisted methods for diagnosing Mtb and TB pathology from digitised human lung tissue images. Some studies presented a completely automated method of diagnosis, while other studies developed AI-assisted diagnostic methods. Low-level focus areas included the development of a novel μ CT scanner for soft tissue image contract, and use of multiresolution computed tomography to analyse the 3D structure of the human lung. High-level focus areas included the investigation the effects of aging on the number and size of small airways in the lungs using CT and whole lung high-resolution μ CT, and the 3D microanatomy characterisation of human tuberculosis lung using μ CT in conjunction with histology and immunohistochemistry. Additionally, a novel method for acquiring high-resolution 3D images of human lung structure and topology is also presented. Conclusion The literature indicates that post 1950s, TB was predominantly studied using animal models even though no animal model reflects the full spectrum of human pulmonary TB disease and does not reproducibly transmit Mtb infection to other animals (Hunter, 2011). This explains why there are very few studies that used human lung tissue for detection or analysis of Mtb . Nonetheless, we found 10 studies that used human tissues (predominately lung) of which five studies proposed machine learning (ML) models for the detection of bacilli and the other five used CT on human lung tissue scanned ex-vivo. Objective To conduct a systematic review of the computer vision applications that detect, diagnose, or analyse tuberculosis (TB) pathology or bacilli using digitised human lung tissue images either through automatic or semi-automatic methods. We categorised the computer vision platform into four technologies: image processing, object/pattern recognition, computer graphics, and deep learning. In this paper, the focus is on image processing and deep learning (DL) applications for either 2D or 3D digitised human lung tissue images. This review is useful for establishing a common practice in TB analysis using human lung tissue as well as identifying opportunities for further research in this space. The review brings attention to the state-of-art techniques for detecting TB, with emphasis on the challenges and limitations of the current techniques. The ultimate goal is to promote the development of more efficient and accurate algorithms for the detection or analysis of TB, and raise awareness about the importance of early detection. Design We searched five databases and Google Scholar for articles published between January 2017 and December 2022 that focus on Mycobacterium tuberculosis detection, or tuberculosis pathology using digitised human lung tissue images. Details regarding design, image processing and computer-aided techniques, deep learning models, and datasets were collected and summarised. Discussions, analysis, and comparisons of state-of-the-art methods are provided to help guide future research. Further, a brief update on the relevant techniques and their performance is provided. Results Several studies have been conducted to develop automated and AI-assisted methods for diagnosing Mtb and TB pathology from digitised human lung tissue images. Some studies presented a completely automated method of diagnosis, while other studies developed AI-assisted diagnostic methods. Low-level focus areas included the development of a novel [formula omitted]CT scanner for soft tissue image contract, and use of multiresolution computed tomography to analyse the 3D structure of the human lung. High-level focus areas included the investigation the effects of aging on the number and size of small airways in the lungs using CT and whole lung high-resolution [formula omitted]CT, and the 3D microanatomy characterisation of human tuberculosis lung using [formula omitted]CT in conjunction with histology and immunohistochemistry. Additionally, a novel method for acquiring high-resolution 3D images of human lung structure and topology is also presented. Conclusion The literature indicates that post 1950s, TB was predominantly studied using animal models even though no animal model reflects the full spectrum of human pulmonary TB disease and does not reproducibly transmit Mtb infection to other animals (Hunter, 2011). This explains why there are very few studies that used human lung tissue for detection or analysis of Mtb. Nonetheless, we found 10 studies that used human tissues (predominately lung) of which five studies proposed machine learning (ML) models for the detection of bacilli and the other five used CT on human lung tissue scanned ex-vivo. Keywords: Human lung tissue, Tuberculosis, Image analysis, Deep learning To conduct a systematic review of the computer vision applications that detect, diagnose, or analyse tuberculosis (TB) pathology or bacilli using digitised human lung tissue images either through automatic or semi-automatic methods. We categorised the computer vision platform into four technologies: image processing, object/pattern recognition, computer graphics, and deep learning. In this paper, the focus is on image processing and deep learning (DL) applications for either 2D or 3D digitised human lung tissue images. This review is useful for establishing a common practice in TB analysis using human lung tissue as well as identifying opportunities for further research in this space. The review brings attention to the state-of-art techniques for detecting TB, with emphasis on the challenges and limitations of the current techniques. The ultimate goal is to promote the development of more efficient and accurate algorithms for the detection or analysis of TB, and raise awareness about the importance of early detection. We searched five databases and Google Scholar for articles published between January 2017 and December 2022 that focus on Mycobacterium tuberculosis detection, or tuberculosis pathology using digitised human lung tissue images. Details regarding design, image processing and computer-aided techniques, deep learning models, and datasets were collected and summarised. Discussions, analysis, and comparisons of state-of-the-art methods are provided to help guide future research. Further, a brief update on the relevant techniques and their performance is provided. Several studies have been conducted to develop automated and AI-assisted methods for diagnosing Mtb and TB pathology from digitised human lung tissue images. Some studies presented a completely automated method of diagnosis, while other studies developed AI-assisted diagnostic methods. Low-level focus areas included the development of a novel [formula omitted]CT scanner for soft tissue image contract, and use of multiresolution computed tomography to analyse the 3D structure of the human lung. High-level focus areas included the investigation the effects of aging on the number and size of small airways in the lungs using CT and whole lung high-resolution [formula omitted]CT, and the 3D microanatomy characterisation of human tuberculosis lung using [formula omitted]CT in conjunction with histology and immunohistochemistry. Additionally, a novel method for acquiring high-resolution 3D images of human lung structure and topology is also presented. The literature indicates that post 1950s, TB was predominantly studied using animal models even though no animal model reflects the full spectrum of human pulmonary TB disease and does not reproducibly transmit Mtb infection to other animals (Hunter, 2011). This explains why there are very few studies that used human lung tissue for detection or analysis of Mtb. Nonetheless, we found 10 studies that used human tissues (predominately lung) of which five studies proposed machine learning (ML) models for the detection of bacilli and the other five used CT on human lung tissue scanned ex-vivo. Abstract Objective To conduct a systematic review of the computer vision applications that detect, diagnose, or analyse tuberculosis (TB) pathology or bacilli using digitised human lung tissue images either through automatic or semi-automatic methods. We categorised the computer vision platform into four technologies: image processing, object/pattern recognition, computer graphics, and deep learning. In this paper, the focus is on image processing and deep learning (DL) applications for either 2D or 3D digitised human lung tissue images. This review is useful for establishing a common practice in TB analysis using human lung tissue as well as identifying opportunities for further research in this space. The review brings attention to the state-of-art techniques for detecting TB, with emphasis on the challenges and limitations of the current techniques. The ultimate goal is to promote the development of more efficient and accurate algorithms for the detection or analysis of TB, and raise awareness about the importance of early detection. Design We searched five databases and Google Scholar for articles published between January 2017 and December 2022 that focus on Mycobacterium tuberculosis detection, or tuberculosis pathology using digitised human lung tissue images. Details regarding design, image processing and computer-aided techniques, deep learning models, and datasets were collected and summarised. Discussions, analysis, and comparisons of state-of-the-art methods are provided to help guide future research. Further, a brief update on the relevant techniques and their performance is provided. Results Several studies have been conducted to develop automated and AI-assisted methods for diagnosing Mtb and TB pathology from digitised human lung tissue images. Some studies presented a completely automated method of diagnosis, while other studies developed AI-assisted diagnostic methods. Low-level focus areas included the development of a novel $$\upmu$$ μ CT scanner for soft tissue image contract, and use of multiresolution computed tomography to analyse the 3D structure of the human lung. High-level focus areas included the investigation the effects of aging on the number and size of small airways in the lungs using CT and whole lung high-resolution $$\upmu$$ μ CT, and the 3D microanatomy characterisation of human tuberculosis lung using $$\upmu$$ μ CT in conjunction with histology and immunohistochemistry. Additionally, a novel method for acquiring high-resolution 3D images of human lung structure and topology is also presented. Conclusion The literature indicates that post 1950s, TB was predominantly studied using animal models even though no animal model reflects the full spectrum of human pulmonary TB disease and does not reproducibly transmit Mtb infection to other animals (Hunter, 2011). This explains why there are very few studies that used human lung tissue for detection or analysis of Mtb. Nonetheless, we found 10 studies that used human tissues (predominately lung) of which five studies proposed machine learning (ML) models for the detection of bacilli and the other five used CT on human lung tissue scanned ex-vivo. To conduct a systematic review of the computer vision applications that detect, diagnose, or analyse tuberculosis (TB) pathology or bacilli using digitised human lung tissue images either through automatic or semi-automatic methods. We categorised the computer vision platform into four technologies: image processing, object/pattern recognition, computer graphics, and deep learning. In this paper, the focus is on image processing and deep learning (DL) applications for either 2D or 3D digitised human lung tissue images. This review is useful for establishing a common practice in TB analysis using human lung tissue as well as identifying opportunities for further research in this space. The review brings attention to the state-of-art techniques for detecting TB, with emphasis on the challenges and limitations of the current techniques. The ultimate goal is to promote the development of more efficient and accurate algorithms for the detection or analysis of TB, and raise awareness about the importance of early detection. We searched five databases and Google Scholar for articles published between January 2017 and December 2022 that focus on Mycobacterium tuberculosis detection, or tuberculosis pathology using digitised human lung tissue images. Details regarding design, image processing and computer-aided techniques, deep learning models, and datasets were collected and summarised. Discussions, analysis, and comparisons of state-of-the-art methods are provided to help guide future research. Further, a brief update on the relevant techniques and their performance is provided. Several studies have been conducted to develop automated and AI-assisted methods for diagnosing Mtb and TB pathology from digitised human lung tissue images. Some studies presented a completely automated method of diagnosis, while other studies developed AI-assisted diagnostic methods. Low-level focus areas included the development of a novel CT scanner for soft tissue image contract, and use of multiresolution computed tomography to analyse the 3D structure of the human lung. High-level focus areas included the investigation the effects of aging on the number and size of small airways in the lungs using CT and whole lung high-resolution CT, and the 3D microanatomy characterisation of human tuberculosis lung using CT in conjunction with histology and immunohistochemistry. Additionally, a novel method for acquiring high-resolution 3D images of human lung structure and topology is also presented. The literature indicates that post 1950s, TB was predominantly studied using animal models even though no animal model reflects the full spectrum of human pulmonary TB disease and does not reproducibly transmit Mtb infection to other animals (Hunter, 2011). This explains why there are very few studies that used human lung tissue for detection or analysis of Mtb. Nonetheless, we found 10 studies that used human tissues (predominately lung) of which five studies proposed machine learning (ML) models for the detection of bacilli and the other five used CT on human lung tissue scanned ex-vivo. |
| ArticleNumber | 298 |
| Audience | Academic |
| Author | Steyn, Adrie J. C. Lumamba, Kapongo D. Naicker, Delon Gwetu, Mandlenkosi Wells, Gordon Naidoo, Threnesan |
| Author_xml | – sequence: 1 givenname: Kapongo D. surname: Lumamba fullname: Lumamba, Kapongo D. email: 221027473@stu.ukzn.ac.za organization: School of Mathematics, Statistics and Computer Science, University of Kwazulu Natal (UKZN), Africa Health Research Institute, UKZN – sequence: 2 givenname: Gordon surname: Wells fullname: Wells, Gordon organization: Africa Health Research Institute, UKZN – sequence: 3 givenname: Delon surname: Naicker fullname: Naicker, Delon organization: Africa Health Research Institute, UKZN – sequence: 4 givenname: Threnesan surname: Naidoo fullname: Naidoo, Threnesan organization: Africa Health Research Institute, UKZN, Department of Forensic and Legal Medicine, Walter Sisulu University – sequence: 5 givenname: Adrie J. C. surname: Steyn fullname: Steyn, Adrie J. C. organization: Africa Health Research Institute, UKZN, Department of Microbiology, University of Alabama at Birmingham – sequence: 6 givenname: Mandlenkosi surname: Gwetu fullname: Gwetu, Mandlenkosi email: mgwetu@sun.ac.za organization: Department of Industrial Engineering, Stellenbosch University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39497049$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kstu1DAUhiNURC_wAiyQJTZsUuzYmcQrVFVcKlViA2vLl-OMR0k82MmM5hH61pzplLaDEMoizu_v_D7H-c-LkzGOUBRvGb1krF18zKxqW1rSSpSUCcHL7YvijImGlRUX1cmz9WlxnvOKUta0XLwqTrkUsqFCnhV313FYzxMksgk5xJHo9boPVk-4zsTHRKYlEAcT2L1EUNCj7nc5ZBI9mWYDyc593H_POYwdcaELU8jgyHIe9Ej6GUUU8gwkDLqDTEqiSd7lCQY8x5IEmwDb18VLr_sMbx7eF8XPL59_XH8rb79_vbm-ui1tXcuprL03IIUTrJJeGAO6dY1pwAgvDW8rSi2ljjXMWV-7hXQctKgMNVrY2tXAL4qbg6-LeqXWCXtKOxV1UPdCTJ3SCdvqQQnWSM0qAwKvV5taG7-ojDW-YrCQwNHr08FrPZsBnIVxSro_Mj3eGcNSdXGjGKv5opUSHT48OKT4a4Y8qSFkC32vR4hzVpxVOGkt6xrR93-hqzgn_Bn31AJ7bLh8ojqNE4TRRzzY7k3VVctE23IMBFKX_6DwcTAEizHzAfWjgnfPJ30c8U-SEKgOgE0x5wT-EWFU7eOqDnFVGFd1H1e1xSJ-KMoIjx2kp5H-U_UbZwPwGg |
| Cites_doi | 10.1109/CVPR42600.2020.01044 10.1016/j.media.2020.101693 10.1016/0734-189X(85)90052-0 10.3389/fimmu.2018.02108 10.1164/rccm.202101-0032OC 10.1016/j.celrep.2019.01.010 10.1093/ajcp/aqaa215 10.17700/jai.2015.6.1.152 10.1007/978-3-319-65981-7_12 10.1016/j.tube.2011.03.007 10.3390/diagnostics12030709 10.1016/j.ajpath.2019.05.004 10.1148/radiology.210.2.r99ja34307 10.1016/j.proeng.2013.09.074 10.21037/jtd.2018.01.91 10.1038/s41597-022-01353-y 10.1007/978-3-319-67340-0_1 10.1016/j.tube.2015.11.010 10.7326/0003-4819-151-4-200908180-00135 10.1002/cem.873 10.1007/978-1-4899-7641-3_9 10.1016/j.compmedimag.2020.101752 10.1152/japplphysiol.00803 10.1038/nbt1386 10.1038/nbt1206-1565 10.1007/s10916-019-1203-y 10.1007/s10064-018-1298-2 10.1200/CCI.19 10.1016/j.media.2016.06.037 10.1016/j.rmed.2006.08.006 10.1016/S2213-2600(20)30324-6 10.3390/diagnostics12061484 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2024 2024. The Author(s). COPYRIGHT 2024 BioMed Central Ltd. 2024. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. The Author(s) 2024 2024 |
| Copyright_xml | – notice: The Author(s) 2024 – notice: 2024. The Author(s). – notice: COPYRIGHT 2024 BioMed Central Ltd. – notice: 2024. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: The Author(s) 2024 2024 |
| DBID | C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7QO 7RV 7X7 7XB 88E 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABUWG AFKRA ARAPS AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU COVID DWQXO FR3 FYUFA GHDGH GNUQQ HCIFZ K9. KB0 LK8 M0S M1P M7P NAPCQ P5Z P62 P64 PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI 7X8 5PM DOA |
| DOI | 10.1186/s12880-024-01443-w |
| DatabaseName | SpringerOpen Free (Free internet resource, activated by CARLI) CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Biotechnology Research Abstracts Nursing & Allied Health Database Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection ProQuest Hospital Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials - QC Biological Science Collection ProQuest Central Technology Collection Natural Science Collection ProQuest One Coronavirus Research Database ProQuest Central Korea Engineering Research Database Proquest Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) Nursing & Allied Health Database (Alumni Edition) Biological Sciences Health & Medical Collection (Alumni Edition) Medical Database Biological Science Database Nursing & Allied Health Premium Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts Proquest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest Central Student Technology Collection Technology Research Database ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Central ProQuest One Applied & Life Sciences ProQuest Health & Medical Research Collection Health Research Premium Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) Advanced Technologies & Aerospace Collection ProQuest Biological Science Collection ProQuest One Academic Eastern Edition Coronavirus Research Database ProQuest Nursing & Allied Health Source ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest SciTech Collection ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts Advanced Technologies & Aerospace Database Nursing & Allied Health Premium ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest Nursing & Allied Health Source (Alumni) Engineering Research Database ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | Publicly Available Content Database MEDLINE - Academic MEDLINE |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 1471-2342 |
| EndPage | 15 |
| ExternalDocumentID | oai_doaj_org_article_4179a12be4144ab5abf62bcbf21e69e3 PMC11536899 A814883234 39497049 10_1186_s12880_024_01443_w |
| Genre | Systematic Review Journal Article |
| GrantInformation_xml | – fundername: Wellcome Strategic Core award grantid: 201433/Z/16/A – fundername: Wellcome Leap Delta Tissue Program and NIH grantid: R33/61AI138280 – fundername: NIAID NIH HHS grantid: R61 AI138280 – fundername: Wellcome Trust |
| GroupedDBID | --- 0R~ 23N 2WC 53G 5VS 6J9 7RV 7X7 88E 8FE 8FG 8FH 8FI 8FJ AAFWJ AAJSJ AASML ABUWG ACGFO ACGFS ACIHN ACIWK ACPRK ADBBV ADRAZ ADUKV AEAQA AENEX AFKRA AFPKN AFRAH AHBYD AHMBA AHYZX ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS ARAPS BAPOH BAWUL BBNVY BCNDV BENPR BFQNJ BGLVJ BHPHI BMC BPHCQ BVXVI C6C CCPQU CS3 DIK DU5 E3Z EBD EBLON EBS EMB EMOBN F5P FYUFA GROUPED_DOAJ GX1 HCIFZ HMCUK HYE IAO IHR INH INR ITC KQ8 LK8 M1P M48 M7P M~E NAPCQ O5R O5S OK1 OVT P2P P62 PGMZT PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PUEGO RBZ RNS ROL RPM RSV SMD SOJ SV3 TR2 UKHRP W2D WOQ WOW XSB AAYXX AFFHD CITATION ALIPV CGR CUY CVF ECM EIF NPM 3V. 7QO 7XB 8FD 8FK AZQEC COVID DWQXO FR3 GNUQQ K9. P64 PKEHL PQEST PQUKI 7X8 5PM |
| ID | FETCH-LOGICAL-c559t-5ffbe94d4129f4bbea8d7b7eb4f9b38200c00d171dcf5d69d3ea42b0ba4c5d5e3 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 1 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001347336700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1471-2342 |
| IngestDate | Fri Oct 03 12:45:28 EDT 2025 Tue Nov 04 02:05:48 EST 2025 Thu Oct 02 06:22:37 EDT 2025 Tue Oct 07 05:35:07 EDT 2025 Tue Nov 11 10:54:14 EST 2025 Tue Nov 04 18:13:51 EST 2025 Thu Apr 03 06:59:04 EDT 2025 Sat Nov 29 06:11:10 EST 2025 Sat Sep 06 07:26:54 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Deep learning Human lung tissue Image analysis Tuberculosis |
| Language | English |
| License | 2024. The Author(s). Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c559t-5ffbe94d4129f4bbea8d7b7eb4f9b38200c00d171dcf5d69d3ea42b0ba4c5d5e3 |
| Notes | ObjectType-Article-2 SourceType-Scholarly Journals-1 content type line 14 ObjectType-Feature-3 ObjectType-Evidence Based Healthcare-1 ObjectType-Article-1 ObjectType-Feature-2 content type line 23 ObjectType-Undefined-3 |
| OpenAccessLink | https://doaj.org/article/4179a12be4144ab5abf62bcbf21e69e3 |
| PMID | 39497049 |
| PQID | 3126414739 |
| PQPubID | 44833 |
| PageCount | 15 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_4179a12be4144ab5abf62bcbf21e69e3 pubmedcentral_primary_oai_pubmedcentral_nih_gov_11536899 proquest_miscellaneous_3124125955 proquest_journals_3126414739 gale_infotracmisc_A814883234 gale_infotracacademiconefile_A814883234 pubmed_primary_39497049 crossref_primary_10_1186_s12880_024_01443_w springer_journals_10_1186_s12880_024_01443_w |
| PublicationCentury | 2000 |
| PublicationDate | 2024-11-05 |
| PublicationDateYYYYMMDD | 2024-11-05 |
| PublicationDate_xml | – month: 11 year: 2024 text: 2024-11-05 day: 05 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | BMC medical imaging |
| PublicationTitleAbbrev | BMC Med Imaging |
| PublicationTitleAlternate | BMC Med Imaging |
| PublicationYear | 2024 |
| Publisher | BioMed Central BioMed Central Ltd Springer Nature B.V BMC |
| Publisher_xml | – name: BioMed Central – name: BioMed Central Ltd – name: Springer Nature B.V – name: BMC |
| References | M Azarafza (1443_CR1) 2019; 78 RL Hunter (1443_CR10) 2011; 91 X Dragos (1443_CR8) 2020; 128 1443_CR26 I Barberis (1443_CR3) 2017; 58 1443_CR2 G Wells (1443_CR30) 2021; 204 1443_CR25 Y Xiong (1443_CR34) 2018; 10 1443_CR23 AJ Myles (1443_CR20) 2004; 18 1443_CR9 1443_CR4 1443_CR6 MI Razzak (1443_CR24) 2018; 26 D Moher (1443_CR19) 2009; 151 Y Chen (1443_CR5) 2020; 4 1443_CR17 1443_CR39 1443_CR16 1443_CR38 1443_CR15 1443_CR37 A Madabhushi (1443_CR18) 2016; 33 L Pantanowitz (1443_CR22) 2021; 156 AE Vincent (1443_CR29) 2019; 26 1443_CR32 TM Daniel (1443_CR7) 2006; 100 1443_CR31 S Umaa Mageswari (1443_CR27) 2013; 64 SE Verleden (1443_CR28) 2021; 9 1443_CR36 1443_CR35 1443_CR12 1443_CR11 1443_CR33 (1443_CR13) 2011 OL Katsamenis (1443_CR14) 2019; 189 WS Noble (1443_CR21) 2006; 24 |
| References_xml | – ident: 1443_CR23 doi: 10.1109/CVPR42600.2020.01044 – ident: 1443_CR26 doi: 10.1016/j.media.2020.101693 – ident: 1443_CR31 – ident: 1443_CR32 doi: 10.1016/0734-189X(85)90052-0 – ident: 1443_CR12 doi: 10.3389/fimmu.2018.02108 – volume: 204 start-page: 583 issue: 5 year: 2021 ident: 1443_CR30 publication-title: Am J Respir Crit Care Med. doi: 10.1164/rccm.202101-0032OC – volume: 26 start-page: 996 issue: 4 year: 2019 ident: 1443_CR29 publication-title: Cell Rep. doi: 10.1016/j.celrep.2019.01.010 – volume: 156 start-page: 117 issue: 1 year: 2021 ident: 1443_CR22 publication-title: Am J Clin Pathol. doi: 10.1093/ajcp/aqaa215 – ident: 1443_CR2 doi: 10.17700/jai.2015.6.1.152 – volume: 26 start-page: 323 year: 2018 ident: 1443_CR24 publication-title: Lecture Notes Comput Vis Biomech. doi: 10.1007/978-3-319-65981-7_12 – volume: 91 start-page: 497 issue: 6 year: 2011 ident: 1443_CR10 publication-title: Tuberculosis. doi: 10.1016/j.tube.2011.03.007 – ident: 1443_CR37 doi: 10.3390/diagnostics12030709 – volume: 189 start-page: 1608 issue: 8 year: 2019 ident: 1443_CR14 publication-title: Am J Pathol. doi: 10.1016/j.ajpath.2019.05.004 – ident: 1443_CR17 doi: 10.1148/radiology.210.2.r99ja34307 – volume: 64 start-page: 36 year: 2013 ident: 1443_CR27 publication-title: Procedia Eng. doi: 10.1016/j.proeng.2013.09.074 – volume: 10 start-page: 1936 issue: 3 year: 2018 ident: 1443_CR34 publication-title: J Thorac Dis. doi: 10.21037/jtd.2018.01.91 – ident: 1443_CR33 doi: 10.1038/s41597-022-01353-y – ident: 1443_CR38 doi: 10.1007/978-3-319-67340-0_1 – ident: 1443_CR11 doi: 10.1016/j.tube.2015.11.010 – volume: 151 start-page: 264 issue: 4 year: 2009 ident: 1443_CR19 publication-title: Ann Intern Med. doi: 10.7326/0003-4819-151-4-200908180-00135 – ident: 1443_CR9 – volume: 18 start-page: 275 issue: 6 year: 2004 ident: 1443_CR20 publication-title: J Chemometr. doi: 10.1002/cem.873 – volume-title: A Color Atlas of Comparative Pathology of Pulmonary Tuberculosis year: 2011 ident: 1443_CR13 – ident: 1443_CR25 doi: 10.1007/978-1-4899-7641-3_9 – ident: 1443_CR35 doi: 10.1016/j.compmedimag.2020.101752 – volume: 128 start-page: 1604 year: 2020 ident: 1443_CR8 publication-title: J Appl Physiol. doi: 10.1152/japplphysiol.00803 – ident: 1443_CR16 doi: 10.1038/nbt1386 – volume: 24 start-page: 1565 issue: 12 year: 2006 ident: 1443_CR21 publication-title: Nat Biotechnol. doi: 10.1038/nbt1206-1565 – ident: 1443_CR36 doi: 10.1007/s10916-019-1203-y – volume: 58 start-page: E9 issue: 1 year: 2017 ident: 1443_CR3 publication-title: J Prev Med Hyg. – ident: 1443_CR15 – ident: 1443_CR4 – volume: 78 start-page: 3371 issue: 5 year: 2019 ident: 1443_CR1 publication-title: Bull Eng Geol Environ. doi: 10.1007/s10064-018-1298-2 – ident: 1443_CR6 – volume: 4 start-page: 221 year: 2020 ident: 1443_CR5 publication-title: JCO Clin Cancer Inform. doi: 10.1200/CCI.19 – volume: 33 start-page: 170 year: 2016 ident: 1443_CR18 publication-title: Med Image Anal. doi: 10.1016/j.media.2016.06.037 – volume: 100 start-page: 1862 issue: 11 year: 2006 ident: 1443_CR7 publication-title: Respir Med. doi: 10.1016/j.rmed.2006.08.006 – volume: 9 start-page: 167 issue: 2 year: 2021 ident: 1443_CR28 publication-title: Lancet Respir Med. doi: 10.1016/S2213-2600(20)30324-6 – ident: 1443_CR39 doi: 10.3390/diagnostics12061484 |
| SSID | ssj0017834 |
| Score | 2.3442502 |
| SecondaryResourceType | review_article |
| Snippet | Objective
To conduct a systematic review of the computer vision applications that detect, diagnose, or analyse
tuberculosis
(TB) pathology or
bacilli
using... To conduct a systematic review of the computer vision applications that detect, diagnose, or analyse tuberculosis (TB) pathology or bacilli using digitised... Objective To conduct a systematic review of the computer vision applications that detect, diagnose, or analyse tuberculosis (TB) pathology or bacilli using... ObjectiveTo conduct a systematic review of the computer vision applications that detect, diagnose, or analyse tuberculosis (TB) pathology or bacilli using... Abstract Objective To conduct a systematic review of the computer vision applications that detect, diagnose, or analyse tuberculosis (TB) pathology or bacilli... |
| SourceID | doaj pubmedcentral proquest gale pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Publisher |
| StartPage | 298 |
| SubjectTerms | Algorithms Animal models Bacilli CAD Computed tomography Computer aided design Computer graphics Computer vision CT imaging Deep Learning Digitization Graphics software High resolution Histology Human lung tissue Human tissues Humans Image acquisition Image analysis Image processing Image Processing, Computer-Assisted - methods Image resolution Imaging Imaging, Three-Dimensional - methods Immunohistochemistry Information processing Lung - diagnostic imaging Lung - microbiology Lung - pathology Lung diseases Lungs Machine learning Machine vision Medical imaging Medical imaging equipment Medical research Medicine Medicine & Public Health Mycobacterium tuberculosis - isolation & purification Neural networks Object recognition Pathology Pattern recognition Pattern Recognition, Automated - methods Radiology Reviews Scanning devices Soft tissues State-of-the-art reviews Systematic review Tissues Topology Tuberculosis Tuberculosis, Pulmonary - diagnostic imaging Two dimensional analysis |
| SummonAdditionalLinks | – databaseName: Biological Science Database dbid: M7P link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Jb9UwEB5BQYgL-xIoyEhIHCBqEttJfEIFUXGAqgdAvVne8vqkkpS30N_Av2bGSd5riuDCMbEj2ZnPs3g2gJeZE6UVFKdjgk1FWdjU1J5MFTTjFHemiE37vn2qDg_r42N1NFy4LYewypEnRkbtO0d35Hs8R9Gdi4qrt2c_UuoaRd7VoYXGVbhGVRJ4DN072ngRqInEmChTl3tL5MV1lqJUSsmO4On5RBjFmv1_cuYLouly2OQl32kUSQe3_3czd-DWoIyy_R49d-FKaO_Bjc-Du_0-_Bp7PrA-BZ1ddHczVHcZqo_Mh1WM52oZvjBDkRPWNWy1tmHh1qcdPVOA_Yz5-YyKKAXPYnNAdoq8hq0i8dn8O_K2JUuZYdv60qzPrXkAXw8-fHn_MR16N6QObZRVKpvGBiW8QH2iEdYGREBlq2BFoyxHtSNzWebzKveukb5UngcjCptZI5z0MvCHsNN2bXgMzAgZiqYpOSUd1MiDcluqqnB54XxhqiqB1yMR9VlfokNH06YudU9yjSTXkeT6PIF3ROfNTCqvHV90i5keTqumtmwmL2xAIgljpbENotjZpshDqQJP4BWhRBMTQCg4M-Qy4IKpnJber3GlyCu5SGB3MhMPr5sOjwDRA_NY6i06EnixGaYvKSCuDd06zsFfK5WUCTzqYbnZEldCVWj5JVBPADvZ83SknZ_E0uJoH_ASTfAE3ozY3q7r7z_1yb-38RRuFnTq6DZe7sLOarEOz-C6-4n4WjyPZ_Y3899M0A priority: 102 providerName: ProQuest – databaseName: SpringerLINK Contemporary 1997-Present dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwEB5BQYgL70egICMhcYCIxI8kPhZExQEqxKPqzfJzWakkaDdLfwP_mrGTbJsCBzhubEv2-JvxzM4L4GlheWV4jNPR3uS8oibXjYumCppxkllNU9O-w3f1wUFzdCQ_jElh6ynafXJJJkmd2LqpXq5RkjZFjm9KHq0Alp9chEv43DWRHT9-Otz6DmLriCk95o_rZk9QqtT_uzw-8yCdD5Y85zFND9H-9f87wg24NiqeZG9Ayk244NtbcOX96Fq_DT-n_g5kSDcnZ13bBFVbgqoicb5PsVstwQ96LGhCukD6jfEruznu4u8YTL8gbrmIBZO8I6kRIDlGuUL6dNFk-Q3l2JrkRJPTWtJkyKO5A1_233x-_TYf-zTkFu2RPhchGC-546g7BG6Mx9uuTe0ND9IwVDEKWxSurEtng3CVdMxrTk1hNLfCCc_uwk7btf4-EM2FpyFULCYYNChvSlPJmtqSWkd1XWfwfLo69X0ox6GSGdNUaqCuQuqqRF11ksGreLvbmbGUdvrQrRZq5EwVW7DpkhrPcZE2QpuAiLUm0NJX0rMMnkVsqMjwCACrx7wF3HAsnaX2GtwpykXGM9idzURGtfPhCV1qFBRrxUrUSEteM5nBk-1wXBmD31rfbdIcJK2QQmRwbwDj9khMclmjlZdBM4Pp7MzzkXb5NZURR1uAVWhuZ_BiQuvpvv5O1Af_Nv0hXKUR8PGfeLELO_1q4x_BZfsD8bZ6nDj3FyOxQ6k priority: 102 providerName: Springer Nature |
| Title | Computer vision applications for the detection or analysis of tuberculosis using digitised human lung tissue images - a systematic review |
| URI | https://link.springer.com/article/10.1186/s12880-024-01443-w https://www.ncbi.nlm.nih.gov/pubmed/39497049 https://www.proquest.com/docview/3126414739 https://www.proquest.com/docview/3124125955 https://pubmed.ncbi.nlm.nih.gov/PMC11536899 https://doaj.org/article/4179a12be4144ab5abf62bcbf21e69e3 |
| Volume | 24 |
| WOSCitedRecordID | wos001347336700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVADU databaseName: BioMed Central customDbUrl: eissn: 1471-2342 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017834 issn: 1471-2342 databaseCode: RBZ dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.biomedcentral.com/search/ providerName: BioMedCentral – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1471-2342 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017834 issn: 1471-2342 databaseCode: DOA dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1471-2342 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017834 issn: 1471-2342 databaseCode: M~E dateStart: 20010101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1471-2342 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017834 issn: 1471-2342 databaseCode: P5Z dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 1471-2342 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017834 issn: 1471-2342 databaseCode: M7P dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1471-2342 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017834 issn: 1471-2342 databaseCode: 7X7 dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: Nursing & Allied Health Database customDbUrl: eissn: 1471-2342 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017834 issn: 1471-2342 databaseCode: 7RV dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/nahs providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1471-2342 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017834 issn: 1471-2342 databaseCode: BENPR dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1471-2342 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017834 issn: 1471-2342 databaseCode: PIMPY dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLink Journals customDbUrl: eissn: 1471-2342 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017834 issn: 1471-2342 databaseCode: RSV dateStart: 20011201 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwEB5BQYgL4k2grIyExAGiJrGdxMcWtQKJrqIFVgsXy6-UlUoW7YP-Bv41YyfZbooQFy6WEjuR43l4Jp75BuBlYliumY_TUU7HLM90rErrXRV04wQ1KgtF-6YfivG4nM1EtVPqy8eEtfDA7cId-ApZKs20Y2j6K82VrvGFRtdZ6nLhAs4nWj29M9WdH_jyEX2KTJkfrFALl0mM-1HsPQgaXwy2oYDW_6dO3tmUrgZMXjk1DZvRyV2401mR5LCd_T245pr7cOu0Oyd_AL_6Yg2kzR0nu-fUBO1UgnYfsW4dArEagjdUh05CFjVZb7Rbms35wl_7yPgzYudnHv3IWRKq-pFzVBJkHahG5t9RKa1ITBS5BIYmbVLMQ_h8cvzp7bu4K7oQG3Qu1jGva-0EswwNgZpp7ZB0hS6cZrXQFO2FxCSJTYvUmprbXFjqFMt0ohUz3HJHH8Fes2jcEyCKcZfVdU59tkCJyiPVuSgyk2bGZqooInjd00D-aLE1ZPBJyly2FJNIMRkoJi8iOPJk2o70uNjhBnKL7LhF_otbInjliSy99CIljeqSEHDCHgdLHpY4U1RylEWwPxiJUmeG3T2byE7qV5KmaF6mrKAighfbbv-kj2Rr3GITxuDScsF5BI9brtp-EhVMFOiyRVAO-G3wzcOeZv4tYIKjYU9z9J0jeNOz5uW8_r6oT__Hoj6D25kXLf-zne_D3nq5cc_hpvmJXLgcwfViMvXtrAhtOYIbR8fjajIKIjvy0bYVthX_ij3V-9PqC15NPk5_A6oeSKE |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Jb9QwFLZKQcCFfQkUMBKIQ4maOM7iA0JlqVp1WiFUqt6MtwwjlaTMwoifwJ_hN_Kek8w0RXDrgePEzsj2fO97fvM2Qp5FhmeaY5yOcjrkGdOhKiyaKmDGicQo5pv2HQ7y_f3i6Eh8WCG_ulwYDKvsONETta0N_ke-kcSgumOeJ-L1ybcQu0ahd7VrodHAYtf9mIPJNnm18w5-3-eMbb0_eLsdtl0FQgO352mYlqV2glsOmq7kWjtYW65zp3kpdAIKMTJRZOM8tqZMbSZs4hRnOtKKm9SmLoHvvUAuAo_HGEKWfzxceC2waUWXmFNkGxPg_iIKQQuGaLck4byn_HyPgD81wSlVeDZM84yv1qvArev_2-HdINfayzbdbKTjJllx1S1yea8NJ7hNfnY9LWiTYk9Pu_MpXOcpXI-pdVMfr1ZReKDaIi60Lul0pt3YzI5r_IwJBENqR0MsEuUs9c0P6TFwKZ16cNPRV-DuCQ2posv62bTJHbpDPp3LQdwlq1VdufuEKp46VpZZgkkVBXBsrDORMxMzY5nK84Csd6CRJ00JEulNtyKTDcQkQEx6iMl5QN4grhYzsXy4f1CPh7JlI4lt51TMtANQcKVTpUuQUqNLFrtMuCQgLxCVEkkOoGdUm6sBC8ZyYXKzgJWCLkh4QNZ6M4GcTH-4A6RsyXEil2gMyNPFML6JAX-Vq2d-DhxtKtI0IPcaMVhsKRFc5GDZBqToCUhvz_2RavTFl04H-yfJCgGvvuxkabmuvx_qg39v4wm5sn2wN5CDnf3dh-QqQ4lHz0O6Rlan45l7RC6Z74C18WPPF5R8Pm8Z-w0Ov61s |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Zb9QwEB5BQRUv3NBAASMh8QBRk9g5_FiOFYiyqgRUfbN8Liu1SbWbpb-Bf83YSbabAg-Ix_iQ7PHn8UzmAniRaFYo5v10pFUxKzIVy8p4VQXVOE61zELRvqODcjqtjo_54UYUf_B2H0ySXUyDz9JUt3tnxnVXvCr2lshVqyTG9yX2GgGNz6_CNeaLBnl9_cvR2o7gy0gMoTJ_nDd6jkLW_t9588bjdNlx8pL1NDxKk1v_v53bcLMXSMl-h6A7cMXWd2H7c29yvwc_h7oPpAtDJ5smb4IiL0ERkhjbBp-ummCD7BOdkMaRdqXsQq9OGv_tnexnxMxnPpGSNSQUCCQnyG9IGwBA5qfI35YkJpJc5JgmXXzNffg2ef_17Ye4r98Qa9RT2jh3TlnODEOZwjGlLKKgVKVVzHFFUfRIdJKYtEyNdrkpuKFWskwlSjKdm9zSB7BVN7XdASJZbjPnCuoDDyrkQ6kqeJnpNNMmk2UZwavhGMVZl6ZDBPWmKkRHXYHUFYG64jyCN_6k1yN9iu3Q0Cxmor-xwpdmk2mmLMNJUuVSOUSyVi5LbcEtjeClx4nwjADBoGUfz4AL9im1xH6FK0V-SVkEu6OReIH1uHtAmugZyFLQFCXVlJWUR_B83e1neqe42jarMAZJm_M8j-BhB8z1lihnvETtL4JqBNnRnsc99fx7SC-OOgItUA2P4PWA3It1_Z2oj_5t-DPYPnw3EQcfp58ew43MY9__rM93YatdrOwTuK5_IPQWT8OF_gWteE9x |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Computer+vision+applications+for+the+detection+or+analysis+of+tuberculosis+using+digitised+human+lung+tissue+images+-+a+systematic+review&rft.jtitle=BMC+medical+imaging&rft.au=Lumamba%2C+Kapongo+D&rft.au=Wells%2C+Gordon&rft.au=Naicker%2C+Delon&rft.au=Naidoo%2C+Threnesan&rft.date=2024-11-05&rft.issn=1471-2342&rft.eissn=1471-2342&rft.volume=24&rft.issue=1&rft.spage=298&rft_id=info:doi/10.1186%2Fs12880-024-01443-w&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1471-2342&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1471-2342&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1471-2342&client=summon |