Rapid diagnosis of latent and active pulmonary tuberculosis by autofluorescence spectroscopy of blood plasma combined with artificial neural network algorithm
•A rapid, accurate, label-free and non-invasive diagnosis method of pulmonary tuberculosis (TB) has been developed by using plasma autofluorescence spectroscopy and Machine Learning algorithm.•It is the first time that the autofluorescence spectroscopy combined with optimized three-classification Ar...
Saved in:
| Published in: | Photodiagnosis and photodynamic therapy Vol. 50; p. 104426 |
|---|---|
| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Netherlands
Elsevier B.V
01.12.2024
Elsevier |
| Subjects: | |
| ISSN: | 1572-1000, 1873-1597, 1873-1597 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | •A rapid, accurate, label-free and non-invasive diagnosis method of pulmonary tuberculosis (TB) has been developed by using plasma autofluorescence spectroscopy and Machine Learning algorithm.•It is the first time that the autofluorescence spectroscopy combined with optimized three-classification Artificial Neural Network (ANN) model for distinguishing the active TB patients, latent TB infected individuals from healthy people.•Compared with traditional Principal component analysis (PCA) and Linear discriminant analysis (LDA) method, the three-layer ANN model achieves much better classification accuracy of 96.3 %, it can be developed as a promising diagnostic tool for the early screening of pulmonary TB disease.
The existing clinical diagnostic methods of pulmonary tuberculosis (TB) usually have some of the following limitations, such as time-consuming, invasive, radioactive, insufficiently sensitive and accurate. This study demonstrates the possibility of using blood plasma autofluorescence spectroscopy and Artificial Neural Network (ANN) algorithm for the rapid and accurate diagnosis of latent and active pulmonary TB from healthy subjects. The fluorescence spectra of blood plasma from 18 healthy volunteers, 12 individuals with latent TB infections and 80 active TB patients are measured and analyzed. By optimizing the ANN structure and activation functions, the ANN three-classification model achieves average classification accuracy of 96.3 %, and the accuracy of healthy persons, latent TB infections and active TB patients are 100 %, 83.3 % and 97.5 %, respectively, which is much better than the results of traditional Principal component analysis (PCA) and Linear discriminant analysis (LDA) method. To the best of our knowledge, this is the first research work of differentiating latent, active pulmonary TB cases from healthy samples with autofluorescence spectroscopy. As a rapid, accurate, safe, label-free, non-invasive and cost-effective technique, it can be developed as a promising diagnostic tool for the screening of pulmonary TB disease in the early stage.
[Display omitted] |
|---|---|
| AbstractList | •A rapid, accurate, label-free and non-invasive diagnosis method of pulmonary tuberculosis (TB) has been developed by using plasma autofluorescence spectroscopy and Machine Learning algorithm.•It is the first time that the autofluorescence spectroscopy combined with optimized three-classification Artificial Neural Network (ANN) model for distinguishing the active TB patients, latent TB infected individuals from healthy people.•Compared with traditional Principal component analysis (PCA) and Linear discriminant analysis (LDA) method, the three-layer ANN model achieves much better classification accuracy of 96.3 %, it can be developed as a promising diagnostic tool for the early screening of pulmonary TB disease.
The existing clinical diagnostic methods of pulmonary tuberculosis (TB) usually have some of the following limitations, such as time-consuming, invasive, radioactive, insufficiently sensitive and accurate. This study demonstrates the possibility of using blood plasma autofluorescence spectroscopy and Artificial Neural Network (ANN) algorithm for the rapid and accurate diagnosis of latent and active pulmonary TB from healthy subjects. The fluorescence spectra of blood plasma from 18 healthy volunteers, 12 individuals with latent TB infections and 80 active TB patients are measured and analyzed. By optimizing the ANN structure and activation functions, the ANN three-classification model achieves average classification accuracy of 96.3 %, and the accuracy of healthy persons, latent TB infections and active TB patients are 100 %, 83.3 % and 97.5 %, respectively, which is much better than the results of traditional Principal component analysis (PCA) and Linear discriminant analysis (LDA) method. To the best of our knowledge, this is the first research work of differentiating latent, active pulmonary TB cases from healthy samples with autofluorescence spectroscopy. As a rapid, accurate, safe, label-free, non-invasive and cost-effective technique, it can be developed as a promising diagnostic tool for the screening of pulmonary TB disease in the early stage.
[Display omitted] The existing clinical diagnostic methods of pulmonary tuberculosis (TB) usually have some of the following limitations, such as time-consuming, invasive, radioactive, insufficiently sensitive and accurate. This study demonstrates the possibility of using blood plasma autofluorescence spectroscopy and Artificial Neural Network (ANN) algorithm for the rapid and accurate diagnosis of latent and active pulmonary TB from healthy subjects. The fluorescence spectra of blood plasma from 18 healthy volunteers, 12 individuals with latent TB infections and 80 active TB patients are measured and analyzed. By optimizing the ANN structure and activation functions, the ANN three-classification model achieves average classification accuracy of 96.3 %, and the accuracy of healthy persons, latent TB infections and active TB patients are 100 %, 83.3 % and 97.5 %, respectively, which is much better than the results of traditional Principal component analysis (PCA) and Linear discriminant analysis (LDA) method. To the best of our knowledge, this is the first research work of differentiating latent, active pulmonary TB cases from healthy samples with autofluorescence spectroscopy. As a rapid, accurate, safe, label-free, non-invasive and cost-effective technique, it can be developed as a promising diagnostic tool for the screening of pulmonary TB disease in the early stage.The existing clinical diagnostic methods of pulmonary tuberculosis (TB) usually have some of the following limitations, such as time-consuming, invasive, radioactive, insufficiently sensitive and accurate. This study demonstrates the possibility of using blood plasma autofluorescence spectroscopy and Artificial Neural Network (ANN) algorithm for the rapid and accurate diagnosis of latent and active pulmonary TB from healthy subjects. The fluorescence spectra of blood plasma from 18 healthy volunteers, 12 individuals with latent TB infections and 80 active TB patients are measured and analyzed. By optimizing the ANN structure and activation functions, the ANN three-classification model achieves average classification accuracy of 96.3 %, and the accuracy of healthy persons, latent TB infections and active TB patients are 100 %, 83.3 % and 97.5 %, respectively, which is much better than the results of traditional Principal component analysis (PCA) and Linear discriminant analysis (LDA) method. To the best of our knowledge, this is the first research work of differentiating latent, active pulmonary TB cases from healthy samples with autofluorescence spectroscopy. As a rapid, accurate, safe, label-free, non-invasive and cost-effective technique, it can be developed as a promising diagnostic tool for the screening of pulmonary TB disease in the early stage. The existing clinical diagnostic methods of pulmonary tuberculosis (TB) usually have some of the following limitations, such as time-consuming, invasive, radioactive, insufficiently sensitive and accurate. This study demonstrates the possibility of using blood plasma autofluorescence spectroscopy and Artificial Neural Network (ANN) algorithm for the rapid and accurate diagnosis of latent and active pulmonary TB from healthy subjects. The fluorescence spectra of blood plasma from 18 healthy volunteers, 12 individuals with latent TB infections and 80 active TB patients are measured and analyzed. By optimizing the ANN structure and activation functions, the ANN three-classification model achieves average classification accuracy of 96.3 %, and the accuracy of healthy persons, latent TB infections and active TB patients are 100 %, 83.3 % and 97.5 %, respectively, which is much better than the results of traditional Principal component analysis (PCA) and Linear discriminant analysis (LDA) method. To the best of our knowledge, this is the first research work of differentiating latent, active pulmonary TB cases from healthy samples with autofluorescence spectroscopy. As a rapid, accurate, safe, label-free, non-invasive and cost-effective technique, it can be developed as a promising diagnostic tool for the screening of pulmonary TB disease in the early stage. |
| ArticleNumber | 104426 |
| Author | Li, Si Chen, Xuerong Yue, Fengjiao Wu, Lijuan Zhu, Jianhua |
| Author_xml | – sequence: 1 givenname: Fengjiao surname: Yue fullname: Yue, Fengjiao organization: College of Physics, Sichuan University, Chengdu, China – sequence: 2 givenname: Si surname: Li fullname: Li, Si organization: Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China – sequence: 3 givenname: Lijuan surname: Wu fullname: Wu, Lijuan organization: Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China – sequence: 4 givenname: Xuerong surname: Chen fullname: Chen, Xuerong email: 384481688@qq.com organization: Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China – sequence: 5 givenname: Jianhua orcidid: 0000-0003-4169-6019 surname: Zhu fullname: Zhu, Jianhua email: zhujh@scu.edu.cn organization: College of Physics, Sichuan University, Chengdu, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39615559$$D View this record in MEDLINE/PubMed |
| BookMark | eNqFkstuFDEQRVsoiDzgC5CQl2xmsLvdLyEWKOIRKRISgrXlLpcHT9x2Y7sTzc_wrbinExZZkJWt0j3X5Vt1Xpw477AoXjO6ZZQ17_bbSU0qbUta8lzhvGyeFWesa6sNq_v2JN_rttwwSulpcR7jntKK95S_KE6rvmF1XfdnxZ_vcjKKKCN3zkcTidfEyoQuEekUkZDMLZJptqN3MhxImgcMMNujdjgQOSev7ewDRkAHSOKEkIKP4KfDYjZY7xWZrIyjJODHwThU5M6kX0SGZLQBIy1xOIfjke58uCHS7nzIkvFl8VxLG_HV_XlR_Pz86cfl1831ty9Xlx-vN1CXZdpA32ALupNQI1Jd87KtGwa0BGg4lLThHXKoKNdDg4PuUesm1yrJ2wZLzquL4mr1VV7uxRTMmD8rvDTiWPBhJ5ZuwaLgbMGV7lWDnCIbWKU7xbu-7HTHeJ-93q5eU_C_Z4xJjCaHY6106OcoKsZp17V1TbP0zb10HkZU_x5-mE8W9KsAcqQxoBZgkkzGuxSksYJRseyCyD0vuyCWXRDrLmS2esQ-2P-f-rBSmOO-NRhEBLNMVpmQJ5vzME_w7x_xYI0zIO0NHp6k_wJx9Oaq |
| CitedBy_id | crossref_primary_10_1002_jbio_202500100 crossref_primary_10_1111_nhs_70077 |
| Cites_doi | 10.1016/j.pdpdt.2022.103102 10.1128/jcm.31.9.2410-2416.1993 10.1586/14737159.8.2.149 10.1562/0031-8655(2003)078<0197:NFSOBP>2.0.CO;2 10.1371/journal.pone.0221421 10.1002/jbio.202300021 10.1002/(SICI)1096-9101(1997)21:5<417::AID-LSM2>3.0.CO;2-T 10.1002/bjs.6239 10.1002/lsm.10153 10.1038/s41598-022-13750-9 10.1016/j.ijid.2022.02.047 10.1007/s10895-010-0751-9 10.1016/j.ijleo.2020.165446 10.1164/ajrccm.162.4.9912115 10.1089/pho.2007.2162 10.1109/TBME.2003.818488 10.1364/BOE.448121 10.1364/BOE.10.004999 10.1039/b304992b 10.4103/0973-6247.126679 10.1002/pros.23198 10.1007/s11814-015-0255-z 10.1021/cr900343z 10.6026/97320630016539 10.1016/j.tube.2015.02.038 10.1016/j.tube.2017.09.006 10.3390/app7010032 10.1002/nbm.3916 10.1364/OE.23.018361 10.3389/frans.2022.906532 10.1021/acs.jpclett.2c02193 10.1016/j.jpba.2020.113757 10.1177/0003702820904444 10.1186/1471-2334-5-111 10.1117/1.JBO.20.5.051033 10.3390/ijms241914703 10.1002/jbio.202200354 10.1016/j.artmed.2017.09.004 10.1002/bit.27933 10.1177/030089160709300609 10.1177/1094428107300338 10.1016/j.pdpdt.2018.10.014 10.2478/v10136-012-0031-x 10.1016/j.pdpdt.2018.05.012 |
| ContentType | Journal Article |
| Copyright | 2024 Copyright © 2024. Published by Elsevier B.V. |
| Copyright_xml | – notice: 2024 – notice: Copyright © 2024. Published by Elsevier B.V. |
| DBID | 6I. AAFTH AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 DOA |
| DOI | 10.1016/j.pdpdt.2024.104426 |
| DatabaseName | ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | 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: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Occupational Therapy & Rehabilitation |
| EISSN | 1873-1597 |
| ExternalDocumentID | oai_doaj_org_article_419effdf9d6e40e1b13f8d48928f8149 39615559 10_1016_j_pdpdt_2024_104426 S1572100024004629 |
| Genre | Journal Article |
| GroupedDBID | --- --K --M -RU .1- .FO .~1 0R~ 123 1B1 1P~ 1~. 1~5 4.4 457 4G. 53G 5VS 7-5 71M 8P~ AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AATTM AAXKI AAXUO AAYWO ABBQC ABJNI ABMAC ABMZM ABWVN ABXDB ACDAQ ACGFS ACIEU ACLOT ACRLP ACRPL ACVFH ADBBV ADCNI ADEZE ADMUD ADNMO ADVLN AEBSH AEIPS AEKER AENEX AEUPX AEVXI AFJKZ AFPUW AFRHN AFTJW AFXIZ AGHFR AGUBO AGYEJ AIEXJ AIGII AIIUN AIKHN AITUG AJRQY AJUYK AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU ANZVX APXCP AXJTR BKOJK BLXMC BNPGV CS3 DU5 EBS EFJIC EFKBS EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FIRID FNPLU FYGXN G-Q GBLVA GROUPED_DOAJ HVGLF HZ~ IHE J1W KOM M41 MO0 N9A O-L O9- OAUVE OC~ OO- OZT P-8 P-9 P2P PC. Q38 ROL RPZ SDF SDG SEL SES SEW SPCBC SSH SSZ T5K Z5R ~G- ~HD 6I. AACTN AAFTH AFKWA AJOXV AMFUW RIG 9DU AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 |
| ID | FETCH-LOGICAL-c522t-c96e7cf8ac5ee0f5427561c02cc64c20648e4c304fb6ebf9eff66483a476e2443 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 2 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001406678900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1572-1000 1873-1597 |
| IngestDate | Fri Oct 03 12:52:49 EDT 2025 Sun Sep 28 02:36:11 EDT 2025 Thu Apr 03 07:04:49 EDT 2025 Sat Nov 29 02:38:09 EST 2025 Tue Nov 18 22:48:42 EST 2025 Sat Jan 11 15:48:49 EST 2025 Tue Oct 14 19:34:05 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Autofluorescence spectroscopy Artificial neural network Linear discriminant analysis Pulmonary tuberculosis Blood plasma Principal component analysis |
| Language | English |
| License | This is an open access article under the CC BY-NC-ND license. Copyright © 2024. Published by Elsevier B.V. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c522t-c96e7cf8ac5ee0f5427561c02cc64c20648e4c304fb6ebf9eff66483a476e2443 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0003-4169-6019 |
| OpenAccessLink | https://doaj.org/article/419effdf9d6e40e1b13f8d48928f8149 |
| PMID | 39615559 |
| PQID | 3140887550 |
| PQPubID | 23479 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_419effdf9d6e40e1b13f8d48928f8149 proquest_miscellaneous_3140887550 pubmed_primary_39615559 crossref_citationtrail_10_1016_j_pdpdt_2024_104426 crossref_primary_10_1016_j_pdpdt_2024_104426 elsevier_sciencedirect_doi_10_1016_j_pdpdt_2024_104426 elsevier_clinicalkey_doi_10_1016_j_pdpdt_2024_104426 |
| PublicationCentury | 2000 |
| PublicationDate | December 2024 2024-12-00 2024-Dec 20241201 2024-12-01 |
| PublicationDateYYYYMMDD | 2024-12-01 |
| PublicationDate_xml | – month: 12 year: 2024 text: December 2024 |
| PublicationDecade | 2020 |
| PublicationPlace | Netherlands |
| PublicationPlace_xml | – name: Netherlands |
| PublicationTitle | Photodiagnosis and photodynamic therapy |
| PublicationTitleAlternate | Photodiagnosis Photodyn Ther |
| PublicationYear | 2024 |
| Publisher | Elsevier B.V Elsevier |
| Publisher_xml | – name: Elsevier B.V – name: Elsevier |
| References | Krysl, Korzeniewskakosela, Muller, Fitzgerald (bib0005) 1994; 45 Jonas, Alden, Curry (bib0002) 1993; 31 Dou, Dawuti, Zheng (bib0020) 2022; 40 Li, Deng, Yang (bib0049) 2019; 10 Bartosch-Härlid, Andersson, Aho (bib0045) 2008; 95 Al Zahrani, Al Jahdali, Poirier (bib0003) 2000; 162 Kalaivani, Masilamani, Sivaji (bib0015) 2008; 26 Khan, Ullah, Shahzad, Anbreen, Bilal, Khan, Khan (bib0017) 2018; 24 Li, Wang (bib0019) 2006; 6088 Ranjan, Sinha (bib0008) 2019; 32 Gupta, Majumder, Uppal (bib0032) 1997; 21 Danek, Bower (bib0007) 1979; 119 Vijayaraj, Abhinand, Venkatesan, Ragunath (bib0052) 2020; 16 Bai, Zhang, Wang (bib0041) 2022; 12 Shrirao, Schloss, Fritz (bib0025) 2021; 118 Lv, Wang, Ma (bib0035) 2022; 13 Goletti, Delogu, Matteelli, Migliori (bib0042) 2022; 124 Kumar, Gupta, Mandhani, Sankhwar (bib0009) 2016; 76 Lualdi, Colombo, Leo (bib0031) 2007; 93 Dande, Samant (bib0043) 2018; 108 Masilamani, Al-Zhrani, Al-Salhi (bib0014) 2004; 109 Dou, Dawuti, Zhou, Li, Zhang, Zheng, Lin, Lü (bib0034) 2023; 16 Wu, Gao, Smith, Bailin (bib0038) 2017; 10038 Walczak (bib0044) 2007; 10 Croce, Bottiroli (bib0050) 2014; 58 Zhou, Ni, Xu (bib0016) 2015; 95 Amato, López, Peña-Méndez (bib0040) 2013; 11 (accessed December 2023). Gao, Wu (bib0036) 2019; 10873 Yin, Mi, Zhai (bib0028) 2021; 193 Stone, Kendall, Smith (bib0022) 2004; 126 Wang, Tsai, Chen (bib0039) 2003; 32 Hans, Marwaha (bib0006) 2014; 8 Wang, Long, Tang (bib0021) 2017; 7 Krishna, Kurien, Mathew (bib0023) 2008; 8 Zheng, Wu, Wang (bib0010) 2022; 13 Pavithran M, Lukose, Barik (bib0013) 2023; 16 Soares, Becker, Anzanello (bib0051) 2017; 82 Wang, Zhu, Chen (bib0033) 2020; 224 Al-Salhi, Masilamani, Vijmasi (bib0030) 2011; 21 Shirshin, Cherkasova, Tikhonova (bib0029) 2015; 20 Atif, Alsalhi, Devanesan (bib0012) 2018; 23 Palmer, Zhu, Breslin (bib0037) 2003; 50 Madhuri, Vengadesan, Aruna (bib0027) 2003; 78 Weber, Lednev (bib0026) 2022; 2 Nartowt, Hart, Roffman (bib0046) 2019; 14 WHO, Global tuberculosis report 2023 Wybranowski, Ziomkowska, Cyrankiewicz (bib0024) 2023; 24 van Cleeff, Kivihya-Ndugga, Meme (bib0004) 2005; 5 Eswari, Chandrakar (bib0047) 2016; 33 Berezin, Achilefu (bib0048) 2010; 110 Li, Yang, Li, Jin, Wang, Guan, Ding (bib0018) 2015; 23 Wang, Jiang, Mo (bib0011) 2020; 74 Amato (10.1016/j.pdpdt.2024.104426_bib0040) 2013; 11 Berezin (10.1016/j.pdpdt.2024.104426_bib0048) 2010; 110 Dou (10.1016/j.pdpdt.2024.104426_bib0034) 2023; 16 Eswari (10.1016/j.pdpdt.2024.104426_bib0047) 2016; 33 Nartowt (10.1016/j.pdpdt.2024.104426_bib0046) 2019; 14 Hans (10.1016/j.pdpdt.2024.104426_bib0006) 2014; 8 Lv (10.1016/j.pdpdt.2024.104426_bib0035) 2022; 13 Bartosch-Härlid (10.1016/j.pdpdt.2024.104426_bib0045) 2008; 95 Krysl (10.1016/j.pdpdt.2024.104426_bib0005) 1994; 45 Masilamani (10.1016/j.pdpdt.2024.104426_bib0014) 2004; 109 Wang (10.1016/j.pdpdt.2024.104426_bib0033) 2020; 224 Walczak (10.1016/j.pdpdt.2024.104426_bib0044) 2007; 10 Atif (10.1016/j.pdpdt.2024.104426_bib0012) 2018; 23 Khan (10.1016/j.pdpdt.2024.104426_bib0017) 2018; 24 Jonas (10.1016/j.pdpdt.2024.104426_bib0002) 1993; 31 Zheng (10.1016/j.pdpdt.2024.104426_bib0010) 2022; 13 Stone (10.1016/j.pdpdt.2024.104426_bib0022) 2004; 126 Goletti (10.1016/j.pdpdt.2024.104426_bib0042) 2022; 124 Weber (10.1016/j.pdpdt.2024.104426_bib0026) 2022; 2 Wang (10.1016/j.pdpdt.2024.104426_bib0011) 2020; 74 Yin (10.1016/j.pdpdt.2024.104426_bib0028) 2021; 193 Kumar (10.1016/j.pdpdt.2024.104426_bib0009) 2016; 76 Croce (10.1016/j.pdpdt.2024.104426_bib0050) 2014; 58 Al Zahrani (10.1016/j.pdpdt.2024.104426_bib0003) 2000; 162 Wybranowski (10.1016/j.pdpdt.2024.104426_bib0024) 2023; 24 van Cleeff (10.1016/j.pdpdt.2024.104426_bib0004) 2005; 5 Shrirao (10.1016/j.pdpdt.2024.104426_bib0025) 2021; 118 Krishna (10.1016/j.pdpdt.2024.104426_bib0023) 2008; 8 Zhou (10.1016/j.pdpdt.2024.104426_bib0016) 2015; 95 Dou (10.1016/j.pdpdt.2024.104426_bib0020) 2022; 40 Palmer (10.1016/j.pdpdt.2024.104426_bib0037) 2003; 50 Wang (10.1016/j.pdpdt.2024.104426_bib0039) 2003; 32 Bai (10.1016/j.pdpdt.2024.104426_bib0041) 2022; 12 Li (10.1016/j.pdpdt.2024.104426_bib0049) 2019; 10 Vijayaraj (10.1016/j.pdpdt.2024.104426_bib0052) 2020; 16 Dande (10.1016/j.pdpdt.2024.104426_bib0043) 2018; 108 Madhuri (10.1016/j.pdpdt.2024.104426_bib0027) 2003; 78 10.1016/j.pdpdt.2024.104426_bib0001 Li (10.1016/j.pdpdt.2024.104426_bib0019) 2006; 6088 Li (10.1016/j.pdpdt.2024.104426_bib0018) 2015; 23 Al-Salhi (10.1016/j.pdpdt.2024.104426_bib0030) 2011; 21 Pavithran M (10.1016/j.pdpdt.2024.104426_bib0013) 2023; 16 Gao (10.1016/j.pdpdt.2024.104426_bib0036) 2019; 10873 Shirshin (10.1016/j.pdpdt.2024.104426_bib0029) 2015; 20 Ranjan (10.1016/j.pdpdt.2024.104426_bib0008) 2019; 32 Gupta (10.1016/j.pdpdt.2024.104426_bib0032) 1997; 21 Wu (10.1016/j.pdpdt.2024.104426_bib0038) 2017; 10038 Lualdi (10.1016/j.pdpdt.2024.104426_bib0031) 2007; 93 Wang (10.1016/j.pdpdt.2024.104426_bib0021) 2017; 7 Danek (10.1016/j.pdpdt.2024.104426_bib0007) 1979; 119 Soares (10.1016/j.pdpdt.2024.104426_bib0051) 2017; 82 Kalaivani (10.1016/j.pdpdt.2024.104426_bib0015) 2008; 26 |
| References_xml | – volume: 13 start-page: 1912 year: 2022 end-page: 1923 ident: bib0010 article-title: Rapid detection of hysteromyoma and cervical cancer based on serum surface-enhanced Raman spectroscopy and a support vector machine publication-title: Biomed. Opt. Express – volume: 16 year: 2023 ident: bib0013 article-title: Laser induced fluorescence spectroscopy analysis of kidney tissues: a pilot study for the identification of renal cell carcinoma publication-title: J. Biophotonics – volume: 124 start-page: S12 year: 2022 end-page: S19 ident: bib0042 article-title: The role of IGRA in the diagnosis of tuberculosis infection, differentiating from active tuberculosis, and decision making for initiating treatment or preventive therapy of tuberculosis infection publication-title: Int. J. Infect. Dis. – volume: 95 start-page: 817 year: 2008 end-page: 826 ident: bib0045 article-title: Artificial neural networks in pancreatic disease publication-title: Br. J. Surg. – volume: 21 start-page: 637 year: 2011 end-page: 645 ident: bib0030 article-title: Lung cancer detection by native fluorescence spectra of body fluids-a preliminary study publication-title: J. Fluoresc. – volume: 7 start-page: 32 year: 2017 end-page: 43 ident: bib0021 article-title: Autofluorescence imaging and spectroscopy of human lung cancer publication-title: Appl. Sci – volume: 16 start-page: 539 year: 2020 end-page: 546 ident: bib0052 article-title: An ANN model for the differential diagnosis of tuberculosis and sarcoidosis publication-title: Bioinformation – reference: WHO, Global tuberculosis report 2023, – volume: 78 start-page: 197 year: 2003 end-page: 204 ident: bib0027 article-title: Native fluorescence spectroscopy of blood plasma in the characterization of oral malignancy publication-title: Photochem. Photobiol. – volume: 12 start-page: 9810 year: 2022 ident: bib0041 article-title: Improved diagnosis of rheumatoid arthritis using an artificial neural network publication-title: Sci. Rep. – volume: 33 start-page: 1318 year: 2016 end-page: 1324 ident: bib0047 article-title: Artificial neural networks as classification and diagnostic tools for lymph node-negative breast cancers publication-title: Korean J. Chem. Eng. – volume: 45 start-page: 101 year: 1994 end-page: 107 ident: bib0005 article-title: Radiologic features of pulmonary tuberculosis - an assessment of 188 cases publication-title: Can. Assoc. Radiol. J. – volume: 76 start-page: 1106 year: 2016 end-page: 1119 ident: bib0009 article-title: NMR spectroscopy of filtered serum of prostate cancer: a new frontier in metabolomics publication-title: Prostate – volume: 193 year: 2021 ident: bib0028 article-title: An effective approach to the early diagnosis of colorectal cancer based on three-dimensional fluorescence spectra of human blood plasma publication-title: J. Pharm. Biomed. Anal. – volume: 126 start-page: 141 year: 2004 end-page: 157 ident: bib0022 article-title: Raman spectroscopy for identification of epithelial cancers publication-title: Faraday Discuss. – volume: 2 year: 2022 ident: bib0026 article-title: Brightness of blood: review of fluorescence spectroscopy analysis of bloodstains publication-title: Front. Anal. Sci. – volume: 14 year: 2019 ident: bib0046 article-title: Scoring colorectal cancer risk with an artificial neural network based on self-reportable personal health data publication-title: PLoS One – volume: 24 start-page: 14703 year: 2023 ident: bib0024 article-title: Time-resolved fluorescence spectroscopy of blood, plasma and albumin as a potential diagnostic tool for acute inflammation in COVID-19 pneumonia patients publication-title: Int. J. Mol. Sci. – volume: 23 start-page: 18361 year: 2015 end-page: 18372 ident: bib0018 article-title: Noninvasive liver diseases detection based on serum surface enhanced Raman spectroscopy and statistical analysis publication-title: Opt. Express – volume: 224 year: 2020 ident: bib0033 article-title: Autofluorescence spectroscopy of blood plasma with multivariate analysis methods for the diagnosis of pulmonary tuberculosis publication-title: Optik – volume: 119 start-page: 677 year: 1979 end-page: 679 ident: bib0007 article-title: Diagnosis of pulmonary tuberculosis by flexible fiberoptic bronchoscopy publication-title: Am. Rev. Respir. Dis. – volume: 21 start-page: 417 year: 1997 end-page: 422 ident: bib0032 article-title: Breast cancer diagnosis using N publication-title: Lasers Surg. Med. – volume: 82 start-page: 1 year: 2017 end-page: 10 ident: bib0051 article-title: A hierarchical classifier based on human blood plasma fluorescence for non-invasive colorectal cancer screening publication-title: Artif. Intell. Med. – volume: 109 start-page: 143 year: 2004 end-page: 154 ident: bib0014 article-title: Cancer diagnosis by autofluorescence of blood components publication-title: J. Lumin. – volume: 24 start-page: 286 year: 2018 end-page: 291 ident: bib0017 article-title: Analysis of tuberculosis disease through Raman spectroscopy and machine learning publication-title: Photodiagnosis Photodyn. Ther. – volume: 23 start-page: 40 year: 2018 end-page: 44 ident: bib0012 article-title: A study for the detection of kidney cancer using fluorescence emission spectra and synchronous fluorescence excitation spectra of blood and urine publication-title: Photodiagnosis Photodyn. Ther. – volume: 40 year: 2022 ident: bib0020 article-title: Urine fluorescence spectroscopy combined with machine learning for screening of hepatocellular carcinoma and liver cirrhosis publication-title: Photodiagnosis Photodyn. Ther. – volume: 162 start-page: 1323 year: 2000 end-page: 1329 ident: bib0003 article-title: Accuracy and utility of commercially available amplification and serologic tests for the diagnosis of minimal pulmonary tuberculosis publication-title: Am. J. Respir. Crit. Care Med. – reference: (accessed December 2023). – volume: 26 start-page: 251 year: 2008 end-page: 256 ident: bib0015 article-title: Fluorescence spectra of blood components for breast cancer diagnosis publication-title: Photomed. Laser Surg. – volume: 10038 year: 2017 ident: bib0038 article-title: Optical biopsy using fluorescence spectroscopy for prostate cancer diagnosis publication-title: Proceedings of the SPIE – volume: 31 start-page: 2410 year: 1993 end-page: 2416 ident: bib0002 article-title: Detection and identification of mycobacterium-tuberculosis directly from sputum sediments by amplification of ribosomal-RNA publication-title: J. Clin. Microbiol. – volume: 10873 year: 2019 ident: bib0036 article-title: Breast cancer diagnosis using fluorescence spectroscopy with dual-wavelength excitation and machine learning publication-title: Proceedings of the SPIE – volume: 110 start-page: 2641 year: 2010 end-page: 2684 ident: bib0048 article-title: Fluorescence lifetime measurements and biological imaging publication-title: Chem. Rev. – volume: 93 start-page: 567 year: 2007 end-page: 571 ident: bib0031 article-title: Natural fluorescence spectroscopy of human blood plasma in the diagnosis of colorectal cancer: feasibility study and preliminary results publication-title: Tumori – volume: 58 start-page: 320 year: 2014 end-page: 337 ident: bib0050 article-title: Autofluorescence spectroscopy and imaging: a tool for biomedical research and diagnosis publication-title: Eur. J. Histochem. – volume: 74 start-page: 674 year: 2020 end-page: 683 ident: bib0011 article-title: Rapid screening of thyroid dysfunction using raman spectroscopy combined with an improved support vector machine publication-title: Appl. Spectrosc. – volume: 8 start-page: 149 year: 2008 end-page: 166 ident: bib0023 article-title: Raman spectroscopy of breast tissues publication-title: Expert Rev. Mol. Diagn. – volume: 10 start-page: 710 year: 2007 end-page: 712 ident: bib0044 article-title: Neural networks in organizational research: applying pattern recognition to the analysis of organizational behavior publication-title: Organ. Res. Methods – volume: 5 start-page: 111 year: 2005 ident: bib0004 article-title: The role and performance of chest X-ray for the diagnosis of tuberculosis: a cost-effectiveness analysis in Nairobi, Kenya publication-title: BMC Infect. Dis. – volume: 10 start-page: 4999 year: 2019 end-page: 5014 ident: bib0049 article-title: Early diagnosis of gastric cancer based on deep learning combined with the spectral-spatial classification method publication-title: Biomed. Opt. Express – volume: 32 start-page: e3916 year: 2019 ident: bib0008 article-title: Nuclear magnetic resonance (NMR)-based metabolomics for cancer research publication-title: NMR Biomed. – volume: 95 start-page: 294 year: 2015 end-page: 302 ident: bib0016 article-title: Metabolomics specificity of tuberculosis plasma revealed by publication-title: Tuberculosis – volume: 32 start-page: 318 year: 2003 end-page: 326 ident: bib0039 article-title: PLS-ANN based classification model for oral submucous fibrosis and oral carcinogenesis publication-title: Lasers Surg. Med. – volume: 118 start-page: 4550 year: 2021 end-page: 4576 ident: bib0025 article-title: Autofluorescence of blood and its application in biomedical and clinical research publication-title: Biotechnol. Bioeng. – volume: 8 start-page: 2 year: 2014 end-page: 3 ident: bib0006 article-title: Nucleic acid testing-benefits and constraints publication-title: Asian J. Transf. Sci. – volume: 16 year: 2023 ident: bib0034 article-title: Rapid detection of cholecystitis by serum fluorescence spectroscopy combined with machine learning publication-title: J. Biophotonics – volume: 50 start-page: 1233 year: 2003 end-page: 1242 ident: bib0037 article-title: Comparison of multiexcitation fluorescence and diffuse reflectance spectroscopy for the diagnosis of breast cancer (March 2003) publication-title: IEEE Trans. Biomed. Eng. – volume: 108 start-page: 1 year: 2018 end-page: 9 ident: bib0043 article-title: Acquaintance to artificial neural networks and use of artificial intelligence as a diagnostic tool for tuberculosis: a review publication-title: Tuberculosis – volume: 20 year: 2015 ident: bib0029 article-title: Native fluorescence spectroscopy of blood plasma of rats with experimental diabetes: identifying fingerprints of glucose-related metabolic pathways publication-title: J. Biomed. Opt. – volume: 13 start-page: 9238 year: 2022 end-page: 9249 ident: bib0035 article-title: Machine learning enhanced optical spectroscopy for disease detection publication-title: J. Phys. Chem. Lett. – volume: 11 start-page: 47 year: 2013 end-page: 58 ident: bib0040 article-title: Artificial neural networks in medical diagnosis publication-title: J. Appl. Biomed. – volume: 6088 year: 2006 ident: bib0019 article-title: Spectral analysis of lung cancer serum using fluorescence and Raman spectroscopy publication-title: Proceedings of the SPIE – volume: 40 year: 2022 ident: 10.1016/j.pdpdt.2024.104426_bib0020 article-title: Urine fluorescence spectroscopy combined with machine learning for screening of hepatocellular carcinoma and liver cirrhosis publication-title: Photodiagnosis Photodyn. Ther. doi: 10.1016/j.pdpdt.2022.103102 – volume: 31 start-page: 2410 issue: 9 year: 1993 ident: 10.1016/j.pdpdt.2024.104426_bib0002 article-title: Detection and identification of mycobacterium-tuberculosis directly from sputum sediments by amplification of ribosomal-RNA publication-title: J. Clin. Microbiol. doi: 10.1128/jcm.31.9.2410-2416.1993 – volume: 8 start-page: 149 issue: 2 year: 2008 ident: 10.1016/j.pdpdt.2024.104426_bib0023 article-title: Raman spectroscopy of breast tissues publication-title: Expert Rev. Mol. Diagn. doi: 10.1586/14737159.8.2.149 – volume: 78 start-page: 197 issue: 2 year: 2003 ident: 10.1016/j.pdpdt.2024.104426_bib0027 article-title: Native fluorescence spectroscopy of blood plasma in the characterization of oral malignancy publication-title: Photochem. Photobiol. doi: 10.1562/0031-8655(2003)078<0197:NFSOBP>2.0.CO;2 – volume: 10873 year: 2019 ident: 10.1016/j.pdpdt.2024.104426_bib0036 article-title: Breast cancer diagnosis using fluorescence spectroscopy with dual-wavelength excitation and machine learning – volume: 14 issue: 8 year: 2019 ident: 10.1016/j.pdpdt.2024.104426_bib0046 article-title: Scoring colorectal cancer risk with an artificial neural network based on self-reportable personal health data publication-title: PLoS One doi: 10.1371/journal.pone.0221421 – volume: 16 issue: 11 year: 2023 ident: 10.1016/j.pdpdt.2024.104426_bib0013 article-title: Laser induced fluorescence spectroscopy analysis of kidney tissues: a pilot study for the identification of renal cell carcinoma publication-title: J. Biophotonics doi: 10.1002/jbio.202300021 – volume: 21 start-page: 417 issue: 5 year: 1997 ident: 10.1016/j.pdpdt.2024.104426_bib0032 article-title: Breast cancer diagnosis using N2 laser excited autofluorescence spectroscopy publication-title: Lasers Surg. Med. doi: 10.1002/(SICI)1096-9101(1997)21:5<417::AID-LSM2>3.0.CO;2-T – volume: 95 start-page: 817 issue: 7 year: 2008 ident: 10.1016/j.pdpdt.2024.104426_bib0045 article-title: Artificial neural networks in pancreatic disease publication-title: Br. J. Surg. doi: 10.1002/bjs.6239 – volume: 32 start-page: 318 issue: 4 year: 2003 ident: 10.1016/j.pdpdt.2024.104426_bib0039 article-title: PLS-ANN based classification model for oral submucous fibrosis and oral carcinogenesis publication-title: Lasers Surg. Med. doi: 10.1002/lsm.10153 – volume: 12 start-page: 9810 issue: 1 year: 2022 ident: 10.1016/j.pdpdt.2024.104426_bib0041 article-title: Improved diagnosis of rheumatoid arthritis using an artificial neural network publication-title: Sci. Rep. doi: 10.1038/s41598-022-13750-9 – volume: 124 start-page: S12 year: 2022 ident: 10.1016/j.pdpdt.2024.104426_bib0042 article-title: The role of IGRA in the diagnosis of tuberculosis infection, differentiating from active tuberculosis, and decision making for initiating treatment or preventive therapy of tuberculosis infection publication-title: Int. J. Infect. Dis. doi: 10.1016/j.ijid.2022.02.047 – volume: 21 start-page: 637 issue: 2 year: 2011 ident: 10.1016/j.pdpdt.2024.104426_bib0030 article-title: Lung cancer detection by native fluorescence spectra of body fluids-a preliminary study publication-title: J. Fluoresc. doi: 10.1007/s10895-010-0751-9 – volume: 224 year: 2020 ident: 10.1016/j.pdpdt.2024.104426_bib0033 article-title: Autofluorescence spectroscopy of blood plasma with multivariate analysis methods for the diagnosis of pulmonary tuberculosis publication-title: Optik doi: 10.1016/j.ijleo.2020.165446 – volume: 10038 year: 2017 ident: 10.1016/j.pdpdt.2024.104426_bib0038 article-title: Optical biopsy using fluorescence spectroscopy for prostate cancer diagnosis – volume: 162 start-page: 1323 issue: 4 year: 2000 ident: 10.1016/j.pdpdt.2024.104426_bib0003 article-title: Accuracy and utility of commercially available amplification and serologic tests for the diagnosis of minimal pulmonary tuberculosis publication-title: Am. J. Respir. Crit. Care Med. doi: 10.1164/ajrccm.162.4.9912115 – volume: 26 start-page: 251 issue: 3 year: 2008 ident: 10.1016/j.pdpdt.2024.104426_bib0015 article-title: Fluorescence spectra of blood components for breast cancer diagnosis publication-title: Photomed. Laser Surg. doi: 10.1089/pho.2007.2162 – volume: 50 start-page: 1233 issue: 11 year: 2003 ident: 10.1016/j.pdpdt.2024.104426_bib0037 article-title: Comparison of multiexcitation fluorescence and diffuse reflectance spectroscopy for the diagnosis of breast cancer (March 2003) publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2003.818488 – volume: 13 start-page: 1912 issue: 4 year: 2022 ident: 10.1016/j.pdpdt.2024.104426_bib0010 article-title: Rapid detection of hysteromyoma and cervical cancer based on serum surface-enhanced Raman spectroscopy and a support vector machine publication-title: Biomed. Opt. Express doi: 10.1364/BOE.448121 – volume: 10 start-page: 4999 issue: 10 year: 2019 ident: 10.1016/j.pdpdt.2024.104426_bib0049 article-title: Early diagnosis of gastric cancer based on deep learning combined with the spectral-spatial classification method publication-title: Biomed. Opt. Express doi: 10.1364/BOE.10.004999 – volume: 126 start-page: 141 year: 2004 ident: 10.1016/j.pdpdt.2024.104426_bib0022 article-title: Raman spectroscopy for identification of epithelial cancers publication-title: Faraday Discuss. doi: 10.1039/b304992b – volume: 8 start-page: 2 issue: 1 year: 2014 ident: 10.1016/j.pdpdt.2024.104426_bib0006 article-title: Nucleic acid testing-benefits and constraints publication-title: Asian J. Transf. Sci. doi: 10.4103/0973-6247.126679 – volume: 76 start-page: 1106 issue: 12 year: 2016 ident: 10.1016/j.pdpdt.2024.104426_bib0009 article-title: NMR spectroscopy of filtered serum of prostate cancer: a new frontier in metabolomics publication-title: Prostate doi: 10.1002/pros.23198 – ident: 10.1016/j.pdpdt.2024.104426_bib0001 – volume: 33 start-page: 1318 issue: 4 year: 2016 ident: 10.1016/j.pdpdt.2024.104426_bib0047 article-title: Artificial neural networks as classification and diagnostic tools for lymph node-negative breast cancers publication-title: Korean J. Chem. Eng. doi: 10.1007/s11814-015-0255-z – volume: 110 start-page: 2641 issue: 5 year: 2010 ident: 10.1016/j.pdpdt.2024.104426_bib0048 article-title: Fluorescence lifetime measurements and biological imaging publication-title: Chem. Rev. doi: 10.1021/cr900343z – volume: 16 start-page: 539 issue: 7 year: 2020 ident: 10.1016/j.pdpdt.2024.104426_bib0052 article-title: An ANN model for the differential diagnosis of tuberculosis and sarcoidosis publication-title: Bioinformation doi: 10.6026/97320630016539 – volume: 95 start-page: 294 issue: 3 year: 2015 ident: 10.1016/j.pdpdt.2024.104426_bib0016 article-title: Metabolomics specificity of tuberculosis plasma revealed by 1H NMR spectroscopy publication-title: Tuberculosis doi: 10.1016/j.tube.2015.02.038 – volume: 45 start-page: 101 issue: 2 year: 1994 ident: 10.1016/j.pdpdt.2024.104426_bib0005 article-title: Radiologic features of pulmonary tuberculosis - an assessment of 188 cases publication-title: Can. Assoc. Radiol. J. – volume: 108 start-page: 1 year: 2018 ident: 10.1016/j.pdpdt.2024.104426_bib0043 article-title: Acquaintance to artificial neural networks and use of artificial intelligence as a diagnostic tool for tuberculosis: a review publication-title: Tuberculosis doi: 10.1016/j.tube.2017.09.006 – volume: 7 start-page: 32 issue: 1 year: 2017 ident: 10.1016/j.pdpdt.2024.104426_bib0021 article-title: Autofluorescence imaging and spectroscopy of human lung cancer publication-title: Appl. Sci doi: 10.3390/app7010032 – volume: 32 start-page: e3916 issue: 10 year: 2019 ident: 10.1016/j.pdpdt.2024.104426_bib0008 article-title: Nuclear magnetic resonance (NMR)-based metabolomics for cancer research publication-title: NMR Biomed. doi: 10.1002/nbm.3916 – volume: 119 start-page: 677 issue: 4 year: 1979 ident: 10.1016/j.pdpdt.2024.104426_bib0007 article-title: Diagnosis of pulmonary tuberculosis by flexible fiberoptic bronchoscopy publication-title: Am. Rev. Respir. Dis. – volume: 23 start-page: 18361 issue: 14 year: 2015 ident: 10.1016/j.pdpdt.2024.104426_bib0018 article-title: Noninvasive liver diseases detection based on serum surface enhanced Raman spectroscopy and statistical analysis publication-title: Opt. Express doi: 10.1364/OE.23.018361 – volume: 2 year: 2022 ident: 10.1016/j.pdpdt.2024.104426_bib0026 article-title: Brightness of blood: review of fluorescence spectroscopy analysis of bloodstains publication-title: Front. Anal. Sci. doi: 10.3389/frans.2022.906532 – volume: 13 start-page: 9238 issue: 39 year: 2022 ident: 10.1016/j.pdpdt.2024.104426_bib0035 article-title: Machine learning enhanced optical spectroscopy for disease detection publication-title: J. Phys. Chem. Lett. doi: 10.1021/acs.jpclett.2c02193 – volume: 193 year: 2021 ident: 10.1016/j.pdpdt.2024.104426_bib0028 article-title: An effective approach to the early diagnosis of colorectal cancer based on three-dimensional fluorescence spectra of human blood plasma publication-title: J. Pharm. Biomed. Anal. doi: 10.1016/j.jpba.2020.113757 – volume: 74 start-page: 674 issue: 6 year: 2020 ident: 10.1016/j.pdpdt.2024.104426_bib0011 article-title: Rapid screening of thyroid dysfunction using raman spectroscopy combined with an improved support vector machine publication-title: Appl. Spectrosc. doi: 10.1177/0003702820904444 – volume: 5 start-page: 111 year: 2005 ident: 10.1016/j.pdpdt.2024.104426_bib0004 article-title: The role and performance of chest X-ray for the diagnosis of tuberculosis: a cost-effectiveness analysis in Nairobi, Kenya publication-title: BMC Infect. Dis. doi: 10.1186/1471-2334-5-111 – volume: 20 issue: 5 year: 2015 ident: 10.1016/j.pdpdt.2024.104426_bib0029 article-title: Native fluorescence spectroscopy of blood plasma of rats with experimental diabetes: identifying fingerprints of glucose-related metabolic pathways publication-title: J. Biomed. Opt. doi: 10.1117/1.JBO.20.5.051033 – volume: 58 start-page: 320 issue: 4 year: 2014 ident: 10.1016/j.pdpdt.2024.104426_bib0050 article-title: Autofluorescence spectroscopy and imaging: a tool for biomedical research and diagnosis publication-title: Eur. J. Histochem. – volume: 109 start-page: 143 issue: 3–4 year: 2004 ident: 10.1016/j.pdpdt.2024.104426_bib0014 article-title: Cancer diagnosis by autofluorescence of blood components publication-title: J. Lumin. – volume: 24 start-page: 14703 issue: 19 year: 2023 ident: 10.1016/j.pdpdt.2024.104426_bib0024 article-title: Time-resolved fluorescence spectroscopy of blood, plasma and albumin as a potential diagnostic tool for acute inflammation in COVID-19 pneumonia patients publication-title: Int. J. Mol. Sci. doi: 10.3390/ijms241914703 – volume: 16 issue: 8 year: 2023 ident: 10.1016/j.pdpdt.2024.104426_bib0034 article-title: Rapid detection of cholecystitis by serum fluorescence spectroscopy combined with machine learning publication-title: J. Biophotonics doi: 10.1002/jbio.202200354 – volume: 82 start-page: 1 year: 2017 ident: 10.1016/j.pdpdt.2024.104426_bib0051 article-title: A hierarchical classifier based on human blood plasma fluorescence for non-invasive colorectal cancer screening publication-title: Artif. Intell. Med. doi: 10.1016/j.artmed.2017.09.004 – volume: 118 start-page: 4550 issue: 12 year: 2021 ident: 10.1016/j.pdpdt.2024.104426_bib0025 article-title: Autofluorescence of blood and its application in biomedical and clinical research publication-title: Biotechnol. Bioeng. doi: 10.1002/bit.27933 – volume: 6088 year: 2006 ident: 10.1016/j.pdpdt.2024.104426_bib0019 article-title: Spectral analysis of lung cancer serum using fluorescence and Raman spectroscopy – volume: 93 start-page: 567 issue: 6 year: 2007 ident: 10.1016/j.pdpdt.2024.104426_bib0031 article-title: Natural fluorescence spectroscopy of human blood plasma in the diagnosis of colorectal cancer: feasibility study and preliminary results publication-title: Tumori doi: 10.1177/030089160709300609 – volume: 10 start-page: 710 issue: 4 year: 2007 ident: 10.1016/j.pdpdt.2024.104426_bib0044 article-title: Neural networks in organizational research: applying pattern recognition to the analysis of organizational behavior publication-title: Organ. Res. Methods doi: 10.1177/1094428107300338 – volume: 24 start-page: 286 year: 2018 ident: 10.1016/j.pdpdt.2024.104426_bib0017 article-title: Analysis of tuberculosis disease through Raman spectroscopy and machine learning publication-title: Photodiagnosis Photodyn. Ther. doi: 10.1016/j.pdpdt.2018.10.014 – volume: 11 start-page: 47 issue: 2 year: 2013 ident: 10.1016/j.pdpdt.2024.104426_bib0040 article-title: Artificial neural networks in medical diagnosis publication-title: J. Appl. Biomed. doi: 10.2478/v10136-012-0031-x – volume: 23 start-page: 40 year: 2018 ident: 10.1016/j.pdpdt.2024.104426_bib0012 article-title: A study for the detection of kidney cancer using fluorescence emission spectra and synchronous fluorescence excitation spectra of blood and urine publication-title: Photodiagnosis Photodyn. Ther. doi: 10.1016/j.pdpdt.2018.05.012 |
| SSID | ssj0034904 |
| Score | 2.3644412 |
| Snippet | •A rapid, accurate, label-free and non-invasive diagnosis method of pulmonary tuberculosis (TB) has been developed by using plasma autofluorescence... The existing clinical diagnostic methods of pulmonary tuberculosis (TB) usually have some of the following limitations, such as time-consuming, invasive,... |
| SourceID | doaj proquest pubmed crossref elsevier |
| SourceType | Open Website Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 104426 |
| SubjectTerms | Adult Algorithms Artificial neural network Autofluorescence spectroscopy Blood plasma Female Humans Latent Tuberculosis - diagnosis Linear discriminant analysis Male Middle Aged Neural Networks, Computer Principal component analysis Pulmonary tuberculosis Spectrometry, Fluorescence - methods Tuberculosis, Pulmonary - blood Tuberculosis, Pulmonary - diagnosis |
| Title | Rapid diagnosis of latent and active pulmonary tuberculosis by autofluorescence spectroscopy of blood plasma combined with artificial neural network algorithm |
| URI | https://www.clinicalkey.com/#!/content/1-s2.0-S1572100024004629 https://dx.doi.org/10.1016/j.pdpdt.2024.104426 https://www.ncbi.nlm.nih.gov/pubmed/39615559 https://www.proquest.com/docview/3140887550 https://doaj.org/article/419effdf9d6e40e1b13f8d48928f8149 |
| Volume | 50 |
| WOSCitedRecordID | wos001406678900001&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: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1873-1597 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0034904 issn: 1572-1000 databaseCode: DOA dateStart: 20240101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1873-1597 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0034904 issn: 1572-1000 databaseCode: AIEXJ dateStart: 20040501 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELagcOCCeLMFVoMEnIjIw0nsY0GtgEOFqiLtzfITLUqTKJsg9c_0tzK2N6vtgfbCKZJlO4lnMvONPfmGkHeIEDCuqm2SS1omtCqKhJVSJSXXNE8rzVKpQ7GJ-vSUrVb8x16pL58TFumB48J9ohm3zhnHTWVpajOVFY4ZynjOHEN4761vWvM5mIo2uKA8FA7MyjpP_A72zDcUMrt60xufRplTf8JJPa_Cnk8K1P3XXNO_oGdwQSePyMMtdoSj-MyPyR3bPiHv93mC4TySBMAHOLtGwf2UXJ3Jfm3AxNS69QY6Bw0CzXYE2RqQwe5BPzWol3K4hHFSdtBTE_qqS5DT2Llm6obA_6QthH80PRdmh_fDyUIKPPSIxi8koB5jyG0N-H1e8KscmSrA82eGS8g-B9n86gbscvGM_Dw5Pv_yNdkWZ0g0QrYx0byytXZM6tLa1JXU88hnOs21rqhGMVNmqS5S6lRllfNCrLCtkLSuLGKK4jk5aLvWviSANsA6hn5Rc0pr4yTXiivlnGIK3We5IPksHqG3y-YLaDRiTlH7LYJMhZepiDJdkI-7QX0k7ri5-2cv911Xz7odGlAXxVYXxW26uCB01hox_9iKphgnWt9872o3bIt7Ip65feDbWTUFWgV_1CNb200bUWDcjJ8ghp8L8iLq7O7VCu7Pokt--D9e-RV54B8oJvi8JgfjMNk35L7-M643w5LcrVdsSe4dfTtefV-GT_QvbNhBqw |
| linkProvider | Directory of Open Access Journals |
| 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=Rapid+diagnosis+of+latent+and+active+pulmonary+tuberculosis+by+autofluorescence+spectroscopy+of+blood+plasma+combined+with+artificial+neural+network+algorithm&rft.jtitle=Photodiagnosis+and+photodynamic+therapy&rft.au=Yue%2C+Fengjiao&rft.au=Li%2C+Si&rft.au=Wu%2C+Lijuan&rft.au=Chen%2C+Xuerong&rft.date=2024-12-01&rft.pub=Elsevier+B.V&rft.issn=1572-1000&rft.volume=50&rft_id=info:doi/10.1016%2Fj.pdpdt.2024.104426&rft.externalDocID=S1572100024004629 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1572-1000&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1572-1000&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1572-1000&client=summon |