Training Ultrasound Image Classification Deep-Learning Algorithms for Pneumothorax Detection Using a Synthetic Tissue Phantom Apparatus

Ultrasound (US) imaging is a critical tool in emergency and military medicine because of its portability and immediate nature. However, proper image interpretation requires skill, limiting its utility in remote applications for conditions such as pneumothorax (PTX) which requires rapid intervention....

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Journal of imaging Ročník 8; číslo 9; s. 249
Hlavní autori:	Boice, Emily N., Hernandez Torres, Sofia I., Knowlton, Zechariah J., Berard, David, Gonzalez, Jose M., Avital, Guy, Snider, Eric J.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 11.09.2022 MDPI
Predmet:	Algorithms Artificial intelligence automation Classification Datasets Deep learning Emergency medical care Emergency medical services Gelatin Image acquisition Image analysis Image classification Lungs Machine learning model development Pneumothorax porcine Respiration Shrapnel Swine Three dimensional printing tissue phantom Training Trouble shooting Troubleshooting Ultrasonic imaging ultrasound X-rays United States > US
ISSN:	2313-433X, 2313-433X
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	Ultrasound (US) imaging is a critical tool in emergency and military medicine because of its portability and immediate nature. However, proper image interpretation requires skill, limiting its utility in remote applications for conditions such as pneumothorax (PTX) which requires rapid intervention. Artificial intelligence has the potential to automate ultrasound image analysis for various pathophysiological conditions. Training models require large data sets and a means of troubleshooting in real-time for ultrasound integration deployment, and they also require large animal models or clinical testing. Here, we detail the development of a dynamic synthetic tissue phantom model for PTX and its use in training image classification algorithms. The model comprises a synthetic gelatin phantom cast in a custom 3D-printed rib mold and a lung mimicking phantom. When compared to PTX images acquired in swine, images from the phantom were similar in both PTX negative and positive mimicking scenarios. We then used a deep learning image classification algorithm, which we previously developed for shrapnel detection, to accurately predict the presence of PTX in swine images by only training on phantom image sets, highlighting the utility for a tissue phantom for AI applications.
AbstractList	Ultrasound (US) imaging is a critical tool in emergency and military medicine because of its portability and immediate nature. However, proper image interpretation requires skill, limiting its utility in remote applications for conditions such as pneumothorax (PTX) which requires rapid intervention. Artificial intelligence has the potential to automate ultrasound image analysis for various pathophysiological conditions. Training models require large data sets and a means of troubleshooting in real-time for ultrasound integration deployment, and they also require large animal models or clinical testing. Here, we detail the development of a dynamic synthetic tissue phantom model for PTX and its use in training image classification algorithms. The model comprises a synthetic gelatin phantom cast in a custom 3D-printed rib mold and a lung mimicking phantom. When compared to PTX images acquired in swine, images from the phantom were similar in both PTX negative and positive mimicking scenarios. We then used a deep learning image classification algorithm, which we previously developed for shrapnel detection, to accurately predict the presence of PTX in swine images by only training on phantom image sets, highlighting the utility for a tissue phantom for AI applications. Ultrasound (US) imaging is a critical tool in emergency and military medicine because of its portability and immediate nature. However, proper image interpretation requires skill, limiting its utility in remote applications for conditions such as pneumothorax (PTX) which requires rapid intervention. Artificial intelligence has the potential to automate ultrasound image analysis for various pathophysiological conditions. Training models require large data sets and a means of troubleshooting in real-time for ultrasound integration deployment, and they also require large animal models or clinical testing. Here, we detail the development of a dynamic synthetic tissue phantom model for PTX and its use in training image classification algorithms. The model comprises a synthetic gelatin phantom cast in a custom 3D-printed rib mold and a lung mimicking phantom. When compared to PTX images acquired in swine, images from the phantom were similar in both PTX negative and positive mimicking scenarios. We then used a deep learning image classification algorithm, which we previously developed for shrapnel detection, to accurately predict the presence of PTX in swine images by only training on phantom image sets, highlighting the utility for a tissue phantom for AI applications.Ultrasound (US) imaging is a critical tool in emergency and military medicine because of its portability and immediate nature. However, proper image interpretation requires skill, limiting its utility in remote applications for conditions such as pneumothorax (PTX) which requires rapid intervention. Artificial intelligence has the potential to automate ultrasound image analysis for various pathophysiological conditions. Training models require large data sets and a means of troubleshooting in real-time for ultrasound integration deployment, and they also require large animal models or clinical testing. Here, we detail the development of a dynamic synthetic tissue phantom model for PTX and its use in training image classification algorithms. The model comprises a synthetic gelatin phantom cast in a custom 3D-printed rib mold and a lung mimicking phantom. When compared to PTX images acquired in swine, images from the phantom were similar in both PTX negative and positive mimicking scenarios. We then used a deep learning image classification algorithm, which we previously developed for shrapnel detection, to accurately predict the presence of PTX in swine images by only training on phantom image sets, highlighting the utility for a tissue phantom for AI applications.
Author	Snider, Eric J. Knowlton, Zechariah J. Avital, Guy Boice, Emily N. Hernandez Torres, Sofia I. Gonzalez, Jose M. Berard, David
AuthorAffiliation	1 U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA 3 Division of Anesthesia, Intensive Care & Pain Management, Tel-Aviv Sourasky Medical Center, Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv 64239, Israel 2 Trauma & Combat Medicine Branch, Surgeon General’s Headquarters, Israel Defense Forces, Ramat-Gan 52620, Israel
AuthorAffiliation_xml	– name: 3 Division of Anesthesia, Intensive Care & Pain Management, Tel-Aviv Sourasky Medical Center, Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv 64239, Israel – name: 1 U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA – name: 2 Trauma & Combat Medicine Branch, Surgeon General’s Headquarters, Israel Defense Forces, Ramat-Gan 52620, Israel
Author_xml	– sequence: 1 givenname: Emily N. orcidid: 0000-0001-7180-2842 surname: Boice fullname: Boice, Emily N. – sequence: 2 givenname: Sofia I. orcidid: 0000-0002-0764-519X surname: Hernandez Torres fullname: Hernandez Torres, Sofia I. – sequence: 3 givenname: Zechariah J. surname: Knowlton fullname: Knowlton, Zechariah J. – sequence: 4 givenname: David orcidid: 0000-0003-2286-3846 surname: Berard fullname: Berard, David – sequence: 5 givenname: Jose M. orcidid: 0000-0002-4325-409X surname: Gonzalez fullname: Gonzalez, Jose M. – sequence: 6 givenname: Guy orcidid: 0000-0002-9337-185X surname: Avital fullname: Avital, Guy – sequence: 7 givenname: Eric J. orcidid: 0000-0002-0293-4937 surname: Snider fullname: Snider, Eric J.
BackLink	https://www.osti.gov/biblio/1886975$$D View this record in Osti.gov
BookMark	eNp1kl-LGyEUxYeypbvd7nNfh_alL-nqqDP6Ugjpv0CgC02gb-LonYxhRlN1lu4n6NeuSZbSDfRBFO_vHK-H-7K4cN5BUbzG6D0hAt3u7Ki21m05Eqii4llxVRFMZpSQHxf_nC-Lmxh3CCEsqrzEi-KS1JgwiulV8XsdlHXZpNwMKajoJ2fKZfaFcjGoGG1ntUrWu_IjwH62AhWO9HzY-mBTP8ay86G8czCNPvU-qF-ZTKCPmk08sKr8_uBSD8nqcm1jnKC865VLfizn-70KKk3xVfG8U0OEm8f9uth8_rRefJ2tvn1ZLuarmaYCp5nusEKIUQaCtXVL2k40CutcNB1RXIuaQ6N5RRvVNA1HwE2rESAjGGetAHJdLE--xqud3IccYXiQXll5vPBhK1XIjQ4gTSvqShhGTGVoA6pVLSgE1HTMUMAie304ee2ndgSjweUEhyemTyvO9nLr76VgqKrFweDNycDHZGXUNufWa-9cjk9izmvRsAy9e3wl-J8TxCRHGzUMg3LgpyirBteCEi54Rt-eoTs_BZfzPFKMEYxopm5PlA4-xgDd344xkofBkmeDlRXsTJFbPU5F_pYd_qv7A7Lq2Ok
CitedBy_id	crossref_primary_10_1016_j_bspc_2023_105183 crossref_primary_10_1177_10806032241304441 crossref_primary_10_1038_s41598_024_55480_0 crossref_primary_10_1108_IJRDM_12_2024_0666 crossref_primary_10_3390_diagnostics14030335 crossref_primary_10_1007_s13665_024_00344_1 crossref_primary_10_3389_fphys_2025_1530808 crossref_primary_10_1016_j_techfore_2023_123149 crossref_primary_10_3390_diagnostics13030417 crossref_primary_10_3390_bioengineering11040392 crossref_primary_10_3390_jimaging8090252 crossref_primary_10_3389_fbioe_2023_1244616 crossref_primary_10_3390_s24248204 crossref_primary_10_3390_jimaging8100270
Cites_doi	10.1002/mrd.22489 10.1016/j.eng.2018.11.020 10.1016/S0196-0644(01)70030-3 10.1016/j.annemergmed.2008.12.013 10.1109/JPROC.2015.2494218 10.1371/journal.pone.0255886 10.1109/CVPR.2018.00474 10.1038/sj.sc.3101889 10.1002/jum.14629 10.1007/978-3-030-33128-3_3 10.1111/j.1553-2712.2012.01349.x 10.1007/s00330-019-06130-x 10.1109/TUFFC.2020.2993779 10.1002/jcu.1870170617 10.1109/TUFFC.2020.3002249 10.1186/s13054-016-1399-x 10.1213/ANE.0b013e3181d5e4d8 10.1109/CVPR.2016.90 10.3390/jimaging8050140 10.1002/acm2.13695 10.1186/cc5004 10.1007/s11739-010-0347-z 10.1378/chest.11-0131 10.1016/j.compbiomed.2021.104742 10.1016/j.chest.2017.10.019 10.1088/1361-6560/aa82ec 10.3390/app11020672 10.1016/j.rapm.2005.08.007 10.1038/nmeth.2019 10.1038/s41598-022-12367-2 10.1177/1129729820961941 10.4103/0974-2700.93116 10.1053/j.jvca.2019.11.051 10.1136/emermed-2011-200264
ContentType	Journal Article
Copyright	2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2022 by the authors. 2022
Copyright_xml	– notice: 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2022 by the authors. 2022
DBID	AAYXX CITATION 8FE 8FG ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ CCPQU COVID DWQXO HCIFZ P5Z P62 PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS 7X8 OTOTI 5PM DOA
DOI	10.3390/jimaging8090249
DatabaseName	CrossRef ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials ProQuest Central Technology collection ProQuest One Community College Coronavirus Research Database ProQuest Central SciTech Premium Collection Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic OSTI.GOV PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef Publicly Available Content Database Advanced Technologies & Aerospace Collection Technology Collection ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest One Academic Eastern Edition Coronavirus Research Database ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central China ProQuest Central Advanced Technologies & Aerospace Database ProQuest One Applied & Life Sciences ProQuest One Academic UKI Edition ProQuest Central Korea ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) MEDLINE - Academic
DatabaseTitleList	CrossRef MEDLINE - Academic Publicly Available Content Database
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	2313-433X
ExternalDocumentID	oai_doaj_org_article_db9629d53d2d47eababea0e4df5d4e19 PMC9502699 1886975 10_3390_jimaging8090249
GeographicLocations	United States--US
GeographicLocations_xml	– name: United States--US
GrantInformation_xml	– fundername: U.S. Army Medical Research and Development Command – fundername: National Institutes of Health (NIH) – fundername: U.S. Department of Energy, Oak Ridge Institute for Science and Education
GroupedDBID	5VS 8FE 8FG AADQD AAFWJ AAYXX ADBBV ADMLS AFFHD AFKRA AFPKN AFZYC ALMA_UNASSIGNED_HOLDINGS ARAPS ARCSS BCNDV BENPR BGLVJ CCPQU CITATION GROUPED_DOAJ HCIFZ IAO IHR ITC KQ8 MODMG M~E OK1 P62 PGMZT PHGZM PHGZT PIMPY PQGLB PROAC RPM ABUWG AZQEC COVID DWQXO PKEHL PQEST PQQKQ PQUKI PRINS PUEGO 7X8 DKF OTOTI 5PM
ID	FETCH-LOGICAL-c491t-cf1a00545e95b6b3bf97a1cc49df3a8c968e7c8247a77780e8dbc0e0d9585b9e3
IEDL.DBID	DOA
ISICitedReferencesCount	16
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000856388900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2313-433X
IngestDate	Fri Oct 03 12:40:00 EDT 2025 Tue Nov 04 02:07:16 EST 2025 Mon Sep 19 16:16:41 EDT 2022 Wed Oct 01 13:40:07 EDT 2025 Sun Sep 07 03:46:34 EDT 2025 Sat Nov 29 07:12:25 EST 2025 Tue Nov 18 22:23:29 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	9
Language	English
License	Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c491t-cf1a00545e95b6b3bf97a1cc49df3a8c968e7c8247a77780e8dbc0e0d9585b9e3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 USDOE
ORCID	0000-0002-0764-519X 0000-0003-2286-3846 0000-0002-4325-409X 0000-0002-0293-4937 0000-0001-7180-2842 0000-0002-9337-185X 0000000322863846 0000000171802842 000000029337185X 0000000202934937 000000024325409X
OpenAccessLink	https://doaj.org/article/db9629d53d2d47eababea0e4df5d4e19
PMID	36135414
PQID	2716553104
PQPubID	2059558
ParticipantIDs	doaj_primary_oai_doaj_org_article_db9629d53d2d47eababea0e4df5d4e19 pubmedcentral_primary_oai_pubmedcentral_nih_gov_9502699 osti_scitechconnect_1886975 proquest_miscellaneous_2716943898 proquest_journals_2716553104 crossref_primary_10_3390_jimaging8090249 crossref_citationtrail_10_3390_jimaging8090249
PublicationCentury	2000
PublicationDate	20220911
PublicationDateYYYYMMDD	2022-09-11
PublicationDate_xml	– month: 9 year: 2022 text: 20220911 day: 11
PublicationDecade	2020
PublicationPlace	Basel
PublicationPlace_xml	– name: Basel – name: Switzerland
PublicationTitle	Journal of imaging
PublicationYear	2022
Publisher	MDPI AG MDPI
Publisher_xml	– name: MDPI AG – name: MDPI
References	Alakwaa (ref_12) 2017; 8 Barillari (ref_23) 2010; 5 Shahriari (ref_42) 2016; 104 Prada (ref_36) 2021; 35 Avila (ref_8) 2018; 37 Oveland (ref_24) 2012; 19 Park (ref_11) 2019; 29 Xu (ref_18) 2005; 30 Schindelin (ref_26) 2012; 9 Husain (ref_29) 2012; 5 ref_31 Singh (ref_35) 2018; 153 Nair (ref_14) 2020; 67 Snider (ref_27) 2022; 12 Lo (ref_19) 2012; 29 Lloyd (ref_37) 2006; 44 ref_39 ref_16 Kim (ref_38) 2010; 110 ref_15 Han (ref_13) 2017; 62 Baloescu (ref_32) 2020; 67 Zhang (ref_9) 2006; 10 Selame (ref_21) 2021; 22 Lin (ref_34) 2022; 23 Whitson (ref_3) 2016; 20 Ebrahimi (ref_7) 2014; 13 ref_22 Liu (ref_17) 2019; 5 Secco (ref_33) 2021; 136 ref_43 Kumar (ref_30) 2020; 43 ref_41 ref_40 ref_1 Mp (ref_20) 1989; 17 ref_2 ref_28 Schindelin (ref_25) 2015; 82 ref_5 ref_4 Alrajhi (ref_6) 2012; 141 Lee (ref_10) 2020; Volume 1213
References_xml	– volume: 82 start-page: 518 year: 2015 ident: ref_25 article-title: The ImageJ Ecosystem: An Open Platform for Biomedical Image Analysis publication-title: Mol. Reprod. Dev. doi: 10.1002/mrd.22489 – volume: 43 start-page: 114 year: 2020 ident: ref_30 article-title: Automated Deep Transfer Learning-Based Approach for Detection of COVID-19 Infection in Chest X-Rays publication-title: IRBM – volume: 5 start-page: 261 year: 2019 ident: ref_17 article-title: Deep Learning in Medical Ultrasound Analysis: A Review publication-title: Engineering doi: 10.1016/j.eng.2018.11.020 – ident: ref_4 doi: 10.1016/S0196-0644(01)70030-3 – ident: ref_5 doi: 10.1016/j.annemergmed.2008.12.013 – volume: 104 start-page: 148 year: 2016 ident: ref_42 article-title: Taking the Human Out of the Loop: A Review of Bayesian Optimization publication-title: Proc. IEEE doi: 10.1109/JPROC.2015.2494218 – ident: ref_15 doi: 10.1371/journal.pone.0255886 – ident: ref_41 doi: 10.1109/CVPR.2018.00474 – volume: 44 start-page: 505 year: 2006 ident: ref_37 article-title: Diaphragmatic Paralysis: The Use of M Mode Ultrasound for Diagnosis in Adults publication-title: Spinal Cord doi: 10.1038/sj.sc.3101889 – volume: 37 start-page: 2681 year: 2018 ident: ref_8 article-title: Does the Addition of M-Mode to B-Mode Ultrasound Increase the Accuracy of Identification of Lung Sliding in Traumatic Pneumothoraces? publication-title: J. Ultrasound Med. doi: 10.1002/jum.14629 – volume: Volume 1213 start-page: 47 year: 2020 ident: ref_10 article-title: Deep Learning for Pulmonary Image Analysis: Classification, Detection, and Segmentation publication-title: Deep Learning in Medical Image Analysis doi: 10.1007/978-3-030-33128-3_3 – ident: ref_16 – volume: 19 start-page: 586 year: 2012 ident: ref_24 article-title: A Porcine Pneumothorax Model for Teaching Ultrasound Diagnostics publication-title: Acad. Emerg. Med. doi: 10.1111/j.1553-2712.2012.01349.x – volume: 29 start-page: 5341 year: 2019 ident: ref_11 article-title: Application of Deep Learning–Based Computer-Aided Detection System: Detecting Pneumothorax on Chest Radiograph after Biopsy publication-title: Eur. Radiol. doi: 10.1007/s00330-019-06130-x – ident: ref_40 – volume: 67 start-page: 2493 year: 2020 ident: ref_14 article-title: Deep Learning to Obtain Simultaneous Image and Segmentation Outputs From a Single Input of Raw Ultrasound Channel Data publication-title: IEEE Trans. Ultrason. Ferroelectr. Freq. Control. doi: 10.1109/TUFFC.2020.2993779 – volume: 17 start-page: 456 year: 1989 ident: ref_20 article-title: Preparation of a Homemade Ultrasound Biopsy Phantom publication-title: J. Clin. Ultrasound: JCU doi: 10.1002/jcu.1870170617 – volume: 67 start-page: 2312 year: 2020 ident: ref_32 article-title: Automated Lung Ultrasound B-Line Assessment Using a Deep Learning Algorithm publication-title: IEEE Trans. Ultrason. Ferroelectr. Freq. Control. doi: 10.1109/TUFFC.2020.3002249 – volume: 20 start-page: 227 year: 2016 ident: ref_3 article-title: Ultrasonography in the Emergency Department publication-title: Crit. Care doi: 10.1186/s13054-016-1399-x – volume: 110 start-page: 1349 year: 2010 ident: ref_38 article-title: An Evaluation of Diaphragmatic Movement by M-Mode Sonography as a Predictor of Pulmonary Dysfunction after Upper Abdominal Surgery publication-title: Anesth. Analg. doi: 10.1213/ANE.0b013e3181d5e4d8 – ident: ref_1 – ident: ref_39 doi: 10.1109/CVPR.2016.90 – ident: ref_28 doi: 10.3390/jimaging8050140 – volume: 23 start-page: e13695 year: 2022 ident: ref_34 article-title: Deep Learning for Emergency Ascites Diagnosis Using Ultrasonography Images publication-title: J. Appl. Clin. Med. Phys. doi: 10.1002/acm2.13695 – volume: 10 start-page: R112 year: 2006 ident: ref_9 article-title: Rapid Detection of Pneumothorax by Ultrasonography in Patients with Multiple Trauma publication-title: Crit. Care doi: 10.1186/cc5004 – volume: 5 start-page: 253 year: 2010 ident: ref_23 article-title: Detection of Spontaneous Pneumothorax with Chest Ultrasound in the Emergency Department publication-title: Intern. Emerg. Med. doi: 10.1007/s11739-010-0347-z – volume: 141 start-page: 703 year: 2012 ident: ref_6 article-title: Test Characteristics of Ultrasonography for the Detection of Pneumothorax: A Systematic Review and Meta-Analysis publication-title: Chest doi: 10.1378/chest.11-0131 – volume: 136 start-page: 104742 year: 2021 ident: ref_33 article-title: Deep Learning and Lung Ultrasound for COVID-19 Pneumonia Detection and Severity Classification publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2021.104742 – ident: ref_2 – volume: 153 start-page: 689 year: 2018 ident: ref_35 article-title: The Use of M-Mode Ultrasonography to Differentiate the Causes of B Lines publication-title: Chest doi: 10.1016/j.chest.2017.10.019 – volume: 8 start-page: 409 year: 2017 ident: ref_12 article-title: Lung Cancer Detection and Classification with 3D Convolutional Neural Network (3D-CNN) publication-title: Int. J. Adv. Comput. Sci. Appl. – volume: 62 start-page: 7714 year: 2017 ident: ref_13 article-title: A Deep Learning Framework for Supporting the Classification of Breast Lesions in Ultrasound Images publication-title: Phys. Med. Biol. doi: 10.1088/1361-6560/aa82ec – ident: ref_31 doi: 10.3390/app11020672 – volume: 30 start-page: 593 year: 2005 ident: ref_18 article-title: Ultrasound Phantom for Hands-on Practice publication-title: Reg. Anesth. Pain Med. doi: 10.1016/j.rapm.2005.08.007 – volume: 9 start-page: 676 year: 2012 ident: ref_26 article-title: Fiji: An Open-Source Platform for Biological-Image Analysis publication-title: Nat. Methods doi: 10.1038/nmeth.2019 – volume: 12 start-page: 8427 year: 2022 ident: ref_27 article-title: An Image Classification Deep-Learning Algorithm for Shrapnel Detection from Ultrasound Images publication-title: Sci. Rep. doi: 10.1038/s41598-022-12367-2 – volume: 13 start-page: 29 year: 2014 ident: ref_7 article-title: Diagnostic Accuracy of Chest Ultrasonography versus Chest Radiography for Identification of Pneumothorax: A Systematic Review and Meta-Analysis publication-title: Tanaffos – ident: ref_43 – volume: 22 start-page: 891 year: 2021 ident: ref_21 article-title: A Comparison of Homemade Vascular Access Ultrasound Phantom Models for Peripheral Intravenous Catheter Insertion publication-title: J. Vasc. Access doi: 10.1177/1129729820961941 – volume: 5 start-page: 76 year: 2012 ident: ref_29 article-title: Sonographic Diagnosis of Pneumothorax publication-title: J. Emerg. Trauma Shock doi: 10.4103/0974-2700.93116 – ident: ref_22 – volume: 35 start-page: 310 year: 2021 ident: ref_36 article-title: Tracheal, Lung, and Diaphragmatic Applications of M-Mode Ultrasonography in Anesthesiology and Critical Care publication-title: J. Cardiothorac. Vasc. Anesth. doi: 10.1053/j.jvca.2019.11.051 – volume: 29 start-page: 738 year: 2012 ident: ref_19 article-title: Homemade Ultrasound Phantom for Teaching Identification of Superficial Soft Tissue Abscess publication-title: Emerg. Med. J. doi: 10.1136/emermed-2011-200264
SSID	ssj0001920199
Score	2.2993798
Snippet	Ultrasound (US) imaging is a critical tool in emergency and military medicine because of its portability and immediate nature. However, proper image...
SourceID	doaj pubmedcentral osti proquest crossref
SourceType	Open Website Open Access Repository Aggregation Database Enrichment Source Index Database
StartPage	249
SubjectTerms	Algorithms Artificial intelligence automation Classification Datasets Deep learning Emergency medical care Emergency medical services Gelatin Image acquisition Image analysis Image classification Lungs Machine learning model development Pneumothorax porcine Respiration Shrapnel Swine Three dimensional printing tissue phantom Training Trouble shooting Troubleshooting Ultrasonic imaging ultrasound X-rays
SummonAdditionalLinks	– databaseName: Advanced Technologies & Aerospace Database dbid: P5Z link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lj9MwELZg4QAH3ivCLshIHLiEzcNuPCe0PFYgoVUlutKKS-RX2qI2LU2K4Bfwt5lx026DBBdOkeKJ4mg-2zP25PsYe2Eqr6zPMcmRFmKhvIq1gDwmLnTnU9DaB3b9T8X5ubq8hGG34dZ0ZZXbOTFM1G5haY_8JMPAXiJgEvF6-S0m1Sg6Xe0kNK6zG8SSQNINQ_nlao8FcHkD2DD65Jjdn3ydzoP4j6JyROLP3FuMAmc_XhY4tnrxZr9acm_5Obv7vx2_x-50gSc_3SDlPrvm6wfs9h4d4UP2a9TpRfCLGfahIcUl_hE_xfOgnUlVRcGR_J33y7ijZh3z09kYX9hO5g3HEJgPa78mACC4fqBlG4q9ah6KE7jmn3_WGHRiL_goOJ0PJyRkPOcYEBMP-bp5xC7O3o_efog7oYbYCkjb2FappthPepBmYHJTQaFTi42uyrWyMFC-sCoThS6KQiVeOWMTnzjAZMWAzw_ZQb2o_WPGtTRCgpLgRCXAJCZPqqSqfCWUzbUeROzV1mOl7VjMSUxjVmI2Qy4u_3BxxF7uHlhuCDz-bvqGILAzI-btcGOxGpfdQC6dgUEGTuYuc6Lw2mjjdeKFq6QTCO6IHRGASgxgiIXXUrmSbctUqQEUMmLHW5CU3WTRlFcIidjzXTMOczq70bVfrDc2QEr1KmJFD4-97vZb6ukkEIaDxEwb4Mm_X37EbmX0bwfpY6TH7KBdrf1TdtN-b6fN6lkYWb8BtqA1tg priority: 102 providerName: ProQuest
Title	Training Ultrasound Image Classification Deep-Learning Algorithms for Pneumothorax Detection Using a Synthetic Tissue Phantom Apparatus
URI	https://www.proquest.com/docview/2716553104 https://www.proquest.com/docview/2716943898 https://www.osti.gov/biblio/1886975 https://pubmed.ncbi.nlm.nih.gov/PMC9502699 https://doaj.org/article/db9629d53d2d47eababea0e4df5d4e19
Volume	8
WOSCitedRecordID	wos000856388900001&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: 2313-433X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001920199 issn: 2313-433X databaseCode: DOA dateStart: 20150101 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: 2313-433X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001920199 issn: 2313-433X databaseCode: M~E dateStart: 20150101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 2313-433X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001920199 issn: 2313-433X databaseCode: P5Z dateStart: 20151201 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2313-433X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001920199 issn: 2313-433X databaseCode: BENPR dateStart: 20151201 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2313-433X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001920199 issn: 2313-433X databaseCode: PIMPY dateStart: 20151201 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9NAEF5B4QAHxFOYlmiROHAx9WM3u3NsoRWVILIglQoXa3c9blIlTpU4CH4Bf5vZtRslSIgLB9vS7tgae2a8M_L4-xh7bWvUDnMqcqSDWGjUsRGQxx4LvcIUjMGArv9RjUb64gKKLaov3xPWwQN3D-6wsjDMoJJ5lVVCobHGoklQVLWsBAbAzyxRsFVMXXV5C23QYfnkVNcfXk3ngfZH-0ZEj5y5tQwFtH46LCiqdjLN3T7JrYXn9CF70GeM_KjT9BG7hc1jdn8LR_AJ-zXuiR74-YwusfJUSfyMNEEeSC99O1CwAH-PeB33mKqX_Gh2uVhO28l8xSl35UWDa2858oofJNmGLq2Gh64CbviXnw1li6QFHwdr8WLiGYjnnDJZDyC-Xj1l56cn43cf4p5hIXYC0jZ2dWp80iYRpB3a3NagTOposqpzox0MNSqnM6GMUkonqCvrEkwqoCrDAubP2F6zaPA540ZaIUFLqEQtwCY2T-qkrrEW2uXGDCP29uaBl66HH_csGLOSyhBvofIPC0XszeaE6w554--ix96CGzEPmR0GyJHK3pHKfzlSxPa9_UvKPDx8rvN9Rq4tU62HoGTEDm7couyjfFVmVGxKeoklImKvNtMUn_6ji2lwse5kwFPM64ipHXfaUXd3pplOAtI3SCqRAV78j_vbZ_cy_-uGp79ID9heu1zjS3bXfW-nq-WA3VYXesDuHJ-Mis-DEEy0L-Q3GivOPhVffwMkFC7U
linkProvider	Directory of Open Access Journals
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1db9MwFLVGhwQ88I0IG2AkkHgJy4eT-D4gNBjTqnVVJTppPGWO7bRFbVqaFtgv4N_wG7k3TbsWCd72wFOk2m0c99zr4-TmHMZeZrmV2oa4yYk0uEJa6SoBoUta6Mb6oJSt1PVbSbstz86gs8V-Ld-FobLKZU6sErUZa7pHvhcgsY8QMJ54N_nqkmsUPV1dWmgsYHFsL77jlq182zzA__dVEBx-7H44cmtXAVcL8Geuzn1FRCWyEGVxFmY5JMrX2GjyUEkNsbSJloFIVJIk0rPSZNqzngFk1hnYEH_3GtsWBPYG2-40TzqfL-_qAC6oAAsNoTAEb-_LYFTZDUkqgCTFzrXlr3IJwMMYo3mD4W7WZ64teId3_repustu19Sa7y9i4R7bssV9dmtNcPEB-9mtHTH46RCvuSRPKd7EqbO8cgeluqkKqvzA2olbi8_2-P6whxc4649KjiSfdwo7J4hj-PzAnrOqnK3gVfkFV_zTRYG0GkfBuxWseadPVs0jjpSflNbn5UN2eiUz8Yg1inFhHzOuokxEICMwIheQeVno5V6e21xIHSoVO-zNEiGprnXayS5kmOJ-jSCV_gEph71efWGykCj5e9f3BLlVN9IWrz4YT3tpnapSk0EcgIlCExiRWJWpzCrPCpNHRmD4OmyHAJsiRSOdYU0FWXqW-lLGkEQO212CMq3TYZleItJhL1bNmMjo6ZQq7Hi-6AMC-bN0WLKB_43hbrYUg34liQ6RF8QAT_598ufsxlH3pJW2mu3jHXYzoDdZyA3E32WN2XRun7Lr-ttsUE6f1XHN2flVx8dvjK-VPQ
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3LbtNAFB2VghAseCNMCwwSSGxM_Jix5y4QKoSIqFUUiVaq2LjzchKUOCF2gH4B_8TXccdx0gQJdl2wipSZJPbk3Dvn2tfnEPJC5VZoG2ORwzX4TFjhSwax77TQjQ1BSlur6x-lvZ44PYX-Dvm1ehbGtVWucmKdqM1Uu2vkrQiJPUfABKyVN20R_Xbn7eyr7xyk3J3WlZ3GEiKH9vw7lm_lm24b_-uXUdT5cPz-o984DPiaQVj5Og-lIy3cAleJilUOqQw1Dpo8lkJDImyqRcRSmaapCKwwSgc2MIAsW4GN8XuvkKsp1piunbDPP19c3wHcWgGWakJxDEHry2hSGw8J1wrptDs3NsLaLwBfphjXW1x3u1NzY-vr3P6fF-0OudUQbnqwjJC7ZMcW98jNDRnG--TnceOTQU_GeP6lc5qiXVxGS2vPUNdNVQOYtq2d-Y0k7YAejAd4gtVwUlKk_rRf2IUDPgbVD5xZ1U1uBa2bMqikn84LJNt4FPS4BjvtD52B84RiIeD01xflA3JyKSvxkOwW08I-IlRyxTgIDoblDFSg4iAP8tzmTOhYysQjr1doyXSj3u5MRMYZVnEOXtkf8PLIq_UHZkvhkr9Pfefgt57mFMfrN6bzQdYksMwoSCIwPDaRYamVSiorA8tMzg3DoPbIngNvhsTNqQ9r16alqywUIoGUe2R_BdCsSZJldoFOjzxfD2N6c_esZGGni-UcYMiqhUfSrVjYOtztkWI0rIXSgQdRAvD43z_-jFzHoMiOur3DPXIjco-3OIuQcJ_sVvOFfUKu6W_VqJw_rQOckrPLDo7fph6coA
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=Training+Ultrasound+Image+Classification+Deep-Learning+Algorithms+for+Pneumothorax+Detection+Using+a+Synthetic+Tissue+Phantom+Apparatus&rft.jtitle=Journal+of+imaging&rft.au=Boice%2C+Emily+N.&rft.au=Hernandez+Torres%2C+Sofia+I.&rft.au=Knowlton%2C+Zechariah+J.&rft.au=Berard%2C+David&rft.date=2022-09-11&rft.pub=MDPI+AG&rft.issn=2313-433X&rft.eissn=2313-433X&rft.volume=8&rft.issue=9&rft_id=info:doi/10.3390%2Fjimaging8090249&rft.externalDocID=1886975
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2313-433X&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2313-433X&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2313-433X&client=summon