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Arzamasov K, Vasilev Y, Zelenova M, Pestrenin L, Busygina Y, Bobrovskaya T, Chetverikov S, Shikhmuradov D, Pankratov A, Kirpichev Y, Sinitsyn V, Son I, Omelyanskaya O. Independent evaluation of the accuracy of 5 artificial intelligence software for detecting lung nodules on chest X-rays. Quant Imaging Med Surg 2024; 14:5288-5303. [PMID: 39144030 PMCID: PMC11320553 DOI: 10.21037/qims-24-160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 06/11/2024] [Indexed: 08/16/2024]
Abstract
Background The integration of artificial intelligence (AI) into medicine is growing, with some experts predicting its standalone use soon. However, skepticism remains due to limited positive outcomes from independent validations. This research evaluates AI software's effectiveness in analyzing chest X-rays (CXR) to identify lung nodules, a possible lung cancer indicator. Methods This retrospective study analyzed 7,670,212 record pairs from radiological exams conducted between 2020 and 2022 during the Moscow Computer Vision Experiment, focusing on CXR and computed tomography (CT) scans. All images were acquired during clinical routine. The final dataset comprised 100 CXR images (50 with lung nodules, 50 without), selected consecutively and based on inclusion and exclusion criteria, to evaluate the performance of all five AI-based solutions, participating in the Moscow Computer Vision Experiment and analyzing CXR. The evaluation was performed in 3 stages. In the first stage, the probability of a nodule in the lung obtained from AI services was compared with the Ground Truth (1-there is a nodule, 0-there is no nodule). In the second stage, 3 radiologists evaluated the segmentation of nodules performed by the AI services (1-nodule correctly segmented, 0-nodule incorrectly segmented or not segmented at all). In the third stage, the same radiologists additionally evaluated the classification of the nodules (1-nodule correctly segmented and classified, 0-all other cases). The results obtained in stages 2 and 3 were compared with Ground Truth, which was common to all three stages. For each stage, diagnostic accuracy metrics were calculated for each AI service. Results Three software solutions (Celsus, Lunit INSIGHT CXR, and qXR) demonstrated diagnostic metrics that matched or surpassed the vendor specifications, and achieved the highest area under the receiver operating characteristic curve (AUC) of 0.956 [95% confidence interval (CI): 0.918 to 0.994]. However, when evaluated by three radiologists for accurate nodule segmentation and classification, all solutions performed below the vendor-declared metrics, with the highest AUC reaching 0.812 (95% CI: 0.744 to 0.879). Meanwhile, all AI services demonstrated 100% specificity at stages 2 and 3 of the study. Conclusions To ensure the reliability and applicability of AI-based software, it is crucial to validate performance metrics using high-quality datasets and engage radiologists in the evaluation process. Developers are recommended to improve the accuracy of the underlying models before allowing the standalone use of the software for lung nodule detection. The dataset created during the study may be accessed at https://mosmed.ai/datasets/mosmeddatargogksnalichiemiotsutstviemlegochnihuzlovtipvii/.
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Affiliation(s)
- Kirill Arzamasov
- State Budget-Funded Health Care Institution of the City of Moscow “Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department”, Moscow, Russian Federation
- MIREA – Russian Technological University, Moscow, Russian Federation
| | - Yuriy Vasilev
- State Budget-Funded Health Care Institution of the City of Moscow “Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department”, Moscow, Russian Federation
- Federal State Budgetary Institution “National Medical and Surgical Center named after N.I. Pirogov” of the Ministry of Health of the Russian Federation, Moscow, Russian Federation
| | - Maria Zelenova
- State Budget-Funded Health Care Institution of the City of Moscow “Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department”, Moscow, Russian Federation
| | - Lev Pestrenin
- State Budget-Funded Health Care Institution of the City of Moscow “Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department”, Moscow, Russian Federation
| | - Yulia Busygina
- State Budget-Funded Health Care Institution of the City of Moscow “Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department”, Moscow, Russian Federation
| | - Tatiana Bobrovskaya
- State Budget-Funded Health Care Institution of the City of Moscow “Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department”, Moscow, Russian Federation
| | - Sergey Chetverikov
- State Budget-Funded Health Care Institution of the City of Moscow “Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department”, Moscow, Russian Federation
| | - David Shikhmuradov
- State Budget-Funded Health Care Institution of the City of Moscow “Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department”, Moscow, Russian Federation
| | - Andrey Pankratov
- State Budget-Funded Health Care Institution of the City of Moscow “Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department”, Moscow, Russian Federation
| | - Yury Kirpichev
- State Budget-Funded Health Care Institution of the City of Moscow “Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department”, Moscow, Russian Federation
| | - Valentin Sinitsyn
- State Budget-Funded Health Care Institution of the City of Moscow “Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department”, Moscow, Russian Federation
- Lomonosov Moscow State University, Moscow, Russian Federation
| | - Irina Son
- Federal State Budgetary Educational Institution of Further Professional Education “Russian Medical Academy of Continuous Professional Education” of the Ministry of Healthcare of the Russian Federation, Moscow, Russian Federation
| | - Olga Omelyanskaya
- State Budget-Funded Health Care Institution of the City of Moscow “Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department”, Moscow, Russian Federation
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Kantipudi K, Gu J, Bui V, Yu H, Jaeger S, Yaniv Z. Automated Pulmonary Tuberculosis Severity Assessment on Chest X-rays. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01052-7. [PMID: 38587769 DOI: 10.1007/s10278-024-01052-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 01/18/2024] [Accepted: 02/12/2024] [Indexed: 04/09/2024]
Abstract
According to the 2022 World Health Organization's Global Tuberculosis (TB) report, an estimated 10.6 million people fell ill with TB, and 1.6 million died from the disease in 2021. In addition, 2021 saw a reversal of a decades-long trend of declining TB infections and deaths, with an estimated increase of 4.5% in the number of people who fell ill with TB compared to 2020, and an estimated yearly increase of 450,000 cases of drug resistant TB. Estimating the severity of pulmonary TB using frontal chest X-rays (CXR) can enable better resource allocation in resource constrained settings and monitoring of treatment response, enabling prompt treatment modifications if disease severity does not decrease over time. The Timika score is a clinically used TB severity score based on a CXR reading. This work proposes and evaluates three deep learning-based approaches for predicting the Timika score with varying levels of explainability. The first approach uses two deep learning-based models, one to explicitly detect lesion regions using YOLOV5n and another to predict the presence of cavitation using DenseNet121, which are then utilized in score calculation. The second approach uses a DenseNet121-based regression model to directly predict the affected lung percentage and another to predict cavitation presence using a DenseNet121-based classification model. Finally, the third approach directly predicts the Timika score using a DenseNet121-based regression model. The best performance is achieved by the second approach with a mean absolute error of 13-14% and a Pearson correlation of 0.7-0.84 using three held-out datasets for evaluating generalization.
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Affiliation(s)
- Karthik Kantipudi
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, Bethesda, 20892, MD, USA.
| | - Jingwen Gu
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, Bethesda, 20892, MD, USA
| | - Vy Bui
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, 20894, MD, USA
| | - Hang Yu
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, 20894, MD, USA
| | - Stefan Jaeger
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, 20894, MD, USA
| | - Ziv Yaniv
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, Bethesda, 20892, MD, USA.
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Chandramohan A, Krothapalli V, Augustin A, Kandagaddala M, Thomas HM, Sudarsanam TD, Jagirdar A, Govil S, Kalyanpur A. Teleradiology and technology innovations in radiology: status in India and its role in increasing access to primary health care. THE LANCET REGIONAL HEALTH. SOUTHEAST ASIA 2024; 23:100195. [PMID: 38404514 PMCID: PMC10884973 DOI: 10.1016/j.lansea.2023.100195] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 02/19/2023] [Accepted: 03/27/2023] [Indexed: 02/27/2024]
Abstract
Background There is an inequitable distribution of radiology facilities in India. This scoping review aimed at mapping the available technology instruments to improve access to imaging at primary health care; to identify the facilitators and barriers, and the knowledge gaps for widespread adaptation of technology solutions. Methods A search was conducted using broad inclusive terms non-specific to subtypes of medical imaging devices or informatics. Work published in the English language between 2005 and 2022, conducted primarily in India, and with full manuscripts were included. Two authors independently screened the abstracts against the inclusion criteria for full-text review and a senior author settled discrepancies. Data were extracted using DistillerSR software. Findings 43 original articles and 52 non-academic materials were finally reviewed. The data was from 10 Indian states with n = 9 from rural settings. The broad trends in original articles were: connectivity using teleradiology (n = 7), mobile digital imaging units (n = 9), artificial intelligence (n = 16); mobile devices and smartphone applications (n = 7); data security (n = 7) and web-based technology (n = 2); public-private partnership (n = 9); cost (n = 2); concordance (n = 19); evaluation (n = 4); implementation (n = 2). Interpretation Available evidence suggests that teleradiology when combined with AI and mobile digital imaging units can address radiologist shortages; strengthen programs aimed at population screening and emergency care. However, there is insufficient data on the scale of teleradiology networks within India; needs assessment; cost; facilitators, and barriers for implementation of technologies solutions in primary healthcare settings. Regulations governing quality standards, data protection, and confidentiality are unclear. Funding The authors are The Lancet Citizen's Commission fellows. The Lancet Commission has received financial support from the Lakshmi Mittal and Family South Asia Institute, Harvard University; Christian Medical College, Vellore (CMC), Vellore; Azim Premji Foundation, Infosys; Kirloskar Systems Ltd.; Mahindra & Mahindra Ltd.; Rohini Nilekani Philanthropies; and Serum Institute of India. The views expressed are those of the author(s) and not necessarily those of the Lancet Citizens' Commission or its partners.
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Affiliation(s)
| | | | - Ann Augustin
- Department of Radiology, Christian Medical College, Vellore, 632004, India
| | | | | | | | | | - Shalini Govil
- Department of Radiology, Christian Medical College, Vellore, 632004, India
- Naruvi Hospital, Vellore, India
- Pun Hlaing Hospital, Myanmar
| | - Arjun Kalyanpur
- Teleradiology Solutions, Whitefield, Bengaluru, Karnataka, 560048, India
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Hwang EJ, Jeong WG, David PM, Arentz M, Ruhwald M, Yoon SH. AI for Detection of Tuberculosis: Implications for Global Health. Radiol Artif Intell 2024; 6:e230327. [PMID: 38197795 PMCID: PMC10982823 DOI: 10.1148/ryai.230327] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 12/03/2023] [Accepted: 12/18/2023] [Indexed: 01/11/2024]
Abstract
Tuberculosis, which primarily affects developing countries, remains a significant global health concern. Since the 2010s, the role of chest radiography has expanded in tuberculosis triage and screening beyond its traditional complementary role in the diagnosis of tuberculosis. Computer-aided diagnosis (CAD) systems for tuberculosis detection on chest radiographs have recently made substantial progress in diagnostic performance, thanks to deep learning technologies. The current performance of CAD systems for tuberculosis has approximated that of human experts, presenting a potential solution to the shortage of human readers to interpret chest radiographs in low- or middle-income, high-tuberculosis-burden countries. This article provides a critical appraisal of developmental process reporting in extant CAD software for tuberculosis, based on the Checklist for Artificial Intelligence in Medical Imaging. It also explores several considerations to scale up CAD solutions, encompassing manufacturer-independent CAD validation, economic and political aspects, and ethical concerns, as well as the potential for broadening radiography-based diagnosis to other nontuberculosis diseases. Collectively, CAD for tuberculosis will emerge as a representative deep learning application, catalyzing advances in global health and health equity. Keywords: Computer-aided Diagnosis (CAD), Conventional Radiography, Thorax, Lung, Machine Learning Supplemental material is available for this article. © RSNA, 2024.
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Affiliation(s)
- Eui Jin Hwang
- From the Department of Radiology, Seoul National University Hospital
and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu,
Seoul 03080, Korea (E.J.H., S.H.Y.); Department of Radiology, Chonnam National
University Hwasun Hospital, Hwasun, Korea (W.G.J.); Faculty of Pharmacy,
University of Montréal, Montréal, Canada (P.M.D.);
OBVIA–Observatoire sur les Impacts Sociétaux de l'IA et du
Numérique, Québec, Canada (P.M.D.); and FIND–The Global
Alliance for Diagnostics, Geneva, Switzerland (M.A., M.R.)
| | - Won Gi Jeong
- From the Department of Radiology, Seoul National University Hospital
and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu,
Seoul 03080, Korea (E.J.H., S.H.Y.); Department of Radiology, Chonnam National
University Hwasun Hospital, Hwasun, Korea (W.G.J.); Faculty of Pharmacy,
University of Montréal, Montréal, Canada (P.M.D.);
OBVIA–Observatoire sur les Impacts Sociétaux de l'IA et du
Numérique, Québec, Canada (P.M.D.); and FIND–The Global
Alliance for Diagnostics, Geneva, Switzerland (M.A., M.R.)
| | - Pierre-Marie David
- From the Department of Radiology, Seoul National University Hospital
and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu,
Seoul 03080, Korea (E.J.H., S.H.Y.); Department of Radiology, Chonnam National
University Hwasun Hospital, Hwasun, Korea (W.G.J.); Faculty of Pharmacy,
University of Montréal, Montréal, Canada (P.M.D.);
OBVIA–Observatoire sur les Impacts Sociétaux de l'IA et du
Numérique, Québec, Canada (P.M.D.); and FIND–The Global
Alliance for Diagnostics, Geneva, Switzerland (M.A., M.R.)
| | - Matthew Arentz
- From the Department of Radiology, Seoul National University Hospital
and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu,
Seoul 03080, Korea (E.J.H., S.H.Y.); Department of Radiology, Chonnam National
University Hwasun Hospital, Hwasun, Korea (W.G.J.); Faculty of Pharmacy,
University of Montréal, Montréal, Canada (P.M.D.);
OBVIA–Observatoire sur les Impacts Sociétaux de l'IA et du
Numérique, Québec, Canada (P.M.D.); and FIND–The Global
Alliance for Diagnostics, Geneva, Switzerland (M.A., M.R.)
| | - Morten Ruhwald
- From the Department of Radiology, Seoul National University Hospital
and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu,
Seoul 03080, Korea (E.J.H., S.H.Y.); Department of Radiology, Chonnam National
University Hwasun Hospital, Hwasun, Korea (W.G.J.); Faculty of Pharmacy,
University of Montréal, Montréal, Canada (P.M.D.);
OBVIA–Observatoire sur les Impacts Sociétaux de l'IA et du
Numérique, Québec, Canada (P.M.D.); and FIND–The Global
Alliance for Diagnostics, Geneva, Switzerland (M.A., M.R.)
| | - Soon Ho Yoon
- From the Department of Radiology, Seoul National University Hospital
and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu,
Seoul 03080, Korea (E.J.H., S.H.Y.); Department of Radiology, Chonnam National
University Hwasun Hospital, Hwasun, Korea (W.G.J.); Faculty of Pharmacy,
University of Montréal, Montréal, Canada (P.M.D.);
OBVIA–Observatoire sur les Impacts Sociétaux de l'IA et du
Numérique, Québec, Canada (P.M.D.); and FIND–The Global
Alliance for Diagnostics, Geneva, Switzerland (M.A., M.R.)
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Biewer AM, Tzelios C, Tintaya K, Roman B, Hurwitz S, Yuen CM, Mitnick CD, Nardell E, Lecca L, Tierney DB, Nathavitharana RR. Accuracy of digital chest x-ray analysis with artificial intelligence software as a triage and screening tool in hospitalized patients being evaluated for tuberculosis in Lima, Peru. PLOS GLOBAL PUBLIC HEALTH 2024; 4:e0002031. [PMID: 38324610 PMCID: PMC10849246 DOI: 10.1371/journal.pgph.0002031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 01/08/2024] [Indexed: 02/09/2024]
Abstract
Tuberculosis (TB) transmission in healthcare facilities is common in high-incidence countries. Yet, the optimal approach for identifying inpatients who may have TB is unclear. We evaluated the diagnostic accuracy of qXR (Qure.ai, India) computer-aided detection (CAD) software versions 3.0 and 4.0 (v3 and v4) as a triage and screening tool within the FAST (Find cases Actively, Separate safely, and Treat effectively) transmission control strategy. We prospectively enrolled two cohorts of patients admitted to a tertiary hospital in Lima, Peru: one group had cough or TB risk factors (triage) and the other did not report cough or TB risk factors (screening). We evaluated the sensitivity and specificity of qXR for the diagnosis of pulmonary TB using culture and Xpert as primary and secondary reference standards, including stratified analyses based on risk factors. In the triage cohort (n = 387), qXR v4 sensitivity was 0.91 (59/65, 95% CI 0.81-0.97) and specificity was 0.32 (103/322, 95% CI 0.27-0.37) using culture as reference standard. There was no difference in the area under the receiver-operating-characteristic curve (AUC) between qXR v3 and qXR v4 with either a culture or Xpert reference standard. In the screening cohort (n = 191), only one patient had a positive Xpert result, but specificity in this cohort was high (>90%). A high prevalence of radiographic lung abnormalities, most notably opacities (81%), consolidation (62%), or nodules (58%), was detected by qXR on digital CXR images from the triage cohort. qXR had high sensitivity but low specificity as a triage in hospitalized patients with cough or TB risk factors. Screening patients without cough or risk factors in this setting had a low diagnostic yield. These findings further support the need for population and setting-specific thresholds for CAD programs.
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Affiliation(s)
- Amanda M. Biewer
- Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Christine Tzelios
- Harvard Medical School, Boston, Massachusetts, United States of America
| | | | | | - Shelley Hurwitz
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Courtney M. Yuen
- Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Carole D. Mitnick
- Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Edward Nardell
- Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | | | - Dylan B. Tierney
- Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Massachusetts Department of Public Health, Boston, Massachusetts, United States of America
| | - Ruvandhi R. Nathavitharana
- Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, United States of America
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Vanobberghen F, Keter AK, Jacobs BK, Glass TR, Lynen L, Law I, Murphy K, van Ginneken B, Ayakaka I, van Heerden A, Maama L, Reither K. Computer-aided detection thresholds for digital chest radiography interpretation in tuberculosis diagnostic algorithms. ERJ Open Res 2024; 10:00508-2023. [PMID: 38196890 PMCID: PMC10772898 DOI: 10.1183/23120541.00508-2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 10/25/2023] [Indexed: 01/11/2024] Open
Abstract
Objectives Use of computer-aided detection (CAD) software is recommended to improve tuberculosis screening and triage, but threshold determination is challenging if reference testing has not been performed in all individuals. We aimed to determine such thresholds through secondary analysis of the 2019 Lesotho national tuberculosis prevalence survey. Methods Symptom screening and chest radiographs were performed in participants aged ≥15 years; those symptomatic or with abnormal chest radiographs provided samples for Xpert MTB/RIF and culture testing. Chest radiographs were processed using CAD4TB version 7. We used six methodological approaches to deal with participants who did not have bacteriological test results to estimate pulmonary tuberculosis prevalence and assess diagnostic accuracy. Results Among 17 070 participants, 5214 (31%) had their tuberculosis status determined; 142 had tuberculosis. Prevalence estimates varied between methodological approaches (0.83-2.72%). Using multiple imputation to estimate tuberculosis status for those eligible but not tested, and assuming those not eligible for testing were negative, a CAD4TBv7 threshold of 13 had a sensitivity of 89.7% (95% CI 84.6-94.8) and a specificity of 74.2% (73.6-74.9), close to World Health Organization (WHO) target product profile criteria. Assuming all those not tested were negative produced similar results. Conclusions This is the first study to evaluate CAD4TB in a community screening context employing a range of approaches to account for unknown tuberculosis status. The assumption that those not tested are negative - regardless of testing eligibility status - was robust. As threshold determination must be context specific, our analytically straightforward approach should be adopted to leverage prevalence surveys for CAD threshold determination in other settings with a comparable proportion of eligible but not tested participants.
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Affiliation(s)
- Fiona Vanobberghen
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Alfred Kipyegon Keter
- Institute of Tropical Medicine, Antwerp, Belgium
- Centre for Community Based Research, Human Sciences Research Council, Pietermaritzburg, South Africa
- Ghent University, Ghent, Belgium
| | | | - Tracy R. Glass
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | | | - Irwin Law
- Global Tuberculosis Programme, World Health Organization, Geneva, Switzerland
| | - Keelin Murphy
- Radboud University Medical Center, Nijmegen, Netherlands
| | | | - Irene Ayakaka
- SolidarMed, Partnerships for Health, Maseru, Lesotho
| | - Alastair van Heerden
- Centre for Community Based Research, Human Sciences Research Council, Pietermaritzburg, South Africa
- SAMRC/Wits Developmental Pathways for Health Research Unit (DPHRU), Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Llang Maama
- Disease Control Directorate, National Tuberculosis Program, Ministry of Health, Maseru, Lesotho
| | - Klaus Reither
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
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Biewer A, Tzelios C, Tintaya K, Roman B, Hurwitz S, Yuen CM, Mitnick CD, Nardell E, Lecca L, Tierney DB, Nathavitharana RR. Accuracy of digital chest x-ray analysis with artificial intelligence software as a triage and screening tool in hospitalized patients being evaluated for tuberculosis in Lima, Peru. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.17.23290110. [PMID: 37292955 PMCID: PMC10246158 DOI: 10.1101/2023.05.17.23290110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Introduction Tuberculosis (TB) transmission in healthcare facilities is common in high-incidence countries. Yet, the optimal approach for identifying inpatients who may have TB is unclear. We evaluated the diagnostic accuracy of qXR (Qure.ai, India) computer-aided detection (CAD) software versions 3.0 and 4.0 (v3 and v4) as a triage and screening tool within the FAST (Find cases Actively, Separate safely, and Treat effectively) transmission control strategy. Methods We prospectively enrolled two cohorts of patients admitted to a tertiary hospital in Lima, Peru: one group had cough or TB risk factors (triage) and the other did not report cough or TB risk factors (screening). We evaluated the sensitivity and specificity of qXR for the diagnosis of pulmonary TB using culture and Xpert as primary and secondary reference standards, including stratified analyses based on risk factors. Results In the triage cohort (n=387), qXR v4 sensitivity was 0.91 (59/65, 95% CI 0.81-0.97) and specificity was 0.32 (103/322, 95% CI 0.27-0.37) using culture as reference standard. There was no difference in the area under the receiver-operating-characteristic curve (AUC) between qXR v3 and qXR v4 with either a culture or Xpert reference standard. In the screening cohort (n=191), only one patient had a positive Xpert result, but specificity in this cohort was high (>90%). A high prevalence of radiographic lung abnormalities, most notably opacities (81%), consolidation (62%), or nodules (58%), was detected by qXR on digital CXR images from the triage cohort. Conclusions qXR had high sensitivity but low specificity as a triage in hospitalized patients with cough or TB risk factors. Screening patients without cough or risk factors in this setting had a low diagnostic yield. These findings further support the need for population and setting-specific thresholds for CAD programs.
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Affiliation(s)
- Amanda Biewer
- Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA
| | | | | | | | | | - Courtney M Yuen
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Carole D Mitnick
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Edward Nardell
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | | | - Dylan B Tierney
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA
- Massachusetts Department of Public Health, Boston, MA
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Vijayan S, Jondhale V, Pande T, Khan A, Brouwer M, Hegde A, Gandhi R, Roddawar V, Jichkar S, Kadu A, Bharaswadkar S, Sharma M, Vasquez NA, Richardson L, Robert D, Pawar S. Implementing a chest X-ray artificial intelligence tool to enhance tuberculosis screening in India: Lessons learned. PLOS DIGITAL HEALTH 2023; 2:e0000404. [PMID: 38060461 PMCID: PMC10703224 DOI: 10.1371/journal.pdig.0000404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Accepted: 11/06/2023] [Indexed: 02/12/2024]
Abstract
Artificial Intelligence (AI) based chest X-ray (CXR) screening for tuberculosis (TB) is becoming increasingly popular. Still, deploying such AI tools can be challenging due to multiple real-life barriers like software installation, workflow integration, network connectivity constraints, limited human resources available to interpret findings, etc. To understand these challenges, PATH implemented a TB REACH active case-finding program in a resource-limited setting of Nagpur in India, where an AI software device (qXR) intended for TB screening using CXR images was used. Eight private CXR laboratories that fulfilled prerequisites for AI software installation were engaged for this program. Key lessons about operational feasibility and accessibility, along with the strategies adopted to overcome these challenges, were learned during this program. This program also helped to screen 10,481 presumptive TB individuals using informal providers based on clinical history. Among them, 2,303 individuals were flagged as presumptive for TB by a radiologist or by AI based on their CXR interpretation. Approximately 15.8% increase in overall TB yield could be attributed to the presence of AI alone because these additional cases were not deemed presumptive for TB by radiologists, but AI was able to identify them. Successful implementation of AI tools like qXR in resource-limited settings in India will require solving real-life implementation challenges for seamless deployment and workflow integration.
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Affiliation(s)
| | | | - Tripti Pande
- External consultant, Washington DC, United States of America
| | - Amera Khan
- STOP TB Partnership, Geneva, Switzerland
| | | | | | | | | | - Shilpa Jichkar
- Department of Health Services, Nagpur Municipal Corporation, Nagpur, India
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Maiter A, Hocking K, Matthews S, Taylor J, Sharkey M, Metherall P, Alabed S, Dwivedi K, Shahin Y, Anderson E, Holt S, Rowbotham C, Kamil MA, Hoggard N, Balasubramanian SP, Swift A, Johns CS. Evaluating the performance of artificial intelligence software for lung nodule detection on chest radiographs in a retrospective real-world UK population. BMJ Open 2023; 13:e077348. [PMID: 37940155 PMCID: PMC10632826 DOI: 10.1136/bmjopen-2023-077348] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 10/16/2023] [Indexed: 11/10/2023] Open
Abstract
OBJECTIVES Early identification of lung cancer on chest radiographs improves patient outcomes. Artificial intelligence (AI) tools may increase diagnostic accuracy and streamline this pathway. This study evaluated the performance of commercially available AI-based software trained to identify cancerous lung nodules on chest radiographs. DESIGN This retrospective study included primary care chest radiographs acquired in a UK centre. The software evaluated each radiograph independently and outputs were compared with two reference standards: (1) the radiologist report and (2) the diagnosis of cancer by multidisciplinary team decision. Failure analysis was performed by interrogating the software marker locations on radiographs. PARTICIPANTS 5722 consecutive chest radiographs were included from 5592 patients (median age 59 years, 53.8% women, 1.6% prevalence of cancer). RESULTS Compared with radiologist reports for nodule detection, the software demonstrated sensitivity 54.5% (95% CI 44.2% to 64.4%), specificity 83.2% (82.2% to 84.1%), positive predictive value (PPV) 5.5% (4.6% to 6.6%) and negative predictive value (NPV) 99.0% (98.8% to 99.2%). Compared with cancer diagnosis, the software demonstrated sensitivity 60.9% (50.1% to 70.9%), specificity 83.3% (82.3% to 84.2%), PPV 5.6% (4.8% to 6.6%) and NPV 99.2% (99.0% to 99.4%). Normal or variant anatomy was misidentified as an abnormality in 69.9% of the 943 false positive cases. CONCLUSIONS The software demonstrated considerable underperformance in this real-world patient cohort. Failure analysis suggested a lack of generalisability in the training and testing datasets as a potential factor. The low PPV carries the risk of over-investigation and limits the translation of the software to clinical practice. Our findings highlight the importance of training and testing software in representative datasets, with broader implications for the implementation of AI tools in imaging.
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Affiliation(s)
- Ahmed Maiter
- School of Medicine and Population Health, The University of Sheffield, Sheffield, UK
- Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Katherine Hocking
- Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Suzanne Matthews
- Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
- Medical Imaging and Medical Physics, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Jonathan Taylor
- Medical Imaging and Medical Physics, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Michael Sharkey
- Medical Imaging and Medical Physics, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Peter Metherall
- Medical Imaging and Medical Physics, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Samer Alabed
- School of Medicine and Population Health, The University of Sheffield, Sheffield, UK
- Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Krit Dwivedi
- School of Medicine and Population Health, The University of Sheffield, Sheffield, UK
- Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Yousef Shahin
- School of Medicine and Population Health, The University of Sheffield, Sheffield, UK
- Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Elizabeth Anderson
- Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Sarah Holt
- Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | | | - Mohamed A Kamil
- Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Nigel Hoggard
- School of Medicine and Population Health, The University of Sheffield, Sheffield, UK
- Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
- NIHR Sheffield Biomedical Research Centre, Sheffield, UK
| | - Saba P Balasubramanian
- Medical Imaging and Medical Physics, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
- Surgical directorate, Sheffield Teaching Hospitals Foundation NHS Trust, Sheffield, UK
| | - Andrew Swift
- School of Medicine and Population Health, The University of Sheffield, Sheffield, UK
- Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
- NIHR Sheffield Biomedical Research Centre, Sheffield, UK
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10
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Innes AL, Martinez A, Gao X, Dinh N, Hoang GL, Nguyen TBP, Vu VH, Luu THT, Le TTT, Lebrun V, Trieu VC, Tran NDB, Qin ZZ, Pham HM, Dinh VL, Nguyen BH, Truong TTH, Nguyen VC, Nguyen VN, Mai TH. Computer-Aided Detection for Chest Radiography to Improve the Quality of Tuberculosis Diagnosis in Vietnam's District Health Facilities: An Implementation Study. Trop Med Infect Dis 2023; 8:488. [PMID: 37999607 PMCID: PMC10675130 DOI: 10.3390/tropicalmed8110488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 10/20/2023] [Accepted: 10/25/2023] [Indexed: 11/25/2023] Open
Abstract
In Vietnam, chest radiography (CXR) is used to refer people for GeneXpert (Xpert) testing to diagnose tuberculosis (TB), demonstrating high yield for TB but a wide range of CXR abnormality rates. In a multi-center implementation study, computer-aided detection (CAD) was integrated into facility-based TB case finding to standardize CXR interpretation. CAD integration was guided by a programmatic framework developed for routine implementation. From April through December 2022, 24,945 CXRs from TB-vulnerable populations presenting to district health facilities were evaluated. Physicians interpreted all CXRs in parallel with CAD (qXR 3.0) software, for which the selected TB threshold score was ≥0.60. At three months, there was 47.3% concordance between physician and CAD TB-presumptive CXR results, 7.8% of individuals who received CXRs were referred for Xpert testing, and 858 people diagnosed with Xpert-confirmed TB per 100,000 CXRs. This increased at nine months to 76.1% concordant physician and CAD TB-presumptive CXRs, 9.6% referred for Xpert testing, and 2112 people with Xpert-confirmed TB per 100,000 CXRs. Our programmatic CAD-CXR framework effectively supported physicians in district facilities to improve the quality of referral for diagnostic testing and increase TB detection yield. Concordance between physician and CAD CXR results improved with training and was important to optimize Xpert testing.
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Affiliation(s)
- Anh L. Innes
- FHI 360 Asia Pacific Regional Office, Bangkok 10330, Thailand
| | | | - Xiaoming Gao
- FHI 360, Durham, NC 27701, USA; (A.M.); (X.G.); (N.D.)
| | - Nhi Dinh
- FHI 360, Durham, NC 27701, USA; (A.M.); (X.G.); (N.D.)
| | - Gia Linh Hoang
- FHI 360 Vietnam, Hanoi 10000, Vietnam; (G.L.H.); (T.B.P.N.); (V.H.V.); (T.H.T.L.); (T.T.T.L.); (V.L.); (V.C.T.); (N.D.B.T.); (T.H.M.)
| | - Thi Bich Phuong Nguyen
- FHI 360 Vietnam, Hanoi 10000, Vietnam; (G.L.H.); (T.B.P.N.); (V.H.V.); (T.H.T.L.); (T.T.T.L.); (V.L.); (V.C.T.); (N.D.B.T.); (T.H.M.)
| | - Viet Hien Vu
- FHI 360 Vietnam, Hanoi 10000, Vietnam; (G.L.H.); (T.B.P.N.); (V.H.V.); (T.H.T.L.); (T.T.T.L.); (V.L.); (V.C.T.); (N.D.B.T.); (T.H.M.)
| | - Tuan Ho Thanh Luu
- FHI 360 Vietnam, Hanoi 10000, Vietnam; (G.L.H.); (T.B.P.N.); (V.H.V.); (T.H.T.L.); (T.T.T.L.); (V.L.); (V.C.T.); (N.D.B.T.); (T.H.M.)
| | - Thi Thu Trang Le
- FHI 360 Vietnam, Hanoi 10000, Vietnam; (G.L.H.); (T.B.P.N.); (V.H.V.); (T.H.T.L.); (T.T.T.L.); (V.L.); (V.C.T.); (N.D.B.T.); (T.H.M.)
| | - Victoria Lebrun
- FHI 360 Vietnam, Hanoi 10000, Vietnam; (G.L.H.); (T.B.P.N.); (V.H.V.); (T.H.T.L.); (T.T.T.L.); (V.L.); (V.C.T.); (N.D.B.T.); (T.H.M.)
| | - Van Chinh Trieu
- FHI 360 Vietnam, Hanoi 10000, Vietnam; (G.L.H.); (T.B.P.N.); (V.H.V.); (T.H.T.L.); (T.T.T.L.); (V.L.); (V.C.T.); (N.D.B.T.); (T.H.M.)
| | - Nghi Do Bao Tran
- FHI 360 Vietnam, Hanoi 10000, Vietnam; (G.L.H.); (T.B.P.N.); (V.H.V.); (T.H.T.L.); (T.T.T.L.); (V.L.); (V.C.T.); (N.D.B.T.); (T.H.M.)
| | - Zhi Zhen Qin
- Stop TB Partnership, Grand-Saconnex, 1218 Geneva, Switzerland;
| | - Huy Minh Pham
- United States Agency for International Development/Vietnam, Hanoi 10000, Vietnam;
| | - Van Luong Dinh
- Vietnam National Lung Hospital, Hanoi 10000, Vietnam; (V.L.D.); (B.H.N.); (T.T.H.T.); (V.C.N.); (V.N.N.)
| | - Binh Hoa Nguyen
- Vietnam National Lung Hospital, Hanoi 10000, Vietnam; (V.L.D.); (B.H.N.); (T.T.H.T.); (V.C.N.); (V.N.N.)
| | - Thi Thanh Huyen Truong
- Vietnam National Lung Hospital, Hanoi 10000, Vietnam; (V.L.D.); (B.H.N.); (T.T.H.T.); (V.C.N.); (V.N.N.)
| | - Van Cu Nguyen
- Vietnam National Lung Hospital, Hanoi 10000, Vietnam; (V.L.D.); (B.H.N.); (T.T.H.T.); (V.C.N.); (V.N.N.)
| | - Viet Nhung Nguyen
- Vietnam National Lung Hospital, Hanoi 10000, Vietnam; (V.L.D.); (B.H.N.); (T.T.H.T.); (V.C.N.); (V.N.N.)
- Pulmonology Department, University of Medicine and Pharmacy, Vietnam National University, Hanoi 10000, Vietnam
| | - Thu Hien Mai
- FHI 360 Vietnam, Hanoi 10000, Vietnam; (G.L.H.); (T.B.P.N.); (V.H.V.); (T.H.T.L.); (T.T.T.L.); (V.L.); (V.C.T.); (N.D.B.T.); (T.H.M.)
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11
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Abuzerr S, Zinszer K. Computer-aided diagnostic accuracy of pulmonary tuberculosis on chest radiography among lower respiratory tract symptoms patients. Front Public Health 2023; 11:1254658. [PMID: 37965525 PMCID: PMC10641698 DOI: 10.3389/fpubh.2023.1254658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 10/09/2023] [Indexed: 11/16/2023] Open
Abstract
Even though the Gaza Strip is a low pulmonary tuberculosis (TB) burden region, it is well-known that TB is primarily a socioeconomic problem associated with overcrowding, poor hygiene, a lack of fresh water, and limited access to healthcare, which is the typical case in the Gaza Strip. Therefore, this study aimed at assessing the accuracy of the automatic software computer-aided detection for tuberculosis (CAD4TB) in diagnosing pulmonary TB on chest radiography and compare the CAD4TB software reading with the results of geneXpert. Using a census sampling method, the study was conducted in radiology departments in the Gaza Strip hospitals between 1 December 2022 and 31 March 2023. A digital X-ray, printer, and online X-ray system backed by CAD4TBv6 software were used to screen patients with lower respiratory tract symptoms. GeneXpert analysis was performed for all patients having a score > 40. A total of 1,237 patients presenting with lower respiratory tract symptoms participated in this current study. Chest X-ray readings showed that 7.8% (n = 96) were presumptive for TB. The CAD4TBv6 scores showed that 11.8% (n = 146) of recruited patients were presumptive for TB. GeneXpert testing on sputum samples showed that 6.2% (n = 77) of those with a score > 40 on CAD4TB were positive for pulmonary TB. Significant differences were found in chest X-ray readings, CAD4TBv6 scores, and GeneXpert results among sociodemographic and health status variables (P-value < 0.05). The study showed that the incidence rate of TB in the Gaza Strip is 3.5 per 100,000 population in the Gaza strip. The sensitivity of the CAD4TBv6 score and the symptomatic review for tuberculosis with a threshold score of >40 is 80.2%, and the specificity is 94.0%. The positive Likelihood Ratio is 13.3%, Negative Likelihood Ratio is 0.2 with 7.8% prevalence. Positive Predictive Value is 52.7%, Negative Predictive Value is 98.3%, and accuracy is 92.9%. In a resource-limited country with a high burden of neglected disease, combining chest X-ray readings by CAD4TB and symptomatology is extremely valuable for screening a population at risk. CAD4TB is noticeably more efficient than other methods for TB screening and early diagnosis in people who would otherwise go undetected.
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Affiliation(s)
- Samer Abuzerr
- Department of Medical Sciences, University College of Science and Technology, Gaza, Palestine
| | - Kate Zinszer
- School of Public Health, Department of Social and Preventive Medicine, University of Montreal, Montréal, QC, Canada
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12
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Gelaw SM, Kik SV, Ruhwald M, Ongarello S, Egzertegegne TS, Gorbacheva O, Gilpin C, Marano N, Lee S, Phares CR, Medina V, Amatya B, Denkinger CM. Diagnostic accuracy of three computer-aided detection systems for detecting pulmonary tuberculosis on chest radiography when used for screening: Analysis of an international, multicenter migrants screening study. PLOS GLOBAL PUBLIC HEALTH 2023; 3:e0000402. [PMID: 37450425 PMCID: PMC10348531 DOI: 10.1371/journal.pgph.0000402] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 06/04/2023] [Indexed: 07/18/2023]
Abstract
The aim of this study was to independently evaluate the diagnostic accuracy of three artificial intelligence (AI)-based computer aided detection (CAD) systems for detecting pulmonary tuberculosis (TB) on global migrants screening chest x-ray (CXR) cases when compared against both microbiological and radiological reference standards (MRS and RadRS, respectively). Retrospective clinical data and CXR images were collected from the International Organization for Migration (IOM) pre-migration health assessment TB screening global database for US-bound migrants. A total of 2,812 participants were included in the dataset used for analysis against RadRS, of which 1,769 (62.9%) had accompanying microbiological test results and were included against MRS. All CXRs were interpreted by three CAD systems (CAD4TB v6, Lunit INSIGHT v4.9.0, and qXR v2) in offline setting, and re-interpreted by two expert radiologists in a blinded fashion. The performance was evaluated using receiver operating characteristics curve (ROC), estimates of sensitivity and specificity at different CAD thresholds against both microbiological and radiological reference standards (MRS and RadRS, respectively), and was compared with that of the expert radiologists. The area under the curve against MRS was highest for Lunit (0.85; 95% CI 0.83-0.87), followed by qXR (0.75; 95% CI 0.72-0.77) and then CAD4TB (0.71; 95% CI 0.68-0.73). At a set specificity of 70%, Lunit had the highest sensitivity (81.4%; 95% CI 77.9-84.6); at a set sensitivity of 90%, specificity was also highest for Lunit (54.5%; 95% CI 51.7-57.3). The CAD systems performed comparable to the sensitivity (98.3%), and except CAD4TB, to specificity (13.7%) of the expert radiologists. Similar trends were observed when using RadRS. Area under the curve against RadRS was highest for CAD4TB (0.87; 95% CI 0.86-0.89) and Lunit (0.87; 95% CI 0.85-0.88) followed by qXR (0.81; 95% CI 0.80-0.83). At a set specificity of 70%, CAD4TB had highest sensitivity (84.1%; 95% CI 82.3-85.8) followed by Lunit (80.9%; 95% CI 78.9-82.7); and at a set sensitivity of 90%, specificity was also highest for CAD4TB (54.6%; 95% CI 51.3-57.8). In conclusion, the study demonstrated that the three CAD systems had broadly similar diagnostic accuracy with regard to TB screening and comparable accuracy to an expert radiologist against MRS. Compared with different reference standards, Lunit performed better than both qXR and CAD4TB against MRS, and CAD4TB and Lunit better than qXR against RadRS. Moreover, the performance of the CADs can be impacted by characteristics of subgroup of population. The main limitation was that our study relied on retrospective data and MRS was not routinely done in individuals with a low suspicion of TB and a normal CXR. Our findings suggest that CAD systems could be a useful tool for TB screening programs in remote, high TB prevalent places where access to expert radiologists may be limited. However, further large-scale prospective studies are needed to address outstanding questions around the operational performance and technical requirements of the CAD systems.
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Affiliation(s)
| | | | | | | | | | - Olga Gorbacheva
- International Organization for Migration (IOM), Geneva, Switzerland
| | | | - Nina Marano
- United States Centers for Disease Control and Prevention (CDC), Atlanta, Georgia, United States of America
| | - Scott Lee
- United States Centers for Disease Control and Prevention (CDC), Atlanta, Georgia, United States of America
| | - Christina R. Phares
- United States Centers for Disease Control and Prevention (CDC), Atlanta, Georgia, United States of America
| | - Victoria Medina
- International Organization for Migration (IOM), Manila, Philippines
| | - Bhaskar Amatya
- International Organization for Migration (IOM), Manila, Philippines
| | - Claudia M. Denkinger
- FIND, Geneva, Switzerland
- Heidelberg University Hospital, Center of Infectious Diseases, Heidelberg, Germany
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13
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Park J, Hwang EJ, Lee JH, Hong W, Nam JG, Lim WH, Kim JH, Goo JM, Park CM. Identification of Active Pulmonary Tuberculosis Among Patients With Positive Interferon-Gamma Release Assay Results: Value of a Deep Learning-based Computer-aided Detection System in Different Scenarios of Implementation. J Thorac Imaging 2023; 38:145-153. [PMID: 36744946 DOI: 10.1097/rti.0000000000000691] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
PURPOSE To evaluate the accuracy of a deep learning-based computer-aided detection (CAD) system in identifying active pulmonary tuberculosis on chest radiographs (CRs) of patients with positive interferon-gamma release assay (IGRA) results in different scenarios of clinical implementation. MATERIALS AND METHODS We collected the CRs of consecutive patients with positive IGRA results. Findings of active pulmonary tuberculosis on CRs were independently evaluated by the CAD and a thoracic radiologist, followed by interpretation using the CAD. Sensitivity and specificity were evaluated in different scenarios: (a) radiologists' interpretation, (b) radiologists' CAD-assisted interpretation, and (c) CAD-based prescreening (radiologists' interpretation for positive CAD results only). We conducted a reader test to compare the accuracy of the CAD with those of 5 radiologists. RESULTS Among 1780 patients (men, 53.8%; median age, 56 y), 44 (2.5%) were diagnosed with active pulmonary tuberculosis. The CAD-assisted interpretation exhibited a higher sensitivity (81.8% vs. 72.7%; P =0.046) but lower specificity than the radiologists' interpretation (84.1% vs. 85.7%; P <0.001). The CAD-based prescreening exhibited a higher specificity than the radiologists' interpretation (88.8% vs. 85.7%; P <0.001) at the same sensitivity, with a workload reduction of 85.2% (1780 to 263). In the reader test, the CAD exhibited a higher sensitivity than radiologists (72.7% vs. 59.5%; P =0.005) at the same specificity (88.0%), and CAD-assisted interpretation significantly improved the sensitivity of radiologists' interpretation (72.3%; P <0.001). CONCLUSIONS For identifying active pulmonary tuberculosis among patients with positive IGRA results, deep learning-based CAD can enhance the sensitivity of interpretation. CAD-based prescreening may reduce the radiologists' workload at an improved specificity.
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Affiliation(s)
- Jongsoo Park
- Department of Radiology, Seoul National University Hospital
- Department of Radiology, Yeungnam University Medical Center, Daegu
| | - Eui Jin Hwang
- Department of Radiology, Seoul National University Hospital
- Department of Radiology, Seoul National University College of Medicine, Seoul
| | - Jong Hyuk Lee
- Department of Radiology, Seoul National University Hospital
| | - Wonju Hong
- Department of Radiology, Seoul National University Hospital
- Department of Radiology, Hallym University Sacred Heart Hospital, Gyeonggi-do, Korea
| | - Ju Gang Nam
- Department of Radiology, Seoul National University Hospital
| | - Woo Hyeon Lim
- Department of Radiology, Seoul National University Hospital
| | - Jae Hyun Kim
- Department of Radiology, Seoul National University Hospital
| | - Jin Mo Goo
- Department of Radiology, Seoul National University Hospital
- Department of Radiology, Seoul National University College of Medicine, Seoul
| | - Chang Min Park
- Department of Radiology, Seoul National University Hospital
- Department of Radiology, Seoul National University College of Medicine, Seoul
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14
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Deep learning classification of active tuberculosis lung zones wise manifestations using chest X-rays: a multi label approach. Sci Rep 2023; 13:887. [PMID: 36650270 PMCID: PMC9845381 DOI: 10.1038/s41598-023-28079-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 01/12/2023] [Indexed: 01/19/2023] Open
Abstract
Chest X-rays are the most economically viable diagnostic imaging test for active pulmonary tuberculosis screening despite the high sensitivity and low specificity when interpreted by clinicians or radiologists. Computer aided detection (CAD) algorithms, especially convolution based deep learning architecture, have been proposed to facilitate the automation of radiography imaging modalities. Deep learning algorithms have found success in classifying various abnormalities in lung using chest X-ray. We fine-tuned, validated and tested EfficientNetB4 architecture and utilized the transfer learning methodology for multilabel approach to detect lung zone wise and image wise manifestations of active pulmonary tuberculosis using chest X-ray. We used Area Under Receiver Operating Characteristic (AUC), sensitivity and specificity along with 95% confidence interval as model evaluation metrics. We also utilized the visualisation capabilities of convolutional neural networks (CNN), Gradient-weighted Class Activation Mapping (Grad-CAM) as post-hoc attention method to investigate the model and visualisation of Tuberculosis abnormalities and discuss them from radiological perspectives. EfficientNetB4 trained network achieved remarkable AUC, sensitivity and specificity of various pulmonary tuberculosis manifestations in intramural test set and external test set from different geographical region. The grad-CAM visualisations and their ability to localize the abnormalities can aid the clinicians at primary care settings for screening and triaging of tuberculosis where resources are constrained or overburdened.
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15
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Soares TR, Oliveira RDD, Liu YE, Santos ADS, Santos PCPD, Monte LRS, Oliveira LMD, Park CM, Hwang EJ, Andrews JR, Croda J. Evaluation of chest X-ray with automated interpretation algorithms for mass tuberculosis screening in prisons: a cross-sectional study. LANCET REGIONAL HEALTH. AMERICAS 2023; 17:100388. [PMID: 36776567 PMCID: PMC9904090 DOI: 10.1016/j.lana.2022.100388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 09/28/2022] [Accepted: 10/18/2022] [Indexed: 06/18/2023]
Abstract
Background The World Health Organization (WHO) recommends systematic tuberculosis (TB) screening in prisons. Evidence is lacking for accurate and scalable screening approaches in this setting. We aimed to assess the accuracy of artificial intelligence-based chest x-ray interpretation algorithms for TB screening in prisons. Methods We performed prospective TB screening in three male prisons in Brazil from October 2017 to December 2019. We administered a standardized questionnaire, performed a chest x-ray in a mobile unit, and collected sputum for confirmatory testing using Xpert MTB/RIF and culture. We evaluated x-ray images using three algorithms (CAD4TB version 6, Lunit version 3.1.0.0 and qXR version 3) and compared their accuracy. We utilized multivariable logistic regression to assess the effect of demographic and clinical characteristics on algorithm accuracy. Finally, we investigated the relationship between abnormality scores and Xpert semi-quantitative results. Findings Among 2075 incarcerated individuals, 259 (12.5%) had confirmed TB. All three algorithms performed similarly overall with area under the receiver operating characteristic curve (AUC) of 0.88-0.91. At 90% sensitivity, only LunitTB and qXR met the WHO Target Product Profile requirements for a triage test, with specificity of 84% and 74%, respectively. All algorithms had variable performance by age, prior TB, smoking, and presence of TB symptoms. LunitTB was the most robust to this heterogeneity but nonetheless failed to meet the TPP for individuals with previous TB. Abnormality scores of all three algorithms were significantly correlated with sputum bacillary load. Interpretation Automated x-ray interpretation algorithms can be an effective triage tool for TB screening in prisons. However, their specificity is insufficient in individuals with previous TB. Funding This study was supported by the US National Institutes of Health (grant numbers R01 AI130058 and R01 AI149620) and the State Secretary of Health of Mato Grosso do Sul.
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Affiliation(s)
- Thiego Ramon Soares
- Faculty of Health Sciences of Federal University of Grande Dourados, Dourados, MS, Brazil
| | - Roberto Dias de Oliveira
- Faculty of Health Sciences of Federal University of Grande Dourados, Dourados, MS, Brazil
- Nursing School, State University of Mato Grosso do Sul, Dourados, MS, Brazil
| | - Yiran E. Liu
- Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, CA, United States of America
| | - Andrea da Silva Santos
- Faculty of Health Sciences of Federal University of Grande Dourados, Dourados, MS, Brazil
| | | | | | | | - Chang Min Park
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Eui Jin Hwang
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Jason R. Andrews
- Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, CA, United States of America
| | - Julio Croda
- Oswaldo Cruz Foundation, Campo Grande, MS, Brazil
- Department of Epidemiology of Microbial Diseases, Yale University School of Public Health, New Haven, CT, United States of America
- School of Medicine, Federal University of Mato Grosso do Sul, Campo Grande, MS, Brazil
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16
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van Beek EJR, Ahn JS, Kim MJ, Murchison JT. Validation study of machine-learning chest radiograph software in primary and emergency medicine. Clin Radiol 2023; 78:1-7. [PMID: 36171164 DOI: 10.1016/j.crad.2022.08.129] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 07/20/2022] [Accepted: 08/08/2022] [Indexed: 01/07/2023]
Abstract
AIM To evaluate the performance of a machine learning based algorithm tool for chest radiographs (CXRs), applied to a consecutive cohort of historical clinical cases, in comparison to expert chest radiologists. MATERIALS AND METHODS The study comprised 1,960 consecutive CXR from primary care referrals and the emergency department (992 and 968 cases respectively), obtained in 2015 at a UK hospital. Two chest radiologists, each with >20 years of experience independently read all studies in consensus to serve as a reference standard. A chest artificial intelligence (AI) algorithm, Lunit INSIGHT CXR, was run on the CXRs, and results were correlated with those by the expert readers. The area under the receiver operating characteristic curve (AUROC) was calculated for the normal and 10 common findings: atelectasis, fibrosis, calcification, consolidation, lung nodules, cardiomegaly, mediastinal widening, pleural effusion, pneumothorax, and pneumoperitoneum. RESULTS The ground truth annotation identified 398 primary care and 578 emergency department datasets containing pathologies. The AI algorithm showed AUROC of 0.881-0.999 in the emergency department dataset and 0.881-0.998 in the primary care dataset. The AUROC for each of the findings between the primary care and emergency department datasets did not differ, except for pleural effusion (0.954 versus 0.988, p<0.001). CONCLUSIONS The AI algorithm can accurately and consistently differentiate normal from major thoracic abnormalities in both acute and non-acute settings, and can serve as a triage tool.
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Affiliation(s)
- E J R van Beek
- Edinburgh Imaging, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK; Department of Radiology, Royal Infirmary of Edinburgh, Edinburgh, UK.
| | | | | | - J T Murchison
- Department of Radiology, Royal Infirmary of Edinburgh, Edinburgh, UK
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17
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Han D, Chen Y, Li X, Li W, Zhang X, He T, Yu Y, Dou Y, Duan H, Yu N. Development and validation of a 3D-convolutional neural network model based on chest CT for differentiating active pulmonary tuberculosis from community-acquired pneumonia. LA RADIOLOGIA MEDICA 2023; 128:68-80. [PMID: 36574111 PMCID: PMC9793822 DOI: 10.1007/s11547-022-01580-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 12/13/2022] [Indexed: 12/29/2022]
Abstract
PURPOSE To develop and validate a 3D-convolutional neural network (3D-CNN) model based on chest CT for differentiating active pulmonary tuberculosis (APTB) from community-acquired pneumonia (CAP). MATERIALS AND METHODS Chest CT images of APTB and CAP patients diagnosed in two imaging centers (n = 432 in center A and n = 61 in center B) were collected retrospectively. The data in center A were divided into training, validation and internal test sets, and the data in center B were used as an external test set. A 3D-CNN was built using Keras deep learning framework. After the training, the 3D-CNN selected the model with the highest accuracy in the validation set as the optimal model, which was applied to the two test sets in centers A and B. In addition, the two test sets were independently diagnosed by two radiologists. The 3D-CNN optimal model was compared with the discrimination, calibration and net benefit of the two radiologists in differentiating APTB from CAP using chest CT images. RESULTS The accuracy of the 3D-CNN optimal model was 0.989 and 0.934 with the internal and external test set, respectively. The area-under-the-curve values with the 3D-CNN model in the two test sets were statistically higher than that of the two radiologists (all P < 0.05), and there was a high calibration degree. The decision curve analysis showed that the 3D-CNN optimal model had significantly higher net benefit for patients than the two radiologists. CONCLUSIONS 3D-CNN has high classification performance in differentiating APTB from CAP using chest CT images. The application of 3D-CNN provides a new automatic and rapid diagnosis method for identifying patients with APTB from CAP using chest CT images.
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Affiliation(s)
- Dong Han
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Weiyang West Rd, Xianyang, 712000 China
| | - Yibing Chen
- School of Information Science & Technology, Northwest University, Xi’an, 710127 Shaanxi China
| | - Xuechao Li
- Clinical Research Center, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, 712000 China
| | - Wen Li
- Department of Radiology, Baoji Central Hospital, Baoji, 721008 China
| | - Xirong Zhang
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Weiyang West Rd, Xianyang, 712000 China ,College of Medical Technology, Shaanxi University of Chinese Medicine, Xianyang, 712000 China
| | - Taiping He
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Weiyang West Rd, Xianyang, 712000 China ,College of Medical Technology, Shaanxi University of Chinese Medicine, Xianyang, 712000 China
| | - Yong Yu
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Weiyang West Rd, Xianyang, 712000 China ,College of Medical Technology, Shaanxi University of Chinese Medicine, Xianyang, 712000 China
| | - Yuequn Dou
- Respiratory Department, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, 712000 China
| | - Haifeng Duan
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Weiyang West Rd, Xianyang, 712000 China
| | - Nan Yu
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Weiyang West Rd, Xianyang, 712000, China.
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Diagnostic Accuracy of the Artificial Intelligence Methods in Medical Imaging for Pulmonary Tuberculosis: A Systematic Review and Meta-Analysis. J Clin Med 2022; 12:jcm12010303. [PMID: 36615102 PMCID: PMC9820940 DOI: 10.3390/jcm12010303] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 12/21/2022] [Accepted: 12/24/2022] [Indexed: 01/03/2023] Open
Abstract
Tuberculosis (TB) remains one of the leading causes of death among infectious diseases worldwide. Early screening and diagnosis of pulmonary tuberculosis (PTB) is crucial in TB control, and tend to benefit from artificial intelligence. Here, we aimed to evaluate the diagnostic efficacy of a variety of artificial intelligence methods in medical imaging for PTB. We searched MEDLINE and Embase with the OVID platform to identify trials published update to November 2022 that evaluated the effectiveness of artificial-intelligence-based software in medical imaging of patients with PTB. After data extraction, the quality of studies was assessed using quality assessment of diagnostic accuracy studies 2 (QUADAS-2). Pooled sensitivity and specificity were estimated using a bivariate random-effects model. In total, 3987 references were initially identified and 61 studies were finally included, covering a wide range of 124,959 individuals. The pooled sensitivity and the specificity were 91% (95% confidence interval (CI), 89-93%) and 65% (54-75%), respectively, in clinical trials, and 94% (89-96%) and 95% (91-97%), respectively, in model-development studies. These findings have demonstrated that artificial-intelligence-based software could serve as an accurate tool to diagnose PTB in medical imaging. However, standardized reporting guidance regarding AI-specific trials and multicenter clinical trials is urgently needed to truly transform this cutting-edge technology into clinical practice.
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Margineanu I, Louka C, Akkerman O, Stienstra Y, Alffenaar JW. eHealth in TB clinical management. Int J Tuberc Lung Dis 2022; 26:1151-1161. [PMID: 36447317 PMCID: PMC9728950 DOI: 10.5588/ijtld.21.0602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND: The constant expansion of internet and mobile technologies has created new opportunities in the field of eHealth, or the digital delivery of healthcare services. This TB meta-analysis aims to examine eHealth and its impact on TB clinical management in order to formulate recommendations for further development.METHODS: A systematic search was performed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework in PubMed and Embase of articles published up to April 2021. Screening, extraction and quality assessment were performed by two independent researchers. Studies evaluating an internet and/or mobile-based eHealth intervention with an impact on TB clinical management were included. Outcomes were organised following the five domains described in the WHO "Recommendations on Digital Interventions for Health System Strengthening" guideline.RESULTS: Search strategy yielded 3,873 studies, and 89 full texts were finally included. eHealth tended to enhance screening, diagnosis and treatment indicators, while being cost-effective and acceptable to users. The main challenges concern hardware malfunction and software misuse.CONCLUSION: This study offers a broad overview of the innovative field of eHealth applications in TB. Different studies implementing eHealth solutions consistently reported on benefits, but also on specific challenges. eHealth is a promising field of research and could enhance clinical management of TB.
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Affiliation(s)
- I Margineanu
- Department of Clinical Pharmacy and Pharmacology, University Medical Centrum Groningen, University of Groningen, Groningen, the Netherlands, Iasi Pulmonary Diseases University Hospital, Iasi, Romania
| | - C Louka
- Department of Internal Medicine/Infectious Diseases, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - O Akkerman
- Tuberculosis Center Beatrixoord, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands, Department of Pulmonary Diseases and Tuberculosis, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Y Stienstra
- Department of Internal Medicine/Infectious Diseases, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands, Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK
| | - J-W Alffenaar
- Department of Clinical Pharmacy and Pharmacology, University Medical Centrum Groningen, University of Groningen, Groningen, the Netherlands, Faculty of Medicine and Health, School of Pharmacy, University of Sydney, Camperdown, NSW, Australia, Westmead Hospital, Sydney, NSW, Australia, Marie Bashir Institute for Infectious Diseases and Biosecurity, University of Sydney, Sydney, NSW, Australia
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20
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Han D, He T, Yu Y, Guo Y, Chen Y, Duan H, Yu N. Diagnosis of Active Pulmonary Tuberculosis and Community Acquired Pneumonia Using Convolution Neural Network Based on Transfer Learning. Acad Radiol 2022; 29:1486-1492. [PMID: 35063352 DOI: 10.1016/j.acra.2021.12.025] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 12/20/2021] [Accepted: 12/22/2021] [Indexed: 12/14/2022]
Abstract
RATIONALE AND OBJECTIVES A convolutional neural network (CNN) model for the diagnosis of active pulmonary tuberculosis (APTB) and community-acquired pneumonia (CAP) using chest radiographs (CRs) was constructed and verified based on transfer learning. MATERIALS AND METHODS CRs of 1247 APTB cases, 1488 CAP cases and 1247 normal cases were collected. All CRs were randomly divided into training set (1992 cases), validation set (1194 cases) and test set (796 cases) by stratified sampling in 5:3:2 radio. After normalization of CRs, the convolution base of pre-trained CNN (VGG16) model on ImageNet dataset was used to extract features, and the grid search was used to determine the optimal classifier module, which was added to the convolution base for transfer learning. After the training, the model with the highest accuracy of the validation set was selected as the optimal model to verify in the test set and calculate the accuracy of the model. RESULTS The accuracy of validation set in the 63rd epochs was the highest, which was 0.9430, and the corresponding Categorical crossentropy was 0.1742. The accuracy of the training set was 0.9428, and the Categorical crossentropy was 0.1545. When the optimal model was applied to the test set, the accuracy was 0.9447, and the Categorical crossentropy was 0.1929. CONCLUSION The transfer learning-based CNN model has good classification performance in the diagnosis of APTB, CAP and normal patients using CRs.
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Affiliation(s)
- Dong Han
- Department of Radiology (D.H., T.H., Y.Y., H.D., N.Y.), Affiliated Hospital of Shaanxi University of Chinese Medicine, Weiyang west Rd, Xianyang, Shaanxi 712000, China; College of Medical Technology (T.H., Y.Y.), Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China; Department of Medical Image (Y.G.), The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China; School of Information Science &Technology(Y.C.), Northwest University, Xi'an, Shaanxi, China
| | - Taiping He
- Department of Radiology (D.H., T.H., Y.Y., H.D., N.Y.), Affiliated Hospital of Shaanxi University of Chinese Medicine, Weiyang west Rd, Xianyang, Shaanxi 712000, China; College of Medical Technology (T.H., Y.Y.), Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China; Department of Medical Image (Y.G.), The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China; School of Information Science &Technology(Y.C.), Northwest University, Xi'an, Shaanxi, China
| | - Yong Yu
- Department of Radiology (D.H., T.H., Y.Y., H.D., N.Y.), Affiliated Hospital of Shaanxi University of Chinese Medicine, Weiyang west Rd, Xianyang, Shaanxi 712000, China; College of Medical Technology (T.H., Y.Y.), Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China; Department of Medical Image (Y.G.), The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China; School of Information Science &Technology(Y.C.), Northwest University, Xi'an, Shaanxi, China
| | - Youmin Guo
- Department of Radiology (D.H., T.H., Y.Y., H.D., N.Y.), Affiliated Hospital of Shaanxi University of Chinese Medicine, Weiyang west Rd, Xianyang, Shaanxi 712000, China; College of Medical Technology (T.H., Y.Y.), Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China; Department of Medical Image (Y.G.), The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China; School of Information Science &Technology(Y.C.), Northwest University, Xi'an, Shaanxi, China
| | - Yibing Chen
- Department of Radiology (D.H., T.H., Y.Y., H.D., N.Y.), Affiliated Hospital of Shaanxi University of Chinese Medicine, Weiyang west Rd, Xianyang, Shaanxi 712000, China; College of Medical Technology (T.H., Y.Y.), Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China; Department of Medical Image (Y.G.), The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China; School of Information Science &Technology(Y.C.), Northwest University, Xi'an, Shaanxi, China
| | - Haifeng Duan
- Department of Radiology (D.H., T.H., Y.Y., H.D., N.Y.), Affiliated Hospital of Shaanxi University of Chinese Medicine, Weiyang west Rd, Xianyang, Shaanxi 712000, China; College of Medical Technology (T.H., Y.Y.), Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China; Department of Medical Image (Y.G.), The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China; School of Information Science &Technology(Y.C.), Northwest University, Xi'an, Shaanxi, China
| | - Nan Yu
- Department of Radiology (D.H., T.H., Y.Y., H.D., N.Y.), Affiliated Hospital of Shaanxi University of Chinese Medicine, Weiyang west Rd, Xianyang, Shaanxi 712000, China; College of Medical Technology (T.H., Y.Y.), Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China; Department of Medical Image (Y.G.), The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China; School of Information Science &Technology(Y.C.), Northwest University, Xi'an, Shaanxi, China.
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21
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Mallon DH, McNamara CD, Rahmani GS, O'Regan DP, Amiras DG. Automated detection of enteric tubes misplaced in the respiratory tract on chest radiographs using deep learning with two centre validation. Clin Radiol 2022; 77:e758-e764. [PMID: 35850868 DOI: 10.1016/j.crad.2022.06.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/25/2022] [Accepted: 06/17/2022] [Indexed: 11/27/2022]
Abstract
AIM To develop and test a model based on a convolutional neural network that can identify enteric tube position accurately on chest radiography. MATERIALS AND METHODS The chest radiographs of adult patients were classified by radiologists based on enteric tube position as either critically misplaced (within the respiratory tract) or not critically misplaced (misplaced within the oesophagus or safely positioned below the diaphragm). A deep-learning model based on the 121-layer DenseNet architecture was developed using a training and validation set of 4,693 chest radiographs. The model was evaluated on an external test data set from a separate institution that consisted of 1,514 consecutive radiographs with a real-world incidence of critically misplaced enteric tubes. RESULTS The receiver operator characteristic area under the curve was 0.90 and 0.92 for the internal validation and external test data sets, respectively. For the external data set with a prevalence of 4.4% of critically misplaced enteric tubes, the model achieved high accuracy (92%), sensitivity (80%), and specificity (92%) for identifying a critically misplaced enteric tube. The negative predictive value (99%) was higher than the positive predictive value (32%). CONCLUSION The present study describes the development and external testing of a model that accurately identifies an enteric tube misplaced within the respiratory tract. This model could help reduce the risk of the catastrophic consequences of feeding through a misplaced enteric tube.
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Affiliation(s)
- D H Mallon
- Imperial College Healthcare NHS Trust, London, UK; MRC London Institute of Medical Sciences, Imperial College London, London, UK.
| | - C D McNamara
- Imperial College Healthcare NHS Trust, London, UK
| | - G S Rahmani
- Galway University Hospitals, Galway, Ireland
| | - D P O'Regan
- Imperial College Healthcare NHS Trust, London, UK; MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - D G Amiras
- Imperial College Healthcare NHS Trust, London, UK
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22
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Yousefzadeh M, Hasanpour M, Zolghadri M, Salimi F, Yektaeian Vaziri A, Mahmoudi Aqeel Abadi A, Jafari R, Esfahanian P, Nazem-Zadeh MR. Deep learning framework for prediction of infection severity of COVID-19. Front Med (Lausanne) 2022; 9:940960. [PMID: 36059818 PMCID: PMC9428758 DOI: 10.3389/fmed.2022.940960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 07/15/2022] [Indexed: 11/13/2022] Open
Abstract
With the onset of the COVID-19 pandemic, quantifying the condition of positively diagnosed patients is of paramount importance. Chest CT scans can be used to measure the severity of a lung infection and the isolate involvement sites in order to increase awareness of a patient's disease progression. In this work, we developed a deep learning framework for lung infection severity prediction. To this end, we collected a dataset of 232 chest CT scans and involved two public datasets with an additional 59 scans for our model's training and used two external test sets with 21 scans for evaluation. On an input chest Computer Tomography (CT) scan, our framework, in parallel, performs a lung lobe segmentation utilizing a pre-trained model and infection segmentation using three distinct trained SE-ResNet18 based U-Net models, one for each of the axial, coronal, and sagittal views. By having the lobe and infection segmentation masks, we calculate the infection severity percentage in each lobe and classify that percentage into 6 categories of infection severity score using a k-nearest neighbors (k-NN) model. The lobe segmentation model achieved a Dice Similarity Score (DSC) in the range of [0.918, 0.981] for different lung lobes and our infection segmentation models gained DSC scores of 0.7254 and 0.7105 on our two test sets, respectfully. Similarly, two resident radiologists were assigned the same infection segmentation tasks, for which they obtained a DSC score of 0.7281 and 0.6693 on the two test sets. At last, performance on infection severity score over the entire test datasets was calculated, for which the framework's resulted in a Mean Absolute Error (MAE) of 0.505 ± 0.029, while the resident radiologists' was 0.571 ± 0.039.
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Affiliation(s)
- Mehdi Yousefzadeh
- Department of Physics, Shahid Beheshti University, Tehran, Iran
- School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Masoud Hasanpour
- Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
| | - Mozhdeh Zolghadri
- Department of Medical Physics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Fatemeh Salimi
- Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
| | - Ava Yektaeian Vaziri
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Abolfazl Mahmoudi Aqeel Abadi
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Ramezan Jafari
- Department of Radiology, Health Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Parsa Esfahanian
- School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Mohammad-Reza Nazem-Zadeh
- Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- *Correspondence: Mohammad-Reza Nazem-Zadeh
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23
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Orjuela-Cañón AD, Jutinico AL, Awad C, Vergara E, Palencia A. Machine learning in the loop for tuberculosis diagnosis support. Front Public Health 2022; 10:876949. [PMID: 35958865 PMCID: PMC9362992 DOI: 10.3389/fpubh.2022.876949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 06/30/2022] [Indexed: 11/13/2022] Open
Abstract
The use of machine learning (ML) for diagnosis support has advanced in the field of health. In the present paper, the results of studying ML techniques in a tuberculosis diagnosis loop in a scenario of limited resources are presented. Data are analyzed using a tuberculosis (TB) therapy program at a health institution in a main city of a developing country using five ML models. Logistic regression, classification trees, random forest, support vector machines, and artificial neural networks are trained under physician supervision following physicians' typical daily work. The models are trained on seven main variables collected when patients arrive at the facility. Additionally, the variables applied to train the models are analyzed, and the models' advantages and limitations are discussed in the context of the automated ML techniques. The results show that artificial neural networks obtain the best results in terms of accuracy, sensitivity, and area under the receiver operating curve. These results represent an improvement over smear microscopy, which is commonly used techniques to detect TB for special cases. Findings demonstrate that ML in the TB diagnosis loop can be reinforced with available data to serve as an alternative diagnosis tool based on data processing in places where the health infrastructure is limited.
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Affiliation(s)
- Alvaro D. Orjuela-Cañón
- School of Medicine and Health Sciences, Universidad del Rosario, Bogotá, Colombia
- *Correspondence: Alvaro D. Orjuela-Cañón
| | | | - Carlos Awad
- Subred Integrada de Servicios de Salud Centro Oriente E.S.E, Bogotá, Colombia
| | - Erika Vergara
- Biomedical Engineering, Universidad Antonio Nariño, Bogotá, Colombia
| | - Angélica Palencia
- Subred Integrada de Servicios de Salud Centro Oriente E.S.E, Bogotá, Colombia
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24
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Goncalves S, Fong PC, Blokhina M. Artificial intelligence for early diagnosis of lung cancer through incidental nodule detection in low- and middle-income countries-acceleration during the COVID-19 pandemic but here to stay. Am J Cancer Res 2022; 12:1-16. [PMID: 35141002 PMCID: PMC8822269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 11/27/2021] [Indexed: 06/14/2023] Open
Abstract
Although the coronavirus disease of 2019 (COVID-19) pandemic had profound pernicious effects, it revealed deficiencies in health systems, particularly among low- and middle-income countries (LMICs). With increasing uncertainty in healthcare, existing unmet needs such as poor outcomes of lung cancer (LC) patients in LMICs, mainly due to late stages at diagnosis, have been challenging-necessitating a shift in focus for judicious health resource utilization. Leveraging artificial intelligence (AI) for screening large volumes of pulmonary images performed for noncancerous reasons, such as health checks, immigration, tuberculosis screening, or other lung conditions, including but not limited to COVID-19, can facilitate easy and early identification of incidental pulmonary nodules (IPNs), which otherwise could have been missed. AI can review every chest X-ray or computed tomography scan through a trained pair of eyes, thus strengthening the infrastructure and enhancing capabilities of manpower for interpreting images in LMICs for streamlining accurate and early identification of IPNs. AI can be a catalyst for driving LC screening with enhanced efficiency, particularly in primary care settings, for timely referral and adequate management of coincidental IPN. AI can facilitate shift in the stage of LC diagnosis for improving survival, thus fostering optimal health-resource utilization and sustainable healthcare systems resilient to crisis. This article highlights the challenges for organized LC screening in LMICs and describes unique opportunities for leveraging AI. We present pilot initiatives from Asia, Latin America, and Russia illustrating AI-supported IPN identification from routine imaging to facilitate early diagnosis of LC at a potentially curable stage.
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Affiliation(s)
- Susana Goncalves
- Medical Director, AstraZeneca LatAm AreaNicolás de Vedia 3616, 8° Piso (C1430DAH) CABA, República Argentina
| | - Pei-Chieh Fong
- Head of Oncology, International MedicalAstraZeneca 21st Fl., 207, Tun Hwa South Road, Sec. 2, Taipei 10602, Taiwan
| | - Mariya Blokhina
- Therapeutic Area Lead, AstraZeneca1st Krasnogvardeyskiy Proezd 21, Building 1, Moscow 123100, Russian Federation
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25
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Jones CM, Danaher L, Milne MR, Tang C, Seah J, Oakden-Rayner L, Johnson A, Buchlak QD, Esmaili N. Assessment of the effect of a comprehensive chest radiograph deep learning model on radiologist reports and patient outcomes: a real-world observational study. BMJ Open 2021; 11:e052902. [PMID: 34930738 PMCID: PMC8689166 DOI: 10.1136/bmjopen-2021-052902] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
OBJECTIVES Artificial intelligence (AI) algorithms have been developed to detect imaging features on chest X-ray (CXR) with a comprehensive AI model capable of detecting 124 CXR findings being recently developed. The aim of this study was to evaluate the real-world usefulness of the model as a diagnostic assistance device for radiologists. DESIGN This prospective real-world multicentre study involved a group of radiologists using the model in their daily reporting workflow to report consecutive CXRs and recording their feedback on level of agreement with the model findings and whether this significantly affected their reporting. SETTING The study took place at radiology clinics and hospitals within a large radiology network in Australia between November and December 2020. PARTICIPANTS Eleven consultant diagnostic radiologists of varying levels of experience participated in this study. PRIMARY AND SECONDARY OUTCOME MEASURES Proportion of CXR cases where use of the AI model led to significant material changes to the radiologist report, to patient management, or to imaging recommendations. Additionally, level of agreement between radiologists and the model findings, and radiologist attitudes towards the model were assessed. RESULTS Of 2972 cases reviewed with the model, 92 cases (3.1%) had significant report changes, 43 cases (1.4%) had changed patient management and 29 cases (1.0%) had further imaging recommendations. In terms of agreement with the model, 2569 cases showed complete agreement (86.5%). 390 (13%) cases had one or more findings rejected by the radiologist. There were 16 findings across 13 cases (0.5%) deemed to be missed by the model. Nine out of 10 radiologists felt their accuracy was improved with the model and were more positive towards AI poststudy. CONCLUSIONS Use of an AI model in a real-world reporting environment significantly improved radiologist reporting and showed good agreement with radiologists, highlighting the potential for AI diagnostic support to improve clinical practice.
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Affiliation(s)
- Catherine M Jones
- Annalise-AI, Sydney, New South Wales, Australia
- I-Med Radiology Network, Sydney, New South Wales, Australia
| | - Luke Danaher
- I-Med Radiology Network, Sydney, New South Wales, Australia
| | - Michael R Milne
- Annalise-AI, Sydney, New South Wales, Australia
- I-Med Radiology Network, Sydney, New South Wales, Australia
| | - Cyril Tang
- Annalise-AI, Sydney, New South Wales, Australia
| | - Jarrel Seah
- Annalise-AI, Sydney, New South Wales, Australia
- Department of Radiology, Alfred Health, Melbourne, Victoria, Australia
| | - Luke Oakden-Rayner
- Australian Institute for Machine Learning, The University of Adelaide, Adelaide, South Australia, Australia
| | | | - Quinlan D Buchlak
- Annalise-AI, Sydney, New South Wales, Australia
- School of Medicine, The University of Notre Dame Australia School of Medicine Sydney Campus, Darlinghurst, New South Wales, Australia
| | - Nazanin Esmaili
- School of Medicine, The University of Notre Dame Australia School of Medicine Sydney Campus, Darlinghurst, New South Wales, Australia
- Faculty of Engineering and IT, University of Technology Sydney, Sydney, New South Wales, Australia
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Independent evaluation of 12 artificial intelligence solutions for the detection of tuberculosis. Sci Rep 2021; 11:23895. [PMID: 34903808 PMCID: PMC8668935 DOI: 10.1038/s41598-021-03265-0] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 11/30/2021] [Indexed: 11/13/2022] Open
Abstract
There have been few independent evaluations of computer-aided detection (CAD) software for tuberculosis (TB) screening, despite the rapidly expanding array of available CAD solutions. We developed a test library of chest X-ray (CXR) images which was blindly re-read by two TB clinicians with different levels of experience and then processed by 12 CAD software solutions. Using Xpert MTB/RIF results as the reference standard, we compared the performance characteristics of each CAD software against both an Expert and Intermediate Reader, using cut-off thresholds which were selected to match the sensitivity of each human reader. Six CAD systems performed on par with the Expert Reader (Qure.ai, DeepTek, Delft Imaging, JF Healthcare, OXIPIT, and Lunit) and one additional software (Infervision) performed on par with the Intermediate Reader only. Qure.ai, Delft Imaging and Lunit were the only software to perform significantly better than the Intermediate Reader. The majority of these CAD software showed significantly lower performance among participants with a past history of TB. The radiography equipment used to capture the CXR image was also shown to affect performance for some CAD software. TB program implementers now have a wide selection of quality CAD software solutions to utilize in their CXR screening initiatives.
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Yamaguchi S, Soyama A, Ono S, Hamauzu S, Yamada M, Fukuda T, Hidaka M, Tsurumoto T, Uetani M, Eguchi S. Novel Computer-Aided Diagnosis Software for the Prevention of Retained Surgical Items. J Am Coll Surg 2021; 233:686-696. [PMID: 34592404 DOI: 10.1016/j.jamcollsurg.2021.08.689] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 08/23/2021] [Accepted: 08/23/2021] [Indexed: 12/22/2022]
Abstract
BACKGROUND Retained surgical items are a serious human error. Surgical sponges account for 70% of retained surgical items. To prevent retained surgical sponges, it is important to establish a system that can identify errors and avoid the occurrence of adverse events. To date, no computer-aided diagnosis software specialized for detecting retained surgical sponges has been reported. We developed a software program that enables easy and effective computer-aided diagnosis of retained surgical sponges with high sensitivity and specificity using the technique of deep learning, a subfield of artificial intelligence. STUDY DESIGN In this study, we developed the software by training it through deep learning using a dataset and then validating the software. The dataset consisted of a training set and validation set. We created composite x-rays consisting of normal postoperative x-rays and surgical sponge x-rays for a training set (n = 4,554) and a validation set (n = 470). Phantom x-rays (n = 12) were prepared for software validation. X-rays obtained with surgical sponges inserted into cadavers were used for validation purposes (formalin: Thiel's method = 252:117). In addition, postoperative x-rays without retained surgical sponges were used for the validation of software performance to determine false-positive rates. Sensitivity, specificity, and false positives per image were calculated. RESULTS In the phantom x-rays, both the sensitivity and specificity in software image interpretation were 100%. The software achieved 97.7% sensitivity and 83.8% specificity in the composite x-rays. In the normal postoperative x-rays, 86.6% specificity was achieved. In reading the cadaveric x-rays, the software attained both sensitivity and specificity of >90%. CONCLUSIONS Software with high sensitivity for diagnosis of retained surgical sponges was developed successfully.
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Affiliation(s)
- Shun Yamaguchi
- Department of Surgery, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Akihiko Soyama
- Department of Surgery, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Shinichiro Ono
- Department of Digestive and General Surgery, Graduate School of Medicine, University of the Ryukyus, Nishihara, Japan
| | - Shin Hamauzu
- Imaging Technology Center, Research and Development Management Headquarters, FUJIFILM Corporation, Tokyo, Japan
| | - Masahiko Yamada
- Imaging Technology Center, Research and Development Management Headquarters, FUJIFILM Corporation, Tokyo, Japan
| | - Toru Fukuda
- Department of Radiology, Nagasaki University Hospital
| | - Masaaki Hidaka
- Department of Surgery, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan; Department of Radiological Sciences, Nagasaki University Graduate School of Biomedical Sciences
| | - Toshiyuki Tsurumoto
- Department of Macroscopic Anatomy, Nagasaki University Graduate School of Biomedical Sciences
| | - Masataka Uetani
- Department of Radiological Sciences, Nagasaki University Graduate School of Biomedical Sciences
| | - Susumu Eguchi
- Department of Surgery, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan.
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Qin ZZ, Ahmed S, Sarker MS, Paul K, Adel ASS, Naheyan T, Barrett R, Banu S, Creswell J. Tuberculosis detection from chest x-rays for triaging in a high tuberculosis-burden setting: an evaluation of five artificial intelligence algorithms. LANCET DIGITAL HEALTH 2021; 3:e543-e554. [PMID: 34446265 DOI: 10.1016/s2589-7500(21)00116-3] [Citation(s) in RCA: 81] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 05/07/2021] [Accepted: 06/07/2021] [Indexed: 12/22/2022]
Abstract
BACKGROUND Artificial intelligence (AI) algorithms can be trained to recognise tuberculosis-related abnormalities on chest radiographs. Various AI algorithms are available commercially, yet there is little impartial evidence on how their performance compares with each other and with radiologists. We aimed to evaluate five commercial AI algorithms for triaging tuberculosis using a large dataset that had not previously been used to train any AI algorithms. METHODS Individuals aged 15 years or older presenting or referred to three tuberculosis screening centres in Dhaka, Bangladesh, between May 15, 2014, and Oct 4, 2016, were recruited consecutively. Every participant was verbally screened for symptoms and received a digital posterior-anterior chest x-ray and an Xpert MTB/RIF (Xpert) test. All chest x-rays were read independently by a group of three registered radiologists and five commercial AI algorithms: CAD4TB (version 7), InferRead DR (version 2), Lunit INSIGHT CXR (version 4.9.0), JF CXR-1 (version 2), and qXR (version 3). We compared the performance of the AI algorithms with each other, with the radiologists, and with the WHO's Target Product Profile (TPP) of triage tests (≥90% sensitivity and ≥70% specificity). We used a new evaluation framework that simultaneously evaluates sensitivity, proportion of Xpert tests avoided, and number needed to test to inform implementers' choice of software and selection of threshold abnormality scores. FINDINGS Chest x-rays from 23 954 individuals were included in the analysis. All five AI algorithms significantly outperformed the radiologists. The areas under the receiver operating characteristic curve were 90·81% (95% CI 90·33-91·29) for qXR, 90·34% (89·81-90·87) for CAD4TB, 88·61% (88·03-89·20) for Lunit INSIGHT CXR, 84·90% (84·27-85·54) for InferRead DR, and 84·89% (84·26-85·53) for JF CXR-1. Only qXR (74·3% specificity [95% CI 73·3-74·9]) and CAD4TB (72·9% specificity [72·3-73·5]) met the TPP at 90% sensitivity. All five AI algorithms reduced the number of Xpert tests required by 50% while maintaining a sensitivity above 90%. All AI algorithms performed worse among older age groups (>60 years) and people with a history of tuberculosis. INTERPRETATION AI algorithms can be highly accurate and useful triage tools for tuberculosis detection in high-burden regions, and outperform human readers. FUNDING Government of Canada.
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Affiliation(s)
| | - Shahriar Ahmed
- International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh
| | | | - Kishor Paul
- International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh
| | | | | | | | - Sayera Banu
- International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh
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Seah JCY, Tang CHM, Buchlak QD, Holt XG, Wardman JB, Aimoldin A, Esmaili N, Ahmad H, Pham H, Lambert JF, Hachey B, Hogg SJF, Johnston BP, Bennett C, Oakden-Rayner L, Brotchie P, Jones CM. Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study. Lancet Digit Health 2021; 3:e496-e506. [PMID: 34219054 DOI: 10.1016/s2589-7500(21)00106-0] [Citation(s) in RCA: 86] [Impact Index Per Article: 28.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 05/02/2021] [Accepted: 05/12/2021] [Indexed: 02/01/2023]
Abstract
BACKGROUND Chest x-rays are widely used in clinical practice; however, interpretation can be hindered by human error and a lack of experienced thoracic radiologists. Deep learning has the potential to improve the accuracy of chest x-ray interpretation. We therefore aimed to assess the accuracy of radiologists with and without the assistance of a deep-learning model. METHODS In this retrospective study, a deep-learning model was trained on 821 681 images (284 649 patients) from five data sets from Australia, Europe, and the USA. 2568 enriched chest x-ray cases from adult patients (≥16 years) who had at least one frontal chest x-ray were included in the test dataset; cases were representative of inpatient, outpatient, and emergency settings. 20 radiologists reviewed cases with and without the assistance of the deep-learning model with a 3-month washout period. We assessed the change in accuracy of chest x-ray interpretation across 127 clinical findings when the deep-learning model was used as a decision support by calculating area under the receiver operating characteristic curve (AUC) for each radiologist with and without the deep-learning model. We also compared AUCs for the model alone with those of unassisted radiologists. If the lower bound of the adjusted 95% CI of the difference in AUC between the model and the unassisted radiologists was more than -0·05, the model was considered to be non-inferior for that finding. If the lower bound exceeded 0, the model was considered to be superior. FINDINGS Unassisted radiologists had a macroaveraged AUC of 0·713 (95% CI 0·645-0·785) across the 127 clinical findings, compared with 0·808 (0·763-0·839) when assisted by the model. The deep-learning model statistically significantly improved the classification accuracy of radiologists for 102 (80%) of 127 clinical findings, was statistically non-inferior for 19 (15%) findings, and no findings showed a decrease in accuracy when radiologists used the deep-learning model. Unassisted radiologists had a macroaveraged mean AUC of 0·713 (0·645-0·785) across all findings, compared with 0·957 (0·954-0·959) for the model alone. Model classification alone was significantly more accurate than unassisted radiologists for 117 (94%) of 124 clinical findings predicted by the model and was non-inferior to unassisted radiologists for all other clinical findings. INTERPRETATION This study shows the potential of a comprehensive deep-learning model to improve chest x-ray interpretation across a large breadth of clinical practice. FUNDING Annalise.ai.
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Affiliation(s)
- Jarrel C Y Seah
- Annalise.ai, Sydney, NSW, Australia; Department of Radiology, Alfred Health, Melbourne, VIC, Australia
| | | | | | | | | | | | - Nazanin Esmaili
- School of Medicine, University of Notre Dame Australia, Sydney, NSW, Australia; Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW, Australia
| | | | | | | | | | | | | | - Christine Bennett
- School of Medicine, University of Notre Dame Australia, Sydney, NSW, Australia
| | - Luke Oakden-Rayner
- Australian Institute for Machine Learning, The University of Adelaide, Adelaide, SA, Australia
| | - Peter Brotchie
- Annalise.ai, Sydney, NSW, Australia; Department of Radiology, St Vincent's Health Australia, Melbourne, VIC, Australia
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Çallı E, Sogancioglu E, van Ginneken B, van Leeuwen KG, Murphy K. Deep learning for chest X-ray analysis: A survey. Med Image Anal 2021; 72:102125. [PMID: 34171622 DOI: 10.1016/j.media.2021.102125] [Citation(s) in RCA: 98] [Impact Index Per Article: 32.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 05/17/2021] [Accepted: 05/27/2021] [Indexed: 12/14/2022]
Abstract
Recent advances in deep learning have led to a promising performance in many medical image analysis tasks. As the most commonly performed radiological exam, chest radiographs are a particularly important modality for which a variety of applications have been researched. The release of multiple, large, publicly available chest X-ray datasets in recent years has encouraged research interest and boosted the number of publications. In this paper, we review all studies using deep learning on chest radiographs published before March 2021, categorizing works by task: image-level prediction (classification and regression), segmentation, localization, image generation and domain adaptation. Detailed descriptions of all publicly available datasets are included and commercial systems in the field are described. A comprehensive discussion of the current state of the art is provided, including caveats on the use of public datasets, the requirements of clinically useful systems and gaps in the current literature.
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Affiliation(s)
- Erdi Çallı
- Radboud University Medical Center, Institute for Health Sciences, Department of Medical Imaging, Nijmegen, the Netherlands.
| | - Ecem Sogancioglu
- Radboud University Medical Center, Institute for Health Sciences, Department of Medical Imaging, Nijmegen, the Netherlands
| | - Bram van Ginneken
- Radboud University Medical Center, Institute for Health Sciences, Department of Medical Imaging, Nijmegen, the Netherlands
| | - Kicky G van Leeuwen
- Radboud University Medical Center, Institute for Health Sciences, Department of Medical Imaging, Nijmegen, the Netherlands
| | - Keelin Murphy
- Radboud University Medical Center, Institute for Health Sciences, Department of Medical Imaging, Nijmegen, the Netherlands
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Cao XF, Li Y, Xin HN, Zhang HR, Pai M, Gao L. Application of artificial intelligence in digital chest radiography reading for pulmonary tuberculosis screening. Chronic Dis Transl Med 2021; 7:35-40. [PMID: 34013178 PMCID: PMC8110935 DOI: 10.1016/j.cdtm.2021.02.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Indexed: 12/18/2022] Open
Abstract
Currently, the diagnosis of tuberculosis (TB) is mainly based on the comprehensive consideration of the patient's symptoms and signs, laboratory examinations and chest radiography (CXR). CXR plays a pivotal role to support the early diagnosis of TB, especially when used for TB screening and differential diagnosis. However, high cost of CXR hardware and shortage of certified radiologists poses a major challenge for CXR application in TB screening in resource limited settings. The latest development of artificial intelligence (AI) combined with the accumulation of a large number of medical images provides new opportunities for the establishment of computer-aided detection (CAD) systems in the medical applications, especially in the era of deep learning (DL) technology. Several CAD solutions are now commercially available and there is growing evidence demonstrate their value in imaging diagnosis. Recently, WHO published a rapid communication which stated that CAD may be used as an alternative to human reader interpretation of plain digital CXRs for screening and triage of TB.
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Affiliation(s)
- Xue-Fang Cao
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, And Center for Tuberculosis Research, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Yuan Li
- JF Healthcare, Nanchang, Jiangxi 330072, China
| | - He-Nan Xin
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, And Center for Tuberculosis Research, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Hao-Ran Zhang
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, And Center for Tuberculosis Research, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Madhukar Pai
- McGill International TB Centre, McGill University, Montreal, Canada
| | - Lei Gao
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, And Center for Tuberculosis Research, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
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Qin ZZ, Naheyan T, Ruhwald M, Denkinger CM, Gelaw S, Nash M, Creswell J, Kik SV. A new resource on artificial intelligence powered computer automated detection software products for tuberculosis programmes and implementers. Tuberculosis (Edinb) 2021; 127:102049. [PMID: 33440315 DOI: 10.1016/j.tube.2020.102049] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 12/22/2020] [Accepted: 12/27/2020] [Indexed: 01/25/2023]
Abstract
Recently, the number of artificial intelligence powered computer-aided detection (CAD) products that detect tuberculosis (TB)-related abnormalities from chest X-rays (CXR) available on the market has increased. Although CXR is a relatively effective and inexpensive method for TB screening and triaging, a shortage of skilled radiologists in many high TB-burden countries limits its use. CAD technology offers a solution to this problem. Before adopting a CAD product, TB programmes need to consider not only the diagnostic accuracy but also implementation-relevant features including operational characteristics, deployment mechanism, input and machine compatibility, output format, options for integration into the legacy system, costs, data sharing and privacy aspects, and certification. A landscaping analysis was conducted to collect this information among CAD developers known to have or soon to have a TB product. The responses were reviewed and finalized with the developers, and are published on an open-access website: www.ai4hlth.org. CAD products are constantly being improved and the site will continuously be updated to account for updates and new products. This unique online resource aims to inform the TB community about available CAD tools, their features and set-up procedures, to enable TB programmes to identify the most suitable product to incorporate in interventions.
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Affiliation(s)
- Zhi Zhen Qin
- Stop TB Partnership, Chemin Du Pommier 40, Le Grand-Saconnex, Geneva, 1218, Switzerland.
| | - Tasneem Naheyan
- Stop TB Partnership, Chemin Du Pommier 40, Le Grand-Saconnex, Geneva, 1218, Switzerland
| | - Morten Ruhwald
- Foundation for Innovative New Diagnostics, Chemin des Mines, Geneva, 1201, Switzerland
| | - Claudia M Denkinger
- Foundation for Innovative New Diagnostics, Chemin des Mines, Geneva, 1201, Switzerland; Division of Tropical Medicine, Center of Infectious Diseases, University Hospital Heidelberg, Heidelberg, Im Neuenheimer Feld 324, Heidelberg, 69120, Germany
| | - Sifrash Gelaw
- International Organization for Migration, Global Teleradiology and QC Center, Migration Health Division, Manila (Global) Administrative Center, 24F Citibank Tower, 8741 Paseo de Roxas, Makati City, Metro Manila, 1226, Philippines
| | - Madlen Nash
- McGill International TB Centre, 1001 Boulevard Décarie, Montréal, QC H4A 3J1, Canada
| | - Jacob Creswell
- Stop TB Partnership, Chemin Du Pommier 40, Le Grand-Saconnex, Geneva, 1218, Switzerland
| | - Sandra Vivian Kik
- Foundation for Innovative New Diagnostics, Chemin des Mines, Geneva, 1201, Switzerland.
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Owais M, Arsalan M, Mahmood T, Kim YH, Park KR. Comprehensive Computer-Aided Decision Support Framework to Diagnose Tuberculosis From Chest X-Ray Images: Data Mining Study. JMIR Med Inform 2020; 8:e21790. [PMID: 33284119 PMCID: PMC7752539 DOI: 10.2196/21790] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 11/05/2020] [Accepted: 11/09/2020] [Indexed: 12/29/2022] Open
Abstract
Background Tuberculosis (TB) is one of the most infectious diseases that can be fatal. Its early diagnosis and treatment can significantly reduce the mortality rate. In the literature, several computer-aided diagnosis (CAD) tools have been proposed for the efficient diagnosis of TB from chest radiograph (CXR) images. However, the majority of previous studies adopted conventional handcrafted feature-based algorithms. In addition, some recent CAD tools utilized the strength of deep learning methods to further enhance diagnostic performance. Nevertheless, all these existing methods can only classify a given CXR image into binary class (either TB positive or TB negative) without providing further descriptive information. Objective The main objective of this study is to propose a comprehensive CAD framework for the effective diagnosis of TB by providing visual as well as descriptive information from the previous patients’ database. Methods To accomplish our objective, first we propose a fusion-based deep classification network for the CAD decision that exhibits promising performance over the various state-of-the-art methods. Furthermore, a multilevel similarity measure algorithm is devised based on multiscale information fusion to retrieve the best-matched cases from the previous database. Results The performance of the framework was evaluated based on 2 well-known CXR data sets made available by the US National Library of Medicine and the National Institutes of Health. Our classification model exhibited the best diagnostic performance (0.929, 0.937, 0.921, 0.928, and 0.965 for F1 score, average precision, average recall, accuracy, and area under the curve, respectively) and outperforms the performance of various state-of-the-art methods. Conclusions This paper presents a comprehensive CAD framework to diagnose TB from CXR images by retrieving the relevant cases and their clinical observations from the previous patients’ database. These retrieval results assist the radiologist in making an effective diagnostic decision related to the current medical condition of a patient. Moreover, the retrieval results can facilitate the radiologists in subjectively validating the CAD decision.
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Affiliation(s)
- Muhammad Owais
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Republic of Korea
| | - Muhammad Arsalan
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Republic of Korea
| | - Tahir Mahmood
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Republic of Korea
| | - Yu Hwan Kim
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Republic of Korea
| | - Kang Ryoung Park
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Republic of Korea
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Behar JA, Liu C, Kotzen K, Tsutsui K, Corino VDA, Singh J, Pimentel MAF, Warrick P, Zaunseder S, Andreotti F, Sebag D, Kopanitsa G, McSharry PE, Karlen W, Karmakar C, Clifford GD. Remote health diagnosis and monitoring in the time of COVID-19. Physiol Meas 2020; 41:10TR01. [PMID: 32947271 DOI: 10.1088/1361-6579/abba0a] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is rapidly spreading across the globe. The clinical spectrum of SARS-CoV-2 pneumonia requires early detection and monitoring, within a clinical environment for critical cases and remotely for mild cases, with a large spectrum of symptoms. The fear of contamination in clinical environments has led to a dramatic reduction in on-site referrals for routine care. There has also been a perceived need to continuously monitor non-severe COVID-19 patients, either from their quarantine site at home, or dedicated quarantine locations (e.g. hotels). In particular, facilitating contact tracing with proximity and location tracing apps was adopted in many countries very rapidly. Thus, the pandemic has driven incentives to innovate and enhance or create new routes for providing healthcare services at distance. In particular, this has created a dramatic impetus to find innovative ways to remotely and effectively monitor patient health status. In this paper, we present a review of remote health monitoring initiatives taken in 20 states during the time of the pandemic. We emphasize in the discussion particular aspects that are common ground for the reviewed states, in particular the future impact of the pandemic on remote health monitoring and consideration on data privacy.
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Affiliation(s)
- Joachim A Behar
- Faculty of Biomedical Engineering, Technion-IIT, Haifa, Israel
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Jin C, Chen W, Cao Y, Xu Z, Tan Z, Zhang X, Deng L, Zheng C, Zhou J, Shi H, Feng J. Development and evaluation of an artificial intelligence system for COVID-19 diagnosis. Nat Commun 2020; 11:5088. [PMID: 33037212 PMCID: PMC7547659 DOI: 10.1038/s41467-020-18685-1] [Citation(s) in RCA: 200] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 09/04/2020] [Indexed: 12/27/2022] Open
Abstract
Early detection of COVID-19 based on chest CT enables timely treatment of patients and helps control the spread of the disease. We proposed an artificial intelligence (AI) system for rapid COVID-19 detection and performed extensive statistical analysis of CTs of COVID-19 based on the AI system. We developed and evaluated our system on a large dataset with more than 10 thousand CT volumes from COVID-19, influenza-A/B, non-viral community acquired pneumonia (CAP) and non-pneumonia subjects. In such a difficult multi-class diagnosis task, our deep convolutional neural network-based system is able to achieve an area under the receiver operating characteristic curve (AUC) of 97.81% for multi-way classification on test cohort of 3,199 scans, AUC of 92.99% and 93.25% on two publicly available datasets, CC-CCII and MosMedData respectively. In a reader study involving five radiologists, the AI system outperforms all of radiologists in more challenging tasks at a speed of two orders of magnitude above them. Diagnosis performance of chest x-ray (CXR) is compared to that of CT. Detailed interpretation of deep network is also performed to relate system outputs with CT presentations. The code is available at https://github.com/ChenWWWeixiang/diagnosis_covid19 .
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Affiliation(s)
- Cheng Jin
- Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China
| | - Weixiang Chen
- Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China
| | - Yukun Cao
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Zhanwei Xu
- Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China
| | - Zimeng Tan
- Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China
| | - Xin Zhang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Lei Deng
- Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China
| | - Chuansheng Zheng
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Jie Zhou
- Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China
| | - Heshui Shi
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China.
| | - Jianjiang Feng
- Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China.
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Jin C, Chen W, Cao Y, Xu Z, Tan Z, Zhang X, Deng L, Zheng C, Zhou J, Shi H, Feng J. Development and evaluation of an artificial intelligence system for COVID-19 diagnosis. Nat Commun 2020; 11:5088. [PMID: 33037212 DOI: 10.1101/2020.03.20.20039834] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 09/04/2020] [Indexed: 05/23/2023] Open
Abstract
Early detection of COVID-19 based on chest CT enables timely treatment of patients and helps control the spread of the disease. We proposed an artificial intelligence (AI) system for rapid COVID-19 detection and performed extensive statistical analysis of CTs of COVID-19 based on the AI system. We developed and evaluated our system on a large dataset with more than 10 thousand CT volumes from COVID-19, influenza-A/B, non-viral community acquired pneumonia (CAP) and non-pneumonia subjects. In such a difficult multi-class diagnosis task, our deep convolutional neural network-based system is able to achieve an area under the receiver operating characteristic curve (AUC) of 97.81% for multi-way classification on test cohort of 3,199 scans, AUC of 92.99% and 93.25% on two publicly available datasets, CC-CCII and MosMedData respectively. In a reader study involving five radiologists, the AI system outperforms all of radiologists in more challenging tasks at a speed of two orders of magnitude above them. Diagnosis performance of chest x-ray (CXR) is compared to that of CT. Detailed interpretation of deep network is also performed to relate system outputs with CT presentations. The code is available at https://github.com/ChenWWWeixiang/diagnosis_covid19 .
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Affiliation(s)
- Cheng Jin
- Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China
| | - Weixiang Chen
- Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China
| | - Yukun Cao
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Zhanwei Xu
- Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China
| | - Zimeng Tan
- Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China
| | - Xin Zhang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Lei Deng
- Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China
| | - Chuansheng Zheng
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Jie Zhou
- Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China
| | - Heshui Shi
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China.
| | - Jianjiang Feng
- Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China.
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Jin C, Chen W, Cao Y, Xu Z, Tan Z, Zhang X, Deng L, Zheng C, Zhou J, Shi H, Feng J. Development and evaluation of an artificial intelligence system for COVID-19 diagnosis. Nat Commun 2020; 11:5088. [PMID: 33037212 DOI: 10.1101/823377] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 09/04/2020] [Indexed: 05/22/2023] Open
Abstract
Early detection of COVID-19 based on chest CT enables timely treatment of patients and helps control the spread of the disease. We proposed an artificial intelligence (AI) system for rapid COVID-19 detection and performed extensive statistical analysis of CTs of COVID-19 based on the AI system. We developed and evaluated our system on a large dataset with more than 10 thousand CT volumes from COVID-19, influenza-A/B, non-viral community acquired pneumonia (CAP) and non-pneumonia subjects. In such a difficult multi-class diagnosis task, our deep convolutional neural network-based system is able to achieve an area under the receiver operating characteristic curve (AUC) of 97.81% for multi-way classification on test cohort of 3,199 scans, AUC of 92.99% and 93.25% on two publicly available datasets, CC-CCII and MosMedData respectively. In a reader study involving five radiologists, the AI system outperforms all of radiologists in more challenging tasks at a speed of two orders of magnitude above them. Diagnosis performance of chest x-ray (CXR) is compared to that of CT. Detailed interpretation of deep network is also performed to relate system outputs with CT presentations. The code is available at https://github.com/ChenWWWeixiang/diagnosis_covid19 .
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Affiliation(s)
- Cheng Jin
- Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China
| | - Weixiang Chen
- Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China
| | - Yukun Cao
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Zhanwei Xu
- Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China
| | - Zimeng Tan
- Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China
| | - Xin Zhang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Lei Deng
- Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China
| | - Chuansheng Zheng
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Jie Zhou
- Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China
| | - Heshui Shi
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China.
| | - Jianjiang Feng
- Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China.
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