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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: 0] [Impact Index Per Article: 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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Leavitt SV, Rodriguez CA, Bouton TC, Horsburgh CR, Abel Zur Wiesch P, Nichols BE, White LF, Jenkins HE. Outcomes for people with TB by disease severity at presentation. Int J Tuberc Lung Dis 2024; 28:142-147. [PMID: 38454178 PMCID: PMC11075045 DOI: 10.5588/ijtld.23.0254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2024] Open
Abstract
BACKGROUND There is substantial heterogeneity in disease presentation for individuals with TB disease, which may correlate with disease outcomes. We estimated disease outcomes by disease severity at presentation among individuals with TB during the pre-chemotherapy era.METHODS We extracted data on people with TB enrolled between 1917 and 1948 in the USA, stratified by three disease severity categories at presentation using the U.S. National Tuberculosis Association diagnostic criteria. These criteria were based largely on radiographic findings ("minimal", "moderately advanced", and "far advanced"). We used Bayesian parametric survival analysis to model the survival distribution overall, and by disease severity and Bayesian logistic regression to estimate the severity-level specific natural recovery odds within 3 years.RESULTS People with minimal TB at presentation had a 2% (95% CrI 0-11%) probability of TB death within 5 years vs. 40% (95% CrI 15-68) for those with far advanced disease. Individuals with minimal disease had 13.62 times the odds (95% CrI 9.87-19.10) of natural recovery within 3 years vs. those with far advanced disease.CONCLUSION Mortality and natural recovery vary by disease severity at presentation. This supports continued work to evaluate individualized (e.g., shortened or longer) regimens based on disease severity at presentation, identified using radiography..
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Affiliation(s)
| | - C A Rodriguez
- Departments of Epidemiology, Boston University School of Public Health, Boston, MA
| | - T C Bouton
- Section of Infectious Diseases, Boston Medical Center, Boston, MA, Boston University School of Medicine, Boston, MA
| | - C R Horsburgh
- Departments of Biostatistics and, Departments of Epidemiology, Boston University School of Public Health, Boston, MA, Boston University School of Medicine, Boston, MA, Department of Global Health, Boston University School of Public Health, Boston, MA, USA
| | - P Abel Zur Wiesch
- Division for Infection Control and Environmental Health, Norwegian Institute of Public Health, Norway;, Center of Infectious Disease Dynamics, Pennsylvania State University, Philadelphia, PA, USA
| | - B E Nichols
- Department of Global Health, Boston University School of Public Health, Boston, MA, USA
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Scott AJ, Perumal T, Hohlfeld A, Oelofse S, Kühn L, Swanepoel J, Geric C, Ahmad Khan F, Esmail A, Ochodo E, Engel M, Dheda K. Diagnostic Accuracy of Computer-Aided Detection During Active Case Finding for Pulmonary Tuberculosis in Africa: A Systematic Review and Meta-analysis. Open Forum Infect Dis 2024; 11:ofae020. [PMID: 38328498 PMCID: PMC10849117 DOI: 10.1093/ofid/ofae020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 01/15/2024] [Indexed: 02/09/2024] Open
Abstract
Background Computer-aided detection (CAD) may be a useful screening tool for tuberculosis (TB). However, there are limited data about its utility in active case finding (ACF) in a community-based setting, and particularly in an HIV-endemic setting where performance may be compromised. Methods We performed a systematic review and evaluated articles published between January 2012 and February 2023 that included CAD as a screening tool to detect pulmonary TB against a microbiological reference standard (sputum culture and/or nucleic acid amplification test [NAAT]). We collected and summarized data on study characteristics and diagnostic accuracy measures. Two reviewers independently extracted data and assessed methodological quality against Quality Assessment of Diagnostic Accuracy Studies-2 criteria. Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Diagnostic Test Accuracy Studies (PRISMA-DTA) guidelines were followed. Results Of 1748 articles reviewed, 5 met with the eligibility criteria and were included in this review. A meta-analysis revealed pooled sensitivity of 0.87 (95% CI, 0.78-0.96) and specificity of 0.74 (95% CI, 0.55-0.93), just below the World Health Organization (WHO)-recommended target product profile (TPP) for a screening test (sensitivity ≥0.90 and specificity ≥0.70). We found a high risk of bias and applicability concerns across all studies. Subgroup analyses, including the impact of HIV and previous TB, were not possible due to the nature of the reporting within the included studies. Conclusions This review provides evidence, specifically in the context of ACF, for CAD as a potentially useful and cost-effective screening tool for TB in a resource-poor HIV-endemic African setting. However, given methodological concerns, caution is required with regards to applicability and generalizability.
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Affiliation(s)
- Alex J Scott
- Division of Pulmonology, Department of Medicine, Centre for Lung Infection and Immunity, University of Cape Town Lung Institute, Cape Town, South Africa
- Centre for the Study of Antimicrobial Resistance, South African Medical Research Council and University of Cape Town, Cape Town, South Africa
| | - Tahlia Perumal
- Division of Pulmonology, Department of Medicine, Centre for Lung Infection and Immunity, University of Cape Town Lung Institute, Cape Town, South Africa
- Centre for the Study of Antimicrobial Resistance, South African Medical Research Council and University of Cape Town, Cape Town, South Africa
| | - Ameer Hohlfeld
- Health Systems Research Unit, South African Medical Research Council, Cape Town, South Africa
| | - Suzette Oelofse
- Division of Pulmonology, Department of Medicine, Centre for Lung Infection and Immunity, University of Cape Town Lung Institute, Cape Town, South Africa
- Centre for the Study of Antimicrobial Resistance, South African Medical Research Council and University of Cape Town, Cape Town, South Africa
| | - Louié Kühn
- Division of Pulmonology, Department of Medicine, Centre for Lung Infection and Immunity, University of Cape Town Lung Institute, Cape Town, South Africa
- Centre for the Study of Antimicrobial Resistance, South African Medical Research Council and University of Cape Town, Cape Town, South Africa
| | - Jeremi Swanepoel
- Division of Pulmonology, Department of Medicine, Centre for Lung Infection and Immunity, University of Cape Town Lung Institute, Cape Town, South Africa
- Centre for the Study of Antimicrobial Resistance, South African Medical Research Council and University of Cape Town, Cape Town, South Africa
| | - Coralie Geric
- McGill International TB Centre, McGill University, Montreal, Quebec, Canada
| | - Faiz Ahmad Khan
- McGill International TB Centre, McGill University, Montreal, Quebec, Canada
| | - Aliasgar Esmail
- Division of Pulmonology, Department of Medicine, Centre for Lung Infection and Immunity, University of Cape Town Lung Institute, Cape Town, South Africa
- Centre for the Study of Antimicrobial Resistance, South African Medical Research Council and University of Cape Town, Cape Town, South Africa
| | - Eleanor Ochodo
- Kenya Medical Research Institute, Nairobi, Kenya
- Division of Epidemiology and Biostatistics, Department of Global Health, Stellenbosch University, Cape Town, South Africa
| | - Mark Engel
- Department of Medicine, University of Cape Town, Cape Town, South Africa
- Cochrane South Africa, South African Medical Research Council, Cape Town, South Africa
| | - Keertan Dheda
- Division of Pulmonology, Department of Medicine, Centre for Lung Infection and Immunity, University of Cape Town Lung Institute, Cape Town, South Africa
- Centre for the Study of Antimicrobial Resistance, South African Medical Research Council and University of Cape Town, Cape Town, South Africa
- Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
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Tenda ED, Henrina J, Setiadharma A, Aristy DJ, Romadhon PZ, Thahadian HF, Mahdi BA, Adhikara IM, Marfiani E, Suryantoro SD, Yunus RE, Yusuf PA. Derivation and validation of novel integrated inpatient mortality prediction score for COVID-19 (IMPACT) using clinical, laboratory, and AI-processed radiological parameter upon admission: a multicentre study. Sci Rep 2024; 14:2149. [PMID: 38272920 PMCID: PMC10810804 DOI: 10.1038/s41598-023-50564-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 12/21/2023] [Indexed: 01/27/2024] Open
Abstract
Limited studies explore the use of AI for COVID-19 prognostication. This study investigates the relationship between AI-aided radiographic parameters, clinical and laboratory data, and mortality in hospitalized COVID-19 patients. We conducted a multicentre retrospective study. The derivation and validation cohort comprised of 512 and 137 confirmed COVID-19 patients, respectively. Variable selection for constructing an in-hospital mortality scoring model was performed using the least absolute shrinkage and selection operator, followed by logistic regression. The accuracy of the scoring model was assessed using the area under the receiver operating characteristic curve. The final model included eight variables: anosmia (OR: 0.280; 95%CI 0.095-0.826), dyspnoea (OR: 1.684; 95%CI 1.049-2.705), loss of consciousness (OR: 4.593; 95%CI 1.702-12.396), mean arterial pressure (OR: 0.928; 95%CI 0.900-0.957), peripheral oxygen saturation (OR: 0.981; 95%CI 0.967-0.996), neutrophil % (OR: 1.034; 95%CI 1.013-1.055), serum urea (OR: 1.018; 95%CI 1.010-1.026), affected lung area score (OR: 1.026; 95%CI 1.014-1.038). The Integrated Inpatient Mortality Prediction Score for COVID-19 (IMPACT) demonstrated a predictive value of 0.815 (95% CI 0.774-0.856) in the derivation cohort. Internal validation resulted in an AUROC of 0.770 (95% CI 0.661-0.879). Our study provides valuable evidence of the real-world application of AI in clinical settings. However, it is imperative to conduct prospective validation of our findings, preferably utilizing a control group and extending the application to broader populations.
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Affiliation(s)
- Eric Daniel Tenda
- Pulmonology and Critical Care Medicine Division, Department of Internal Medicine, Dr. Cipto Mangunkusumo National Referral Hospital, Faculty of Medicine Universitas Indonesia, Jl. Pangeran Diponegoro No. 71, RW. 5, Kenari, Kec. Senen, Kota Jakarta Pusat, Daerah Khusus Ibukota Jakarta, 10430, Indonesia.
- Medical Technology Cluster of Indonesian Medical Research Institute (IMERI), Faculty of Medicine Universitas Indonesia, Jakarta, Indonesia.
| | - Joshua Henrina
- Pulmonology and Critical Care Medicine Division, Department of Internal Medicine, Dr. Cipto Mangunkusumo National Referral Hospital, Faculty of Medicine Universitas Indonesia, Jl. Pangeran Diponegoro No. 71, RW. 5, Kenari, Kec. Senen, Kota Jakarta Pusat, Daerah Khusus Ibukota Jakarta, 10430, Indonesia
| | - Andry Setiadharma
- Pulmonology and Critical Care Medicine Division, Department of Internal Medicine, Dr. Cipto Mangunkusumo National Referral Hospital, Faculty of Medicine Universitas Indonesia, Jl. Pangeran Diponegoro No. 71, RW. 5, Kenari, Kec. Senen, Kota Jakarta Pusat, Daerah Khusus Ibukota Jakarta, 10430, Indonesia
| | - Dahliana Jessica Aristy
- Pulmonology and Critical Care Medicine Division, Department of Internal Medicine, Dr. Cipto Mangunkusumo National Referral Hospital, Faculty of Medicine Universitas Indonesia, Jl. Pangeran Diponegoro No. 71, RW. 5, Kenari, Kec. Senen, Kota Jakarta Pusat, Daerah Khusus Ibukota Jakarta, 10430, Indonesia
| | - Pradana Zaky Romadhon
- Hematology and Medical Oncology, Department of Internal Medicine, Universitas Airlangga Academic Hospital, Faculty of Medicine Universitas Airlangga, Surabaya, Indonesia
| | - Harik Firman Thahadian
- Pulmonology and Critical Care Medicine Division, Department of Internal Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Dr. Sardjito General Hospital, Yogyakarta, Indonesia
| | - Bagus Aulia Mahdi
- Department of Internal Medicine, Faculty of Medicine Universitas Airlangga, Surabaya, Indonesia
| | - Imam Manggalya Adhikara
- Cardiology Division, Department of Internal Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Dr. Sardjito General Hospital, Yogyakarta, Indonesia
| | - Erika Marfiani
- Pulmonology and Critical Care Medicine Division, Department of Internal Medicine, Faculty of Medicine Universitas Airlangga, Universitas Airlangga Academic Hospital, Surabaya, Indonesia
| | - Satriyo Dwi Suryantoro
- Nephrology Division, Department of Internal Medicine, Faculty of Medicine Universitas Airlangga, Universitas Airlangga Academic Hospital, Surabaya, Indonesia
| | - Reyhan Eddy Yunus
- Medical Technology Cluster of Indonesian Medical Research Institute (IMERI), Faculty of Medicine Universitas Indonesia, Jakarta, Indonesia
- Department of Radiology, Dr. Cipto Mangunkusumo National Referral Hospital, Faculty of Medicine Universitas Indonesia, Jakarta, Indonesia
| | - Prasandhya Astagiri Yusuf
- Medical Technology Cluster of Indonesian Medical Research Institute (IMERI), Faculty of Medicine Universitas Indonesia, Jakarta, Indonesia
- Department of Medical Physiology and Biophysics, Faculty of Medicine Universitas Indonesia, Jakarta, Indonesia
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Okada K, Yamada N, Takayanagi K, Hiasa Y, Kitamura Y, Hoshino Y, Hirao S, Yoshiyama T, Onozaki I, Kato S. Applicability of artificial intelligence-based computer-aided detection (AI-CAD) for pulmonary tuberculosis to community-based active case finding. Trop Med Health 2024; 52:2. [PMID: 38163868 PMCID: PMC10759734 DOI: 10.1186/s41182-023-00560-6] [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: 08/06/2023] [Accepted: 12/02/2023] [Indexed: 01/03/2024] Open
Abstract
BACKGROUND Artificial intelligence-based computer-aided detection (AI-CAD) for tuberculosis (TB) has become commercially available and several studies have been conducted to evaluate the performance of AI-CAD for pulmonary tuberculosis (TB) in clinical settings. However, little is known about its applicability to community-based active case-finding (ACF) for TB. METHODS We analysed an anonymized data set obtained from a community-based ACF in Cambodia, targeting persons aged 55 years or over, persons with any TB symptoms, such as chronic cough, and persons at risk of TB, including household contacts. All of the participants in the ACF were screened by chest radiography (CXR) by Cambodian doctors, followed by Xpert test when they were eligible for sputum examination. Interpretation by an experienced chest physician and abnormality scoring by a newly developed AI-CAD were retrospectively conducted for the CXR images. With a reference of Xpert-positive TB or human interpretations, receiver operating characteristic (ROC) curves were drawn to evaluate the AI-CAD performance by area under the ROC curve (AUROC). In addition, its applicability to community-based ACFs in Cambodia was examined. RESULTS TB scores of the AI-CAD were significantly associated with the CXR classifications as indicated by the severity of TB disease, and its AUROC as the bacteriological reference was 0.86 (95% confidence interval 0.83-0.89). Using a threshold for triage purposes, the human reading and bacteriological examination needed fell to 21% and 15%, respectively, detecting 95% of Xpert-positive TB in ACF. For screening purposes, we could detect 98% of Xpert-positive TB cases. CONCLUSIONS AI-CAD is applicable to community-based ACF in high TB burden settings, where experienced human readers for CXR images are scarce. The use of AI-CAD in developing countries has the potential to expand CXR screening in community-based ACFs, with a substantial decrease in the workload on human readers and laboratory labour. Further studies are needed to generalize the results to other countries by increasing the sample size and comparing the AI-CAD performance with that of more human readers.
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Affiliation(s)
- Kosuke Okada
- The Research Institute of Tuberculosis (RIT), Japan Anti-Tuberculosis Association (JATA), Tokyo, Japan.
- Department of International Programme, Japan Anti-Tuberculosis Association (JATA), Tokyo, Japan.
| | - Norio Yamada
- The Research Institute of Tuberculosis (RIT), Japan Anti-Tuberculosis Association (JATA), Tokyo, Japan
| | - Kiyoko Takayanagi
- Fukujuji Hospital, Japan Anti-Tuberculosis Association (JATA), Tokyo, Japan
| | - Yuta Hiasa
- Imaging Technology Center, ICT Strategy Division, Fujifilm Corporation, Tokyo, Japan
| | - Yoshiro Kitamura
- Imaging Technology Center, ICT Strategy Division, Fujifilm Corporation, Tokyo, Japan
| | - Yutaka Hoshino
- The Research Institute of Tuberculosis (RIT), Japan Anti-Tuberculosis Association (JATA), Tokyo, Japan
| | - Susumu Hirao
- The Research Institute of Tuberculosis (RIT), Japan Anti-Tuberculosis Association (JATA), Tokyo, Japan
| | - Takashi Yoshiyama
- The Research Institute of Tuberculosis (RIT), Japan Anti-Tuberculosis Association (JATA), Tokyo, Japan
- Fukujuji Hospital, Japan Anti-Tuberculosis Association (JATA), Tokyo, Japan
| | - Ikushi Onozaki
- The Research Institute of Tuberculosis (RIT), Japan Anti-Tuberculosis Association (JATA), Tokyo, Japan
- Department of International Programme, Japan Anti-Tuberculosis Association (JATA), Tokyo, Japan
| | - Seiya Kato
- The Research Institute of Tuberculosis (RIT), Japan Anti-Tuberculosis Association (JATA), Tokyo, Japan
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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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Glaser N, Bosman S, Madonsela T, van Heerden A, Mashaete K, Katende B, Ayakaka I, Murphy K, Signorell A, Lynen L, Bremerich J, Reither K. Incidental radiological findings during clinical tuberculosis screening in Lesotho and South Africa: a case series. J Med Case Rep 2023; 17:365. [PMID: 37620921 PMCID: PMC10464059 DOI: 10.1186/s13256-023-04097-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 07/21/2023] [Indexed: 08/26/2023] Open
Abstract
BACKGROUND Chest X-ray offers high sensitivity and acceptable specificity as a tuberculosis screening tool, but in areas with a high burden of tuberculosis, there is often a lack of radiological expertise to interpret chest X-ray. Computer-aided detection systems based on artificial intelligence are therefore increasingly used to screen for tuberculosis-related abnormalities on digital chest radiographies. The CAD4TB software has previously been shown to demonstrate high sensitivity for chest X-ray tuberculosis-related abnormalities, but it is not yet calibrated for the detection of non-tuberculosis abnormalities. When screening for tuberculosis, users of computer-aided detection need to be aware that other chest pathologies are likely to be as prevalent as, or more prevalent than, active tuberculosis. However, non--tuberculosis chest X-ray abnormalities detected during chest X-ray screening for tuberculosis remain poorly characterized in the sub-Saharan African setting, with only minimal literature. CASE PRESENTATION In this case series, we report on four cases with non-tuberculosis abnormalities detected on CXR in TB TRIAGE + ACCURACY (ClinicalTrials.gov Identifier: NCT04666311), a study in adult presumptive tuberculosis cases at health facilities in Lesotho and South Africa to determine the diagnostic accuracy of two potential tuberculosis triage tests: computer-aided detection (CAD4TB v7, Delft, the Netherlands) and C-reactive protein (Alere Afinion, USA). The four Black African participants presented with the following chest X-ray abnormalities: a 59-year-old woman with pulmonary arteriovenous malformation, a 28-year-old man with pneumothorax, a 20-year-old man with massive bronchiectasis, and a 47-year-old woman with aspergilloma. CONCLUSIONS Solely using chest X-ray computer-aided detection systems based on artificial intelligence as a tuberculosis screening strategy in sub-Saharan Africa comes with benefits, but also risks. Due to the limitation of CAD4TB for non-tuberculosis-abnormality identification, the computer-aided detection software may miss significant chest X-ray abnormalities that require treatment, as exemplified in our four cases. Increased data collection, characterization of non-tuberculosis anomalies and research on the implications of these diseases for individuals and health systems in sub-Saharan Africa is needed to help improve existing artificial intelligence software programs and their use in countries with high tuberculosis burden.
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Affiliation(s)
- Naomi Glaser
- Faculty of Medicine, University of Zürich, Zurich, Switzerland.
- Department of Health Sciences and Medicine, University of Lucerne, Lucerne, Switzerland.
| | - Shannon Bosman
- Center for Community Based Research, Human Sciences Research Council, Pietermaritzburg, South Africa
| | - Thandanani Madonsela
- Center for Community Based Research, Human Sciences Research Council, Pietermaritzburg, South Africa
| | - Alastair van Heerden
- Center for Community Based Research, Human Sciences Research Council, Pietermaritzburg, South Africa
| | | | | | - Irene Ayakaka
- SolidarMed, Partnerships for Health, Maseru, Lesotho
| | - Keelin Murphy
- Radboud University Medical Center, Nijmegen, The Netherlands
| | - Aita Signorell
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Lutgarde Lynen
- Institute of Tropical Medicine Antwerp, Antwerp, Belgium
| | - Jens Bremerich
- Department of Radiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Klaus Reither
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland.
- University of Basel, Basel, Switzerland.
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8
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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: 0] [Impact Index Per Article: 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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9
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Fanni SC, Marcucci A, Volpi F, Valentino S, Neri E, Romei C. Artificial Intelligence-Based Software with CE Mark for Chest X-ray Interpretation: Opportunities and Challenges. Diagnostics (Basel) 2023; 13:2020. [PMID: 37370915 DOI: 10.3390/diagnostics13122020] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 04/26/2023] [Accepted: 06/08/2023] [Indexed: 06/29/2023] Open
Abstract
Chest X-ray (CXR) is the most important technique for performing chest imaging, despite its well-known limitations in terms of scope and sensitivity. These intrinsic limitations of CXR have prompted the development of several artificial intelligence (AI)-based software packages dedicated to CXR interpretation. The online database "AI for radiology" was queried to identify CE-marked AI-based software available for CXR interpretation. The returned studies were divided according to the targeted disease. AI-powered computer-aided detection software is already widely adopted in screening and triage for pulmonary tuberculosis, especially in countries with few resources and suffering from high a burden of this disease. AI-based software has also been demonstrated to be valuable for the detection of lung nodules detection, automated flagging of positive cases, and post-processing through the development of digital bone suppression software able to produce digital bone suppressed images. Finally, the majority of available CE-marked software packages for CXR are designed to recognize several findings, with potential differences in sensitivity and specificity for each of the recognized findings.
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Affiliation(s)
- Salvatore Claudio Fanni
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
| | - Alessandro Marcucci
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
| | - Federica Volpi
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
| | | | - Emanuele Neri
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
| | - Chiara Romei
- Department of Diagnostic Imaging, 2nd Radiology Unit, Pisa University-Hospital, Via Paradisa 2, 56124 Pisa, Italy
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10
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Manyazewal T, Ali MK, Kebede T, Magee MJ, Getinet T, Patel SA, Hailemariam D, Escoffery C, Woldeamanuel Y, Makonnen N, Solomon S, Amogne W, Marconi VC, Fekadu A. Mapping digital health ecosystems in Africa in the context of endemic infectious and non-communicable diseases. NPJ Digit Med 2023; 6:97. [PMID: 37237022 PMCID: PMC10213589 DOI: 10.1038/s41746-023-00839-2] [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: 10/24/2022] [Accepted: 05/05/2023] [Indexed: 05/28/2023] Open
Abstract
Investments in digital health technologies such as artificial intelligence, wearable devices, and telemedicine may support Africa achieve United Nations (UN) Sustainable Development Goal for Health by 2030. We aimed to characterize and map digital health ecosystems of all 54 countries in Africa in the context of endemic infectious and non-communicable diseases (ID and NCD). We performed a cross-national ecological analysis of digital health ecosystems using 20-year data from the World Bank, UN Economic Commission for Africa, World Health Organization, and Joint UN Programme on HIV/AIDS. Spearman's rank correlation coefficients were used to characterize ecological correlations between exposure (technology characteristics) and outcome (IDs and NCDs incidence/mortality) variables. Weighted linear combination model was used as the decision rule, combining disease burden, technology access, and economy, to explain, rank, and map digital health ecosystems of a given country. The perspective of our analysis was to support government decision-making. The 20-year trend showed that technology characteristics have been steadily growing in Africa, including internet access, mobile cellular and fixed broadband subscriptions, high-technology manufacturing, GDP per capita, and adult literacy, while many countries have been overwhelmed by a double burden of IDs and NCDs. Inverse correlations exist between technology characteristics and ID burdens, such as fixed broadband subscription and incidence of tuberculosis and malaria, or GDP per capita and incidence of tuberculosis and malaria. Based on our models, countries that should prioritize digital health investments were South Africa, Nigeria, and Tanzania for HIV; Nigeria, South Africa, and Democratic Republic of the Congo (DROC) for tuberculosis; DROC, Nigeria, and Uganda for malaria; and Egypt, Nigeria, and Ethiopia for endemic NCDs including diabetes, cardiovascular disease, respiratory diseases, and malignancies. Countries such as Kenya, Ethiopia, Zambia, Zimbabwe, Angola, and Mozambique were also highly affected by endemic IDs. By mapping digital health ecosystems in Africa, this study provides strategic guidance about where governments should prioritize digital health technology investments that require preliminary analysis of country-specific contexts to bring about sustainable health and economic returns. Building digital infrastructure should be a key part of economic development programs in countries with high disease burdens to ensure more equitable health outcomes. Though infrastructure developments alongside digital health technologies are the responsibility of governments, global health initiatives can cultivate digital health interventions substantially by bridging knowledge and investment gaps, both through technology transfer for local production and negotiation of prices for large-scale deployment of the most impactful digital health technologies.
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Affiliation(s)
- Tsegahun Manyazewal
- Addis Ababa University, College of Health Sciences, Center for Innovative Drug Development and Therapeutic Trials for Africa (CDT-Africa), Addis Ababa, Ethiopia.
| | - Mohammed K Ali
- Emory University, Rollins School of Public Health, Hubert Department of Global Health, Atlanta, GA, USA
- Emory University, School of Medicine, Department of Family and Preventive Medicine, Atlanta, GA, USA
| | - Tedla Kebede
- Addis Ababa University, College of Health Sciences, School of Medicine, Addis Ababa, Ethiopia
| | - Matthew J Magee
- Emory University, Rollins School of Public Health, Hubert Department of Global Health, Atlanta, GA, USA
| | - Tewodros Getinet
- St. Paul's Hospital Millennium Medical College, School of Public Health, Addis Ababa, Ethiopia
| | - Shivani A Patel
- Emory University, Rollins School of Public Health, Hubert Department of Global Health, Atlanta, GA, USA
| | - Damen Hailemariam
- Addis Ababa University, College of Health Sciences, School of Public Health, Addis Ababa, Ethiopia
| | - Cam Escoffery
- Emory University, Rollins School of Public Health, Department of Behavioral, Social, and Health Education Sciences, Atlanta, GA, USA
| | - Yimtubezinash Woldeamanuel
- Addis Ababa University, College of Health Sciences, Center for Innovative Drug Development and Therapeutic Trials for Africa (CDT-Africa), Addis Ababa, Ethiopia
| | - Nardos Makonnen
- University of Virginia, School of Medicine, Department of Emergency Medicine, Charlottesville, VA, USA
| | - Samrawit Solomon
- St. Paul's Hospital Millennium Medical College, School of Public Health, Addis Ababa, Ethiopia
| | - Wondwossen Amogne
- Addis Ababa University, College of Health Sciences, Addis Ababa, Ethiopia
| | - Vincent C Marconi
- Emory University School of Medicine and Rollins School of Public Health, Atlanta, GA, USA
| | - Abebaw Fekadu
- Addis Ababa University, College of Health Sciences, Center for Innovative Drug Development and Therapeutic Trials for Africa (CDT-Africa), Addis Ababa, Ethiopia
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11
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Cai J, Guo L, Zhu L, Xia L, Qian L, Lure YMF, Yin X. Impact of localized fine tuning in the performance of segmentation and classification of lung nodules from computed tomography scans using deep learning. Front Oncol 2023; 13:1140635. [PMID: 37056345 PMCID: PMC10088514 DOI: 10.3389/fonc.2023.1140635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 03/16/2023] [Indexed: 03/30/2023] Open
Abstract
BackgroundAlgorithm malfunction may occur when there is a performance mismatch between the dataset with which it was developed and the dataset on which it was deployed.MethodsA baseline segmentation algorithm and a baseline classification algorithm were developed using public dataset of Lung Image Database Consortium to detect benign and malignant nodules, and two additional external datasets (i.e., HB and XZ) including 542 cases and 486 cases were involved for the independent validation of these two algorithms. To explore the impact of localized fine tuning on the individual segmentation and classification process, the baseline algorithms were fine tuned with CT scans of HB and XZ datasets, respectively, and the performance of the fine tuned algorithms was tested to compare with the baseline algorithms.ResultsThe proposed baseline algorithms of both segmentation and classification experienced a drop when directly deployed in external HB and XZ datasets. Comparing with the baseline validation results in nodule segmentation, the fine tuned segmentation algorithm obtained better performance in Dice coefficient, Intersection over Union, and Average Surface Distance in HB dataset (0.593 vs. 0.444; 0.450 vs. 0.348; 0.283 vs. 0.304) and XZ dataset (0.601 vs. 0.486; 0.482 vs. 0.378; 0.225 vs. 0.358). Similarly, comparing with the baseline validation results in benign and malignant nodule classification, the fine tuned classification algorithm had improved area under the receiver operating characteristic curve value, accuracy, and F1 score in HB dataset (0.851 vs. 0.812; 0.813 vs. 0.769; 0.852 vs. 0.822) and XZ dataset (0.724 vs. 0.668; 0.696 vs. 0.617; 0.737 vs. 0.668).ConclusionsThe external validation performance of localized fine tuned algorithms outperformed the baseline algorithms in both segmentation process and classification process, which showed that localized fine tuning may be an effective way to enable a baseline algorithm generalize to site-specific use.
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Affiliation(s)
- Jingwei Cai
- Radiology Department, Affiliated Hospital of Hebei University, Baoding, Hebei, China
- Clinical Medical College, Hebei University, Baoding, Hebei, China
| | - Lin Guo
- Shenzhen Zhiying Medical Imaging, Shenzhen, Guangdong, China
| | - Litong Zhu
- Department of Medicine, Queen Mary Hospital, University of Hong, Hong Kong, Hong Kong SAR, China
| | - Li Xia
- Shenzhen Zhiying Medical Imaging, Shenzhen, Guangdong, China
| | - Lingjun Qian
- Shenzhen Zhiying Medical Imaging, Shenzhen, Guangdong, China
| | | | - Xiaoping Yin
- Radiology Department, Affiliated Hospital of Hebei University, Baoding, Hebei, China
- *Correspondence: Xiaoping Yin,
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12
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Fehr J, Gunda R, Siedner MJ, Hanekom W, Ndung’u T, Grant A, Lippert C, Wong EB. CAD4TB software updates: different triaging thresholds require caution by users and regulation by authorities. Int J Tuberc Lung Dis 2023; 27:157-160. [PMID: 36853104 PMCID: PMC9904401 DOI: 10.5588/ijtld.22.0437] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Affiliation(s)
- J. Fehr
- Africa Health Research Institute, KwaZulu-Natal, South Africa
,Digital Health & Machine Learning, Hasso Plattner Institute for Digital Engineering, Potsdam, Germany
,Digital Engineering Faculty, University of Potsdam, Potsdam, Germany
| | - R. Gunda
- Africa Health Research Institute, KwaZulu-Natal, South Africa
,School of Nursing and Public Health, College of Health Sciences, University of KwaZulu-Natal, KwaZulu-Natal, South Africa
,Division of Infection and Immunity, University College London, London, UK
| | - M. J. Siedner
- Africa Health Research Institute, KwaZulu-Natal, South Africa
,School of Clinical Medicine, College of Health Sciences, University of KwaZulu-Natal, KwaZulu-Natal, South Africa
,Harvard Medical School, Boston, MA, USA
,Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, USA
| | - W. Hanekom
- Africa Health Research Institute, KwaZulu-Natal, South Africa
| | - T. Ndung’u
- Africa Health Research Institute, KwaZulu-Natal, South Africa
,Division of Infection and Immunity, University College London, London, UK
,HIV Pathogenesis Programme, The Doris Duke Medical Research Institute, University of KwaZulu-Natal, Durban, South Africa
,Ragon Institute of MGH, MIT and Harvard University, Cambridge, MA, USA
| | - A. Grant
- Africa Health Research Institute, KwaZulu-Natal, South Africa
,School of Nursing and Public Health, College of Health Sciences, University of KwaZulu-Natal, KwaZulu-Natal, South Africa
,London School of Hygiene & Tropical Medicine, London, UK
,School of Clinical Medicine, College of Health Sciences, University of KwaZulu-Natal, KwaZulu-Natal, South Africa
| | - C. Lippert
- Digital Health & Machine Learning, Hasso Plattner Institute for Digital Engineering, Potsdam, Germany
,Digital Engineering Faculty, University of Potsdam, Potsdam, Germany
,Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - E. B. Wong
- Africa Health Research Institute, KwaZulu-Natal, South Africa
,Division of Infectious Diseases, University of Alabama at Birmingham, AL, USA
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13
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Castle AC, Hoeppner SS, Magodoro IM, Singh U, Moosa Y, Bassett IV, Wong EB, Siedner MJ. Association between prior tuberculosis disease and dysglycemia within an HIV-endemic, rural South African population. PLoS One 2023; 18:e0282371. [PMID: 36928895 PMCID: PMC10019670 DOI: 10.1371/journal.pone.0282371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 02/13/2023] [Indexed: 03/18/2023] Open
Abstract
OBJECTIVE Tuberculosis (TB) may predispose individuals to the development of diabetes. Such a relationship could have an outsized impact in high-prevalence TB settings. However, few studies have explored this relationship in populations heavily burdened by diabetes and TB. METHODS We analyzed data from a community-based population cohort that enrolled adults in rural South Africa. Individuals were considered to have prior TB if they self-reported a history of TB treatment. We fitted sex-specific logistic regression models, adjusted for potential clinical and demographic confounders, to estimate relationships between dysglycemia (HBA1c ≥6.5%) and prior TB. Propensity score-matched cohorts accounted for the differential age distributions between comparator groups. We examined the interactions between sex, prior TB, and HIV status. RESULTS In the analytic cohort (n = 17,593), the prevalence of prior TB was 13.8% among men and 10.7% among women. Dysglycemia was found in 9.1% of the population, and HIV prevalence was 34.0%. We found no difference in dysglycemia prevalence by prior TB (men OR 0.96, 95% CI 0.60-1.56: women OR 1.05, 95% CI 0.79-1.39). However, there was a qualitative interaction by HIV serostatus, such that among men without HIV, those with a history of TB had a greater prevalence of dysglycemia than those without prior TB (10.1% vs. 4.6%, p = 0.0077). An inverse relationship was observed among men living with HIV (prior TB 3.3% vs. no TB 7.3%, p = 0.0073). CONCLUSIONS Treated TB disease was not associated with dysglycemia in an HIV-endemic, rural South African population. However, we found a significant interaction between prior TB and HIV status among men, suggesting distinct pathophysiological mechanisms between the two infections that may impact glucose metabolism. Longitudinal studies are needed to better establish a causal effect and underlying mechanisms related to resolved TB, HIV, and diabetes.
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Affiliation(s)
- Alison C. Castle
- Africa Health Research Institute, KwaZulu-Natal, South Africa
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- * E-mail:
| | | | - Itai M. Magodoro
- Emory Global Diabetes Research Center, Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Urisha Singh
- Africa Health Research Institute, KwaZulu-Natal, South Africa
- University of KwaZulu-Natal, Durban, KwaZulu-Natal, South Africa
| | - Yumna Moosa
- Africa Health Research Institute, KwaZulu-Natal, South Africa
- University of KwaZulu-Natal, Durban, KwaZulu-Natal, South Africa
| | - Ingrid V. Bassett
- Africa Health Research Institute, KwaZulu-Natal, South Africa
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Emily B. Wong
- Africa Health Research Institute, KwaZulu-Natal, South Africa
- University of KwaZulu-Natal, Durban, KwaZulu-Natal, South Africa
- Division of Infectious Diseases, University of Alabama Birmingham, Birmingham, Alabama, United States of America
| | - Mark J. Siedner
- Africa Health Research Institute, KwaZulu-Natal, South Africa
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- University of KwaZulu-Natal, Durban, KwaZulu-Natal, South Africa
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14
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Kagujje M, Kerkhoff AD, Nteeni M, Dunn I, Mateyo K, Muyoyeta M. The Performance of Computer-Aided Detection Digital Chest X-ray Reading Technologies for Triage of Active Tuberculosis Among Persons With a History of Previous Tuberculosis. Clin Infect Dis 2022; 76:e894-e901. [PMID: 36004409 PMCID: PMC9907528 DOI: 10.1093/cid/ciac679] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 08/04/2022] [Accepted: 08/19/2022] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Digital chest X-ray (dCXR) computer-aided detection (CAD) technology uses lung shape and texture analysis to determine the probability of tuberculosis (TB). However, many patients with previously treated TB have sequelae, which also distort lung shape and texture. We evaluated the diagnostic performance of 2 CAD systems for triage of active TB in patients with previously treated TB. METHODS We conducted a retrospective analysis of data from a cross-sectional active TB case finding study. Participants ≥15 years, with ≥1 current TB symptom and complete data on history of previous TB, dCXR, and TB microbiological reference (Xpert MTB/RIF) were included. dCXRs were evaluated using CAD4TB (v.7.0) and qXR (v.3.0). We determined the diagnostic accuracy of both systems, overall and stratified by history of TB, using a single threshold for each system that achieved 90% sensitivity and maximized specificity in the overall population. RESULTS Of 1884 participants, 452 (24.0%) had a history of previous TB. Prevalence of microbiologically confirmed TB among those with and without history of previous TB was 12.4% and 16.9%, respectively. Using CAD4TB, sensitivity and specificity were 89.3% (95% CI: 78.1-96.0%) and 24.0% (19.9-28.5%) and 90.5% (86.1-93.3%) and 60.3% (57.4-63.0%) among those with and without previous TB, respectively. Using qXR, sensitivity and specificity were 94.6% (95% CI: 85.1-98.9%) and 22.2% (18.2-26.6%) and 89.7% (85.1-93.2%) and 61.8% (58.9-64.5%) among those with and without previous TB, respectively. CONCLUSIONS The performance of CAD systems as a TB triage tool is decreased among persons previously treated for TB.
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Affiliation(s)
- Mary Kagujje
- Correspondence: M. Kagujje, Centre for Infectious Disease Research in Zambia, Plot 34620 Off Alick Nkhata Road between ERB and FAZ, Mass Media, PO Box 34681, Lusaka, Zambia ()
| | - Andrew D Kerkhoff
- Division of HIV, Infectious Diseases and Global Medicine Zuckerberg, San Francisco General Hospital and Trauma Center, University of California, San Francisco, San Francisco, California, USA
| | - Mutinta Nteeni
- Department of Radiology, Levy Mwanawasa University Teaching Hospital, Lusaka, Zambia
| | - Ian Dunn
- Department of Radiology, University of British Columbia, Vancouver, Canada
| | - Kondwelani Mateyo
- Department of Internal Medicine, University Teaching Hospital, Lusaka, Zambia
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15
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Nathavitharana RR, Garcia-Basteiro AL, Ruhwald M, Cobelens F, Theron G. Reimagining the status quo: How close are we to rapid sputum-free tuberculosis diagnostics for all? EBioMedicine 2022; 78:103939. [PMID: 35339423 PMCID: PMC9043971 DOI: 10.1016/j.ebiom.2022.103939] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 02/14/2022] [Accepted: 02/28/2022] [Indexed: 01/26/2023] Open
Abstract
Rapid, accurate, sputum-free tests for tuberculosis (TB) triage and confirmation are urgently needed to close the widening diagnostic gap. We summarise key technologies and review programmatic, systems, and resource issues that could affect the impact of diagnostics. Mid-to-early-stage technologies like artificial intelligence-based automated digital chest X-radiography and capillary blood point-of-care assays are particularly promising. Pitfalls in the diagnostic pipeline, included a lack of community-based tools. We outline how these technologies may complement one another within the context of the TB care cascade, help overturn current paradigms (eg, reducing syndromic triage reliance, permitting subclinical TB to be diagnosed), and expand options for extra-pulmonary TB. We review challenges such as the difficulty of detecting paucibacillary TB and the limitations of current reference standards, and discuss how researchers and developers can better design and evaluate assays to optimise programmatic uptake. Finally, we outline how leveraging the urgency and innovation applied to COVID-19 is critical to improving TB patients' diagnostic quality-of-care.
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Affiliation(s)
- Ruvandhi R. Nathavitharana
- Division of Infectious Diseases, Beth Israel Deaconess Medical Center & Harvard Medical School, Boston, USA
| | - Alberto L. Garcia-Basteiro
- ISGlobal, Hospital Clínic - Universitat de Barcelona, Barcelona, Spain,Centro de Investigação em Saude de Manhiça, Maputo, Mozambique
| | - Morten Ruhwald
- FIND, the global alliance for diagnostics, Geneva, Switzerland
| | - Frank Cobelens
- Department of Global Health and Amsterdam Institute for Global Health and Development, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | - Grant Theron
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, South Africa,Corresponding author.
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16
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Digital Chest X-ray with Computer-aided Detection for TB Screening within Correctional Facilities. Ann Am Thorac Soc 2021; 19:1313-1319. [PMID: 34914539 DOI: 10.1513/annalsats.202103-380oc] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
RATIONALE Realizing the Global Plan to End TB will require reaching at least 90% of people in key populations such as inmates through optimizing case-finding approaches. OBJECTIVES To evaluate the addition of digital chest x-ray (d-CXR) with computer-aided detection (CAD) to symptoms on TB yield among inmates. METHODS Consecutive adult inmates from four correctional facilities in South Africa were screened for TB using symptoms and d-CXR. Any person with >1 symptom or CAD>50 provided two sputa for liquid culture and GeneXpert MTB/RIF Ultra (Xpert Ultra) testing. In a sample of 800 symptom-negative inmates with CAD<50, Xpert Ultra testing was also conducted. TB yield was defined as the proportion of new bacteriologically-confirmed TB patients identified. RESULTS We enrolled 3,576 participants; 99.6% male, median age of 34 years (IQR: 28-41), and 584 (16.3%) HIV-positive. Of those screened, 867 (24.2%) participants required investigation [394 (11.2%) symptomatic, 685 (19.1%) on abnormal CAD and 867 (24.2%) with either]. Sputum was taken in 747 (86.2%) participants, with 28 (7.8 per 1000 population) new TB cases diagnosed. Based on hypothesized screening modalities, yield would have been 3.6 per 1,000 population on symptoms alone and 7.0 per 1,000 population on d-CXR alone. Amongst an additional 800 inmates tested whom initially screened symptom-negative and CAD<50, five TB cases were diagnosed. No difference in TB yield when comparing Xpert Ultra against culture (5.6 vs. 4.8 per 1,000 population; p=0.21). CONCLUSIONS The addition of d-CXR identified two times more undiagnosed TB than patients investigated on symptoms alone. Complimentary use of d-CXR may potentially overcome subjectivity inherent in symptom screening alone for identifying TB in this population.
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Govender I, Karat AS, Olivier S, Baisley K, Beckwith P, Dayi N, Dreyer J, Gareta D, Gunda R, Kielmann K, Koole O, Mhlongo N, Modise T, Moodley S, Mpofana X, Ndung'u T, Pillay D, Siedner MJ, Smit T, Surujdeen A, Wong EB, Grant AD. Prevalence of Mycobacterium tuberculosis in sputum and reported symptoms among clinic attendees compared to a community survey in rural South Africa. Clin Infect Dis 2021; 75:314-322. [PMID: 34864910 PMCID: PMC9410725 DOI: 10.1093/cid/ciab970] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Tuberculosis (TB) case finding efforts typically target symptomatic people attending health facilities. We compared the prevalence of Mycobacterium tuberculosis (Mtb) sputum culture-positivity among adult clinic attendees in rural South Africa with a concurrent, community-based estimate from the surrounding demographic surveillance area (DSA). METHODS Clinic: Randomly-selected adults (≥18 years) attending two primary healthcare clinics were interviewed and requested to give sputum for mycobacterial culture. HIV and antiretroviral therapy (ART) status were based on self-report and record review. Community: All adult (≥15 years) DSA residents were invited to a mobile clinic for health screening, including serological HIV testing; those with ≥1 TB symptom (cough, weight loss, night sweats, fever) or abnormal chest radiograph were asked for sputum. RESULTS Clinic: 2,055 patients were enrolled (76.9% female, median age 36 years); 1,479 (72.0%) were classified HIV-positive (98.9% on ART) and 131 (6.4%) reported ≥1 TB symptom. Of 20/2,055 (1.0% [95% CI 0.6-1.5]) with Mtb culture-positive sputum, 14 (70%) reported no symptoms. Community: 10,320 residents were enrolled (68.3% female, median age 38 years); 3,105 (30.3%) tested HIV-positive (87.4% on ART) and 1,091 (10.6%) reported ≥1 TB symptom. Of 58/10,320 (0.6% [95% CI 0.4-0.7]) with Mtb culture-positive sputum, 45 (77.6%) reported no symptoms.In both surveys, sputum culture positivity was associated with male sex and reporting >1 TB symptom. CONCLUSIONS In both clinic and community settings, most participants with Mtb culture-positive sputum were asymptomatic. TB screening based only on symptoms will miss many people with active disease in both settings.
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Affiliation(s)
- Indira Govender
- TB Centre, London School of Hygiene & Tropical Medicine, London, United Kingdom.,Clinical Research Department, Africa Health Research Institute, Somkhele, South Africa
| | - Aaron S Karat
- TB Centre, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Stephen Olivier
- Clinical Research Department, Africa Health Research Institute, Somkhele, South Africa
| | - Kathy Baisley
- Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Peter Beckwith
- TB Centre, London School of Hygiene & Tropical Medicine, London, United Kingdom.,Department of Medicine, University of Cape Town, Cape Town, South Africa
| | - Njabulo Dayi
- Clinical Research Department, Africa Health Research Institute, Somkhele, South Africa
| | - Jaco Dreyer
- Clinical Research Department, Africa Health Research Institute, Somkhele, South Africa
| | - Dickman Gareta
- Clinical Research Department, Africa Health Research Institute, Somkhele, South Africa
| | - Resign Gunda
- Clinical Research Department, Africa Health Research Institute, Somkhele, South Africa.,School of Nursing and Public Health, University of KwaZulu-Natal, Durban, South Africa
| | - Karina Kielmann
- Institute for Global Health and Development, Queen Margaret University, Edinburgh, United Kingdom
| | - Olivier Koole
- TB Centre, London School of Hygiene & Tropical Medicine, London, United Kingdom.,Clinical Research Department, Africa Health Research Institute, Somkhele, South Africa
| | - Ngcebo Mhlongo
- Clinical Research Department, Africa Health Research Institute, Somkhele, South Africa
| | - Tshwaraganang Modise
- Clinical Research Department, Africa Health Research Institute, Somkhele, South Africa
| | - Sashen Moodley
- Clinical Research Department, Africa Health Research Institute, Somkhele, South Africa
| | - Xolile Mpofana
- Clinical Research Department, Africa Health Research Institute, Somkhele, South Africa
| | - Thumbi Ndung'u
- Clinical Research Department, Africa Health Research Institute, Somkhele, South Africa.,School of Public Health, Harvard Medical School, Boston, United States of America
| | - Deenan Pillay
- Clinical Research Department, Africa Health Research Institute, Somkhele, South Africa.,Division of Infection & Immunity, University College London, London, United Kingdom
| | - Mark J Siedner
- Clinical Research Department, Africa Health Research Institute, Somkhele, South Africa.,Division of Infectious Diseases, Massachusetts General Hospital, Boston, United States of America
| | - Theresa Smit
- Clinical Research Department, Africa Health Research Institute, Somkhele, South Africa
| | - Ashmika Surujdeen
- Clinical Research Department, Africa Health Research Institute, Somkhele, South Africa
| | - Emily B Wong
- Clinical Research Department, Africa Health Research Institute, Somkhele, South Africa.,Division of Infection & Immunity, University College London, London, United Kingdom.,Division of Infectious Diseases, Massachusetts General Hospital, Boston, United States of America.,Division of Infectious Diseases, University of Alabama Birmingham, Birmingham, United States of America.,Division of Infection and Immunity, University College London, London, United Kingdom
| | - Alison D Grant
- TB Centre, London School of Hygiene & Tropical Medicine, London, United Kingdom.,Clinical Research Department, Africa Health Research Institute, Somkhele, South Africa.,School of Laboratory Medicine and Medical Sciences, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa.,School of Public Health, University of the Witwatersrand, Johannesburg, South Africa
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18
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Gunda R, Koole O, Gareta D, Olivier S, Surujdeen A, Smit T, Modise T, Dreyer J, Ording-Jespersen G, Munatsi D, Nxumalo S, Khoza T, Mhlongo N, Baisley K, Seeley J, Grant AD, Herbst K, Ndung'u T, Hanekom WA, Siedner MJ, Pillay D, Wong EB. Cohort Profile: The Vukuzazi ('Wake Up and Know Yourself' in isiZulu) population science programme. Int J Epidemiol 2021; 51:e131-e142. [PMID: 34849923 PMCID: PMC9189966 DOI: 10.1093/ije/dyab229] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Indexed: 11/13/2022] Open
Affiliation(s)
- Resign Gunda
- Africa Health Research Institute, KwaZulu-Natal, South Africa.,Division of Infection and Immunity, University College London, London, UK.,School of Nursing and Public Health, University of KwaZulu-Natal, Durban, South Africa
| | - Olivier Koole
- Africa Health Research Institute, KwaZulu-Natal, South Africa.,London School of Hygiene & Tropical Medicine, London, UK
| | - Dickman Gareta
- Africa Health Research Institute, KwaZulu-Natal, South Africa
| | - Stephen Olivier
- Africa Health Research Institute, KwaZulu-Natal, South Africa
| | | | - Theresa Smit
- Africa Health Research Institute, KwaZulu-Natal, South Africa
| | | | - Jaco Dreyer
- Africa Health Research Institute, KwaZulu-Natal, South Africa
| | | | - Day Munatsi
- Africa Health Research Institute, KwaZulu-Natal, South Africa
| | | | - Thandeka Khoza
- Africa Health Research Institute, KwaZulu-Natal, South Africa
| | - Ngcebo Mhlongo
- Africa Health Research Institute, KwaZulu-Natal, South Africa
| | - Kathy Baisley
- Africa Health Research Institute, KwaZulu-Natal, South Africa.,London School of Hygiene & Tropical Medicine, London, UK
| | - Janet Seeley
- Africa Health Research Institute, KwaZulu-Natal, South Africa.,London School of Hygiene & Tropical Medicine, London, UK
| | - Alison D Grant
- Africa Health Research Institute, KwaZulu-Natal, South Africa.,London School of Hygiene & Tropical Medicine, London, UK.,School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal, Durban, South Africa.,School of Clinical Medicine, College of Health Sciences, University of KwaZulu-Natal, Durban, KwaZulu-Natal, South Africa
| | - Kobus Herbst
- Africa Health Research Institute, KwaZulu-Natal, South Africa.,DSI-MRC South African Population Research Infrastructure Network, South African Medical Research Council, Durban, South Africa
| | - Thumbi Ndung'u
- Africa Health Research Institute, KwaZulu-Natal, South Africa.,Division of Infection and Immunity, University College London, London, UK.,HIV Pathogenesis Programme, Doris Duke Medical Research Institute, Nelson R. Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa.,Ragon Institute of MGH, MIT, and Harvard, Harvard Medical School, Cambridge, MA, USA.,Max Planck Institute for Infection Biology, Berlin, Germany
| | - Willem A Hanekom
- Africa Health Research Institute, KwaZulu-Natal, South Africa.,Division of Infection and Immunity, University College London, London, UK
| | - Mark J Siedner
- Africa Health Research Institute, KwaZulu-Natal, South Africa.,School of Clinical Medicine, College of Health Sciences, University of KwaZulu-Natal, Durban, KwaZulu-Natal, South Africa.,Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, USA.,Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Deenan Pillay
- Africa Health Research Institute, KwaZulu-Natal, South Africa.,Division of Infection and Immunity, University College London, London, UK
| | - Emily B Wong
- Africa Health Research Institute, KwaZulu-Natal, South Africa.,Division of Infection and Immunity, University College London, London, UK.,Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, USA.,Division of Infectious Diseases, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
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