Computer-assisted chest radiography reading for tuberculosis screening in people living with diabetes mellitus

Koesoemadinata, Raspati C; Kranzer, K; Livia, Resvi; susilawati, nenden; Annisa, Jessi; Soetedjo, Nanny Natalia Mulyani; Ruslami, Rovina; Philipsen, Rick H. H. M.; Ginneken, Bram van; Soetikno, Ristaniah D.; Crevel, Reinout van; Alisjahbana, Bachti; Hill, Philip C.

doi:10.5588/ijtld.17.0827

Cited by 24 publications

(26 citation statements)

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“…Within the triage use-case studies, 6/8 (75%) used a microbiologic reference standard on all participants [18, 19, 22, 25, 67, 68]. Within the screening studies, 4/5 (80%) used a microbiologic reference [20, 24, 69, 70]. In two Clinical studies, the CADscore was used to select which participants underwent microbiologic testing, hence the software’s diagnostic accuracy could not be assessed [17, 69].…”

Section: Resultsmentioning

confidence: 99%

A systematic review of the diagnostic accuracy of artificial intelligence-based computer programs to analyze chest x-rays for pulmonary tuberculosis

et al. 2019

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We undertook a systematic review of the diagnostic accuracy of artificial intelligence-based software for identification of radiologic abnormalities ( computer-aided detection , or CAD) compatible with pulmonary tuberculosis on chest x-rays (CXRs). We searched four databases for articles published between January 2005-February 2019. We summarized data on CAD type, study design, and diagnostic accuracy. We assessed risk of bias with QUADAS-2. We included 53 of the 4712 articles reviewed: 40 focused on CAD design methods (“Development” studies) and 13 focused on evaluation of CAD (“Clinical” studies). Meta-analyses were not performed due to methodological differences. Development studies were more likely to use CXR databases with greater potential for bias as compared to Clinical studies. Areas under the receiver operating characteristic curve (median AUC [IQR]) were significantly higher: in Development studies AUC: 0.88 [0.82–0.90]) versus Clinical studies (0.75 [0.66–0.87]; p-value 0.004); and with deep-learning (0.91 [0.88–0.99]) versus machine-learning (0.82 [0.75–0.89]; p = 0.001). We conclude that CAD programs are promising, but the majority of work thus far has been on development rather than clinical evaluation. We provide concrete suggestions on what study design elements should be improved.

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Section: Resultsmentioning

confidence: 99%

A systematic review of the diagnostic accuracy of artificial intelligence-based computer programs to analyze chest x-rays for pulmonary tuberculosis

et al. 2019

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“…Previous studies have suggested thresholds between 60 and 85. 13,20,21 Several reports suggested that CAD4TB's threshold needs to be adjusted to study aims, the number of available microbiological tests, and underlying TB prevalence. 16,18,19,21,22 There has been no recommendation of a triage threshold for community-based screening programmes.…”

Section: Discussionmentioning

confidence: 99%

“…One study at a diabetes-care centre in Indonesia retrospectively evaluated CAD4TB and suggested a decision threshold score of 65-70. 20 Data on its performance in population-based screening is limited. Another study applied CAD4TB retrospectively in a non-clinical population-based setting and suggested that a lower CAD4TB score threshold would be required for triaging.…”

Section: Introductionmentioning

confidence: 99%

Computer-aided interpretation of chest radiography reveals the spectrum of tuberculosis in rural South Africa

Fehr

Konigorski

Olivier

et al. 2020

Preprint

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Background Computer-aided digital chest radiograph (CXR) interpretation can facilitate high-throughput screening for tuberculosis (TB), but its use in population-based screening has been limited. We applied an automated image interpretation algorithm, CAD4TBv5, prospectively in an HIV-endemic area. Methods Participants underwent CXR and, for those with symptoms or lung field abnormality, microbiological assessment of sputum collected at a mobile camp in rural South Africa. CAD4TBv5 scored each CXR on a 0-100 scale in the field. An expert radiologist, blinded to the CAD4TBv5 score and other data, assessed CXRs for 1) lung field abnormality and 2) findings diagnostic of active TB (R+). We estimated the performance of CAD4TBv5 for triaging (identifying lung field abnormality as a criteria for sputum examination) and diagnosis (detection of active TB as defined by microbiologic (M+) or radiologic (R+) gold standards). Findings For triaging, a CAD4TBv5 threshold of 25 identified abnormal lung fields with a sensitivity of 90.3% and specificity of 48.2%. For diagnosis, CAD4TBv5 had less agreement with the microbiological reference standard (M+) used to define definite TB (AUC 0.78) than with the radiological reference standard (R+) used to define probable TB (AUC 0.96). HIV-serostatus did not impact CAD4TB's performance. Interpretation A low CAD4TBv5 threshold was required to achieve acceptable triaging sensitivity. Low specificity at this threshold led to high rates of sputum collection despite normal lung fields. CAD4TBv5 had difficulty identifying microbiologically-confirmed TB cases with subtle radiological features but had excellent agreement with the radiologist in identifying radiologically-defined TB cases. We conclude that computer-aided CXR interpretation can be useful in population-based screening in HIV-endemic settings, but threshold selection should be guided by setting-specific piloting and priorities. CXR interpretation algorithms require refinement for the identification of radiologically-subtle early TB.

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“…However, in comparison with the CAD4TB evaluation conducted in Pakistan among individuals with an unknown diabetes status that reported sensitivities in the range 91.0-97.3%, our current study shows lower sensitivities (range 48.6-90.5%) for the corresponding CAD4TB cut-offs on the same version of CAD4TB software on the same version of CAD4TB software 5 . A recent study from Indonesia evaluating CAD4TB among PWD reported an AUC of 0.89 using against the reference standard of bacteriologically and clinically confirmed TB (7 bacteriologically confirmed TB cases and 2 clinically diagnosed) 12 .…”

Section: Discussionmentioning

confidence: 99%

Evaluation of computer aided detection of tuberculosis on chest radiography among people with diabetes in Karachi Pakistan

Habib

Rafiq

Zaidi

et al. 2020

Sci Rep

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Pakistan ranks fifth among high tuberculosis (TB) burden countries and also has seventh highest burden for diabetes mellitus (DM). DM increases the risk of developing TB and contributes to adverse tB treatment outcomes hence screening and integrated management for both diseases in high burden countries is suggested. Computer-Aided Detection for TB (CAD4TB) can potentially be used as triage tool in low resource settings to pre-screen individuals for Xpert MTB/RIF testing. The aim of this study was to evaluate the diagnostic accuracy and performance of CAD4TB software in people with diabetes (PWD) enrolled in a TB screening program in Karachi, Pakistan. A total of 694 individuals with a diagnosis of DM (of whom 31.1% were newly diagnosed) were screened with CAD4TB and simultaneously provided sputum for Xpert MTB/RIF testing. Of the 74 (10.7%) participants who had bacteriologically positive (MTB+) results on Xpert testing, 54 (73%) had a CAD4TB score >70; and 155 (25%) participants who tested MTB-negative had scores >70. The area under the receiver operator curve was 0.78 (95% CI: 0.77-0.80). Our study findings indicate that CAD4TB offers good diagnostic accuracy as a triage test for TB screening among PWD using Xpert MTB/RIF as the reference standard.

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Computer-assisted chest radiography reading for tuberculosis screening in people living with diabetes mellitus

Abstract: CAD4TB has potential as a triage tool for TB screening in PLWD, thereby significantly reducing the need for microbiological examination.

Cited by 24 publications

References 0 publications

A systematic review of the diagnostic accuracy of artificial intelligence-based computer programs to analyze chest x-rays for pulmonary tuberculosis

A systematic review of the diagnostic accuracy of artificial intelligence-based computer programs to analyze chest x-rays for pulmonary tuberculosis

Computer-aided interpretation of chest radiography reveals the spectrum of tuberculosis in rural South Africa

Evaluation of computer aided detection of tuberculosis on chest radiography among people with diabetes in Karachi Pakistan

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