Deep learning, computer-aided radiography reading for tuberculosis: a diagnostic accuracy study from a tertiary hospital in India

Abstract

In general, chest radiographs (CXR) have high sensitivity and moderate specificity for active pulmonary tuberculosis (ptB) screening when interpreted by human readers. However, they are challenging to scale due to hardware costs and the dearth of professionals available to interpret cXR in low-resource, high ptB burden settings. Recently, several computer-aided detection (cAD) programs have been developed to facilitate automated cXR interpretation. We conducted a retrospective case-control study to assess the diagnostic accuracy of a cAD software (qXR, Qure.ai, Mumbai, india) using microbiologically-confirmed PTB as the reference standard. To assess overall accuracy of qXR, receiver operating characteristic (Roc) analysis was used to determine the area under the curve (AUc), along with 95% confidence intervals (CI). Kappa coefficients, and associated 95% CI, were used to investigate inter-rater reliability of the radiologists for detection of specific chest abnormalities. In total, 317 cases and 612 controls were included in the analysis. The AUC for qXR for the detection of microbiologicallyconfirmed PTB was 0.81 (95% CI: 0.78, 0.84). Using the threshold that maximized sensitivity and specificity of qXR simultaneously, the software achieved a sensitivity and specificity of 71% (95% CI: 66%, 76%) and 80% (95% CI: 77%, 83%), respectively. The sensitivity and specificity of radiologists for the detection of microbiologically-confirmed PTB was 56% (95% CI: 50%, 62%) and 80% (95% CI: 77%, 83%), respectively. For detection of key PTB-related abnormalities 'pleural effusion' and 'cavity', qXR achieved an AUC of 0.94 (95% CI: 0.92, 0.96) and 0.84 (95% CI: 0.82, 0.87), respectively. For the other abnormalities, the AUC ranged from 0.75 (95% CI: 0.70, 0.80) to 0.94 (95% CI: 0.91, 0.96). The controls had a high prevalence of other lung diseases which can cause radiological manifestations similar to PTB (e.g., 26% had pneumonia, 15% had lung malignancy, etc.). In a tertiary hospital in india, qXR demonstrated moderate sensitivity and specificity for the detection of PTB. There is likely a larger role for cAD software as a triage test for ptB at the primary care level in settings where access to radiologists in limited. Larger prospective studies that can better assess heterogeneity in important subgroups are needed.

Application of artificial intelligence in digital chest radiography reading for pulmonary tuberculosis screening

“… 27 A retrospective case–control study was conducted in a tertiary hospital from India using microbiologically-confirmed TB as the reference standard, which reported that qXR (v2) could detect TB with an AUC of 0.81, a sensitivity of 71% and a specificity of 80%. 28 As many patients who present at tertiary hospitals have symptoms suggestive of TB, accurate and rapid triage tests that can rule out the disease are needed. Thus, the CAD software as a triage test for TB might be more suitable for the primary care level in settings where access to radiologists is limited than the tertiary care level.…”

Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study

“…25 Several studies have compared the accuracy of AI and radio logists; however, the number of radiologists included in these studies was often less than ten. 14,26,27 Here we evaluate a deep-learning model designed to assist clinicians in the interpretation of chest x-rays, encompassing the full range of clinically relevant findings on frontal and lateral chest x-rays.…”

Development and evaluation of an artificial intelligence system for COVID-19 diagnosis

“…Recent research shows that AI algorithms can even achieve or exceed the performance of human experts in certain medical image diagnosis tasks, including lung diseases [11][12][13][14][15][16][17] . Comparing to other lung diseases, such as lung nodule detection [18][19][20] , tuberculosis diagnosis 16,21 , and lung cancer screening 15 , differentiating COVID-19 from other pneumonias has unique difficulty, i.e., high similarity of pneumonias of different types (especially in early stage) and large variations in different stages of the same type. Hence, developing AI diagnosis algorithm specific to COVID-19 is necessary.…”