Related Experiment Video
Updated: Jan 20, 2026
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
Published on: December 15, 2023
Evaluating the Usefulness of Artificial Intelligence-based Chest X-Ray Screening in Improving Tuberculosis Detection
Abhishek Gupta1, Aswathy M Nair2, Shobha Ekka1
1Tribal TB Initiative, Piramal Swasthya Management and Research Institute, Chhattisgarh, India.
Background:
India accounts for the highest Tuberculosis (TB) burden globally. The incidence and prevalence of TB are higher in tribal population than general population. In this study, we assessed the effectiveness of artificial intelligence (AI) based chest X-ray (CXR) interpretation software device (qXR version 3), in detecting TB from a predominantly tribal population setting.
Methods:
In this multicenter prospective study, all the CXRs of patients aged > 15 years taken for any reason at 3 public health facilities in the Chhattisgarh state of India between 01 August 2023 and 31 March 2024 were included. Patients flagged by AI as TB presumptive were directed to undergo sputum testing, who are subsequently confirmed either microbiologically or clinically.
Results:
Out of 2745 CXRs screened, 363 patients (median age, 44 years [IQR: 30-53]; 261 [71.9%] male) were identified as presumptive for TB. 162 cases were confirmed with TB positivity rate of 44.63% (95% CI: 39.44-49.91). Among the AI-flagged cases, 51 (14.04%) patients were asymptomatic, and 20 (39.22%) of them were confirmed with TB. Descriptively, when compared with baseline (August-2022 to March-2023), an 80.21% (P < .001) increase in the number of TB case notifications was observed during the AI implemented period.
Conclusions:
This study highlights the potential of AI to enhance TB detection and feasibility in a resource-limited tribal setting. Above 40% of the patients flagged by AI were subsequently confirmed to have the TB disease. Additionally, the study demonstrated the potential of AI in identifying asymptomatic individuals who would otherwise have been missed or diagnosed late.
Related Concept Videos
06:37Artificial Intelligence-Based System for Detecting Attention Levels in Students
09:11Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
19:15Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale
05:49Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images
07:31Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
10:39Measurement of X-ray Beam Coherence along Multiple Directions Using 2-D Checkerboard Phase Grating