Artificial intelligence (AI)-driven ensemble model for comprehensive chest X-ray abnormality detection and deployment

Background and objectives Chest X-rays (CXR) are widely used for screening of thoracic abnormalities, particularly for tuberculosis (TB) in public health settings. However, the lack of trained radiologists in peripheral areas limits timely interpretation. This study presents the development and validation of DeepCXR v1.1, an artificial intelligence (AI)-powered tool designed to identify radiological chest abnormalities without relying on metadata or clinical inputs, making it ideal for large-scale screening programmes. Methods In present multicentric study, AI tool was trained on over 282,000 annotated data points from 54,000 CXR images (36,500 abnormal and 17,500 normal) collected from children and adults from 18 centres across 11 States in India. The tool employs a multi-model ensemble architecture including lung segmentation and lesion-specific models to classify images as normal or abnormal. The tool was validated on multiple datasets, and the final independent validation was done on 13927 CXR images collected prospectively from patients coming to the outpatient clinics of the departments of Medicine and Chest of participating centres. Results The tool demonstrated strong generalisability across training and validation datasets, achieving sensitivity of 92.2% [95% confidence interval (CI) 91.6, 92.7] and specificity of 77.4% (95% CI 76.1, 78.6) in a blind prospective validation. Its performance was independently validated by expert committees and health technology assessment panels. Advanced post-processing modules were integrated to enhance detection accuracy, particularly for complex anatomical regions such as the heart and diaphragm. Interpretation and conclusions DeepCXR v1.1, an indigenously developed AI tool for detecting abnormalities in chest X-rays offers a scalable, interpretable, and robust solution for augmenting radiological screening and improving early disease detection. The tool’s ability to function offline on basic hardware further supports its use in resource-limited settings.