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

作者：A. Abhishek, Manjeet Singh Chalga, Reetika Malik Yadav, Kshitij Agarwal, Vikram Vohra, Atul Tayade, Amber Kumar, Mandira Varma-Basil, Parul Mrigpuri, Hirva Manek, Padma Badhe, Sarika Gupta, Shuchi Bhatt, Anjan Kumar Das, Dhruvendra Pandey, C. Ponnuraja, Sanghamitra Pati, Jyotirmayee Turuk, Pratibha Narang, Amita Athavale, Ira Shah, SS Mohanty, Arun M. George, Rashmi Rodrigues, Sushant Satish Mane, Anant Mohan, C.R. Chaudhary, Manoranjan Pattnaik, Radha Munje, Bhavna Dhingra, Ajay Singh, Rekha Meena, Kirti Chaturvedy, Dhruva Chaudhary, P I Singh, Jeetendra Kumar Patra, Abhishek Sharma, Manika Sharma, Manjula Singh · 发表于：The Indian Journal of Medical Research · 年份：2026 · DOI：10.25259/ijmr_1854_2025 · 被引用次数：3 · 研究领域：COVID-19 diagnosis using AI、Ultrasound in Clinical Applications、Lung Cancer Diagnosis and Treatment

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 assessm...