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.

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Publication Details

Journal
The Indian Journal of Medical Research
Published
2026-03-13
DOI
https://doi.org/10.25259/ijmr_1854_2025
Primary Topic
COVID-19 diagnosis using AI
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Manjeet Singh Chalga, Kirti Chaturvedy, A. Abhishek, Jyotirmayee Turuk et al.
The Indian Journal of Medical Research
COVID-19 diagnosis using AI
article

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

Manjeet Singh Chalga, Kirti Chaturvedy, A. Abhishek, Jyotirmayee Turuk, Jeetendra Kumar Patra, Hirva Manek, Rashmi Rodrigues, Sushant Satish Mane, Dhruva Chaudhary, Parul Mrigpuri, Padma Badhe, Manoranjan Pattnaik, Arun M. George, Kshitij Agarwal, Sanghamitra Pati, Amber Kumar, Abhishek Sharma, Dhruvendra Pandey, Rekha Meena, Ajay Singh, Manish Kumar Singh, Anjan Kumar Das, P I Singh, Vikram Vohra, Atul Tayade, Mandira Varma-Basil, Manika Sharma, SS Mohanty, Reetika Malik Yadav, Pratibha Narang, Shuchi Bhatt, C. Ponnuraja, Bhavna Dhingra, Amita Athavale, C.R. Chaudhary, Sarika Gupta, Ira Shah, Radha Munje, Anant Mohan
article en

Abstract

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.

The Indian Journal of Medical ResearchVol. 163
University of Delhi (IN), Pandit Bhagwat Dayal Sharma Post Graduate Institute of Medical Sciences (IN), Grant Medical College and Sir Jamshedjee Jeejeebhoy Group of Hospitals (IN), University College of Medical Sciences (IN), King Edward Memorial Hospital and Seth G.S. Medical College (IN), Sarojini Naidu Medical College (IN), Indian Council of Social Science Research (IN), Regional Medical Research Center (IN), King George's Medical University (IN), Sriram Chandra Bhanja Medical College Hospital (IN), Indian Council of Medical Research (IN), LRS Institute of Tuberculosis and Respiratory Diseases (IN), Indira Gandhi Government Medical College & Hospital (IN), Institute for Plasma Research (IN), All India Institute of Medical Sciences Bhopal (IN), Government Medical College (IN), ICMR - National Institute of Immunohaemotology (IN), Regional Medical Research Centre (IN), Bai Jerbai Wadia Hospital for Children (IN), National Institute of Research in Tuberculosis (IN), St.John's Medical College Hospital (IN), National Institute for Implementation Research on Non-Communicable Diseases (IN), Agartala Government Medical College (IN), All India Institute of Medical Sciences (IN), Mahatma Gandhi Institute of Medical Sciences (IN)
Postgraduate Institute of Medical Education and Research, Chandigarh, Indian Council of Medical Research, All-India Institute of Medical Sciences, University of Hyderabad, Iran Telecommunication Research Center, Bhopal Memorial Hospital and Research Centre
Openalex Percentile: Top 23%
COVID-19 diagnosis using AI
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