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Related Experiment Video

Updated: Apr 9, 2026

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

Agraj Abhishek1, Manjeet Singh Chalga2, Reetika Malik Yadav3

  • 1Department of AI and Digital Twin Division, Institute for Plasma Research, Ahmadabad, Gujarat, India.

The Indian Journal of Medical Research
|April 8, 2026
PubMed
Summary

This study developed DeepCXR v1.1, an AI tool that accurately detects chest X-ray abnormalities without clinical data. This innovation aids in large-scale screening programs, especially in areas with limited access to radiologists.

Keywords:
Abnormality detectionArtificial intelligenceChest X-RayLung segmentationTuberculosis

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Area of Science:

  • Artificial Intelligence in Medical Imaging
  • Radiology and Medical Diagnostics

Background:

  • Chest X-rays (CXR) are crucial for detecting thoracic abnormalities like tuberculosis (TB).
  • A shortage of radiologists in peripheral areas hinders timely CXR interpretation.
  • Existing screening methods face challenges in scalability and accessibility.

Purpose of the Study:

  • To develop and validate DeepCXR v1.1, an AI tool for automated detection of chest X-ray abnormalities.
  • To create a metadata-independent tool suitable for large-scale public health screening.
  • To enhance early disease detection capabilities in resource-limited settings.

Main Methods:

  • Trained an AI tool on over 282,000 annotated CXR images from 54,000 patients across 18 Indian centers.
  • Employed a multi-model ensemble architecture with lung segmentation and lesion-specific models.
  • Validated the tool on multiple datasets, including a prospective validation of 13,927 CXR images.

Main Results:

  • Achieved a sensitivity of 92.2% and specificity of 77.4% in blind prospective validation.
  • Demonstrated strong generalisability across diverse training and validation datasets.
  • Validated by expert committees and health technology assessment panels.

Conclusions:

  • DeepCXR v1.1 provides a scalable, interpretable, and robust AI solution for augmenting radiological screening.
  • The tool can improve early detection of diseases through automated CXR analysis.
  • Its offline functionality on basic hardware makes it ideal for resource-limited settings.