Deep Chest: an artificial intelligence model for multi-disease diagnosis by chest x-rays
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Background
Artificial intelligence is increasingly being used for analyzing image data in medicine.
Objectives
We aimed to develop a computer vision artificial intelligence (AI) application using limited training material to aid in the multi-label, multi-disease diagnosis of chest X-rays.
Methods
We trained an EfficientNetB0 pre-trained model, leveraging transfer learning and deep learning techniques. Six thoracic disease categories were defined, and the model was initially trained on images sourced online and chest X-rays from a hospital database for training and internal validation. Subsequently, the model underwent external validation.
Results
In constructing and validating Deep Chest, we utilized 453 images, achieving an area under curve (AUC) of 0.98, sensitivity of 0.98, specificity of 0.80, and accuracy of 0.83. Notably, for diagnosing masses or nodules, the sensitivity, specificity, and accuracy were 0.97, 0.81, and 0.83, respectively. We deployed Deep Chest as a free experimental web application.
Conclusions
This tool demonstrated high accuracy in diagnosing both single and coexisting pulmonary pathologies, including pulmonary masses or nodules. Deep Chest thus represents a promising AI-based solution for enhancing diagnostic capabilities in thoracic radiology, with the potential to be utilized across various medical disciplines, especially in scenarios where expert support is limited.
