From Classification to Localization and Clinical Validation: Large-Scale Development of a Deep Learning System for Thoracic Disease Detection on Chest Radiographs in Thailand

2026-07-10

AI Summary

This study addresses the critical shortage of radiologists in Thailand and Southeast Asia, which severely limits the capacity for interpreting chest X-rays (CXRs), by proposing Inspectra CXR v5—the first end-to-end deep learning system deployed in a large-scale Thai clinical setting that integrates multi-label disease classification with weakly supervised lesion localization. Built upon a DenseNet-121 backbone, the system introduces an innovative Attend-and-Compare module and a probabilistic class activation map (PCAM) aggregation layer, trained on over 870,000 report-annotated CXRs. It achieves an average in-house AUROC of 0.994 and demonstrates robust generalization across 13 hospitals with an AUROC of 0.970. Lesion localization accuracy reaches 77.9%, while radiologist evaluations confirm high consistency in both classification (93.6%) and localization (94.7%), with a system usability score of 89.

Abstract

Chest radiography (CXR) remains the most widely used thoracic imaging modality, yet expert interpretation is constrained by a severe shortage of radiologists in Thailand and across Southeast Asia. Local adaptation of deep learning models to Thai data has been shown to substantially improve accuracy on Thai populations. Here we present the development and comprehensive validation of the chest radiograph analysis model in Inspectra CXR version 5, a deep learning system that performs multi-label thoracic disease classification and weakly supervised lesion localization within a single model. The architecture couples a DenseNet-121 backbone with Attend-and-Compare Modules (ACM) and a Probabilistic Class Activation Map (PCAM) aggregation layer, producing a per-condition classification score and heatmap simultaneously. The model was developed on 874,858 frontal chest radiographs with paired radiologist reports from Siriraj Hospital, Bangkok. On a held-out, radiologist-verified in-domain test set of 19,871 cases, it achieved a mean AUROC of 0.994 (mean sensitivity 92.4%, specificity 98.6%) across nine clinically important conditions. On an independent generalization set of 5,992 cases from 13 hospitals across Thailand, the mean AUROC was 0.970, indicating robust transfer across sites. For localization, evaluated on 4,549 radiologist-annotated cases, the model attained a mean lesion-localization fraction (LLF) of 77.9% at 0.59 non-lesion localizations per image. In a usability evaluation with five thoracic radiologists, the system reached a classification concordance of 93.6%, a localization concordance of 94.7%, and a mean System Usability Scale (SUS) score of 89. These results indicate that a locally developed, localization-capable CXR system can deliver high accuracy, generalize across heterogeneous Thai hospitals, and earn the trust of practicing radiologists.

Problem

• chest radiography
• thoracic disease detection
• radiologist shortage
• lesion localization
• clinical validation

Innovation

• weakly supervised localization
• DenseNet-121
• Attend-and-Compare Modules
• Probabilistic Class Activation Map
• multi-label thoracic disease classification