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

• URL: http://arxiv.org/abs/2607.09305v1
• Date: Fri, 10 Jul 2026 11:36:50 GMT
• Title: From Classification to Localization and Clinical Validation: Large-Scale Development of a Deep Learning System for Thoracic Disease Detection on Chest Radiographs in Thailand
• Abstract summary: The model was developed on 874,858 frontal chest radiographs with paired radiologist reports from Siriraj Hospital, Bangkok.<n>It achieved a mean AUROC of 0.994 (mean sensitivity 92.4%, specificity 98.6%) across nine clinically important conditions.<n>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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Auditable agentic AI for evidence-grounded thyroid ultrasound diagnosis and reporting [59.709162055944915]
  ThyroidXAgent is an agentic AI system that coordinates specialized diagnostic tools and stores their outputs as an auditable case-level evidence record.<n>It was evaluated on 28,458 non-overlapping test cases, including 8,721 cases from 35 centres in the private NHC-MISD-TUS cohort.<n>It achieved a mean Dice score of 87.21 percent for segmentation and a mean AUROC of 0.9466 for benign-malignant classification.
  arXiv  Detail & Related papers  (2026-08-12T21:02:59Z)
• A Vision-language Framework for Comparative Reasoning in Radiology [68.52471827773482]
  We formulate radiological comparison as an entity-aware cross-image reasoning problem.<n>We construct a large-scale comparative imaging resource derived from routine image-report pairs.<n>We develop MedReCo, an entity-aware visual encoder for controllable retrieval of clinically analogous cases.
  arXiv  Detail & Related papers  (2026-06-04T17:12:47Z)
• A DeepSeek-Powered AI System for Automated Chest Radiograph Interpretation in Clinical Practice [83.11942224668127]
  Janus-Pro-CXR (1B) is a chest X-ray interpretation system based on DeepSeek Janus-Pro model.<n>Our system outperforms state-of-the-art X-ray report generation models in automated report generation.
  arXiv  Detail & Related papers  (2025-12-23T13:26:13Z)
• Neural Discrete Representation Learning for Sparse-View CBCT Reconstruction: From Algorithm Design to Prospective Multicenter Clinical Evaluation [64.42236775544579]
  Cone beam computed tomography (CBCT)-guided puncture has become an established approach for diagnosing and treating thoracic tumours.<n>DeepPriorCBCT is a three-stage deep learning framework that achieves diagnostic-grade reconstruction using only one-sixth of the conventional radiation dose.
  arXiv  Detail & Related papers  (2025-11-30T12:45:02Z)
• Deep Learning-Based Breast Cancer Detection in Mammography: A Multi-Center Validation Study in Thai Population [0.0]
  Deep learning system for breast cancer detection in mammography developed using a modified EfficientNetV2 architecture with enhanced attention mechanisms.<n>Model was trained on mammograms from a major Thai medical center and validated on three distinct datasets.<n>For cancer detection, the model achieved AUROCs of 0.89, 0.96, and 0.94 on the respective datasets.
  arXiv  Detail & Related papers  (2025-05-29T11:11:41Z)
• A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers [51.45596445363302]
  GlobeReady is a clinician-friendly AI platform that enables fundus disease diagnosis without retraining, fine-tuning, or the needs for technical expertise.<n>We demonstrate high accuracy across imaging modalities: 93.9-98.5% for 11 fundus diseases using color fundus photographs (CPFs) and 87.2-92.7% for 15 fundus diseases using optic coherence tomography ( OCT) scans.<n>By leveraging training-free local feature augmentation, GlobeReady platform effectively mitigates domain shifts across centers and populations.
  arXiv  Detail & Related papers  (2025-04-22T14:17:22Z)
• Deep Radiomics Detection of Clinically Significant Prostate Cancer on Multicenter MRI: Initial Comparison to PI-RADS Assessment [0.0]
  This study analyzed biparametric (T2W and DW) prostate MRI sequences of 615 patients (mean age, 63.1 +/- 7 years) from four datasets acquired between 2010 and 2020. Deep radiomics machine learning model achieved comparable performance to PI-RADS assessment in csPCa detection at the patient-level but not at the lesion-level.
  arXiv  Detail & Related papers  (2024-10-21T17:41:58Z)
• Attention-based Saliency Maps Improve Interpretability of Pneumothorax Classification [52.77024349608834]
  To investigate chest radiograph (CXR) classification performance of vision transformers (ViT) and interpretability of attention-based saliency. ViTs were fine-tuned for lung disease classification using four public data sets: CheXpert, Chest X-Ray 14, MIMIC CXR, and VinBigData. ViTs had comparable CXR classification AUCs compared with state-of-the-art CNNs.
  arXiv  Detail & Related papers  (2023-03-03T12:05:41Z)
• VinDr-SpineXR: A deep learning framework for spinal lesions detection and classification from radiographs [0.812774532310979]
  This work aims at developing and evaluating a deep learning-based framework, named VinDr-SpineXR, for the classification and localization of abnormalities from spine X-rays. We build a large dataset, comprising 10,468 spine X-ray images from 5,000 studies, each of which is manually annotated by an experienced radiologist with bounding boxes around abnormal findings in 13 categories. The VinDr-SpineXR is evaluated on a test set of 2,078 images from 1,000 studies, which is kept separate from the training set.
  arXiv  Detail & Related papers  (2021-06-24T11:45:44Z)
• Local Adaptation Improves Accuracy of Deep Learning Model for Automated X-Ray Thoracic Disease Detection : A Thai Study [0.0]
  We present a development and testing of a deep learning algorithm for automated thoracic disease detection, utilizing 421,859 local chest radiographs. Our study shows that convolutional neural networks can achieve remarkable performance in detecting 13 common abnormality conditions on chest X-ray. This paper presents a state-of-the-art model for CXR abnormality detection, reaching an average AUROC of 0.91.
  arXiv  Detail & Related papers  (2020-04-23T05:25:26Z)
• A neural network model that learns differences in diagnosis strategies among radiologists has an improved area under the curve for aneurysm status classification in magnetic resonance angiography image series [0.0]
  This retrospective study included 3423 time-of-flight brain magnetic resonance angiography image series. The image series were read independently for aneurysm status by one of four board-certified radiologists. The constructed neural networks were trained to classify the aneurysm status of zero to five aneurysm-suspicious areas.
  arXiv  Detail & Related papers  (2020-02-03T19:19:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.