Observational studyBMJ open2024
Detection of fibrosing interstitial lung disease-suspected chest radiographs using a deep learning-based computer-aided detection system: a retrospective, observational study.
Observational study in BMJ open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed, 5 citations in OpenAlex.
- Enhancing Explainability in AI-Based Interstitial Lung Disease Detection via Multi-Class Learning Based on Lesion Areas.Journal of imaging informatics in medicine · 2026Article
- Screening for lung fibrosis using serum surfactant protein-D, KL-6, and a deep learning algorithm on chest radiographs: a prospective observational study.BMC pulmonary medicine · 2025Observational
- Computer-Aided Evaluation of Interstitial Lung Diseases.Diagnostics (Basel, Switzerland) · 2025Review
- A comparative accuracy study of multimodal LLMs, VLM and agent-based framework for pulmonary nodule detection on chest radiographs.Frontiers in digital health · 2025Article
Corrections and comments
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Authors and funding
11 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo investigate the effectiveness of BMAX, a deep learning-based computer-aided detection system for detecting fibrosing interstitial lung disease (ILD) on chest radiographs among non-expert and expert physicians in the real-world clinical setting.
designRetrospective, observational study.
settingThis study used chest radiograph images consecutively taken in three medical facilities with various degrees of referral. Three expert ILD physicians interpreted each image and determined whether it was a fibrosing ILD-suspected image (fibrosing ILD positive) or not (fibrosing ILD negative). Interpreters, including non-experts and experts, classified each of 120 images extracted from the pooled data for the reading test into positive or negative for fibrosing ILD without and with the assistance of BMAX.
participantsChest radiographs of patients aged 20 years or older with two or more visits that were taken during consecutive periods were accumulated. 1251 chest radiograph images were collected, from which 120 images (24 positive and 96 negative images) were randomly extracted for the reading test. The interpreters for the reading test were 20 non-expert physicians and 5 expert physicians (3 pulmonologists and 2 radiologists). PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was the comparison of area under the receiver-operating characteristic curve (ROC-AUC) for identifying fibrosing ILD-positive images by non-experts without versus with BMAX. The secondary outcome was the comparison of sensitivity, specificity and accuracy by non-experts and experts without versus with BMAX.
resultsThe mean ROC-AUC of non-expert interpreters was 0.795 (95% CI; 0.765 to 0.825) without BMAX and 0.825 (95% CI; 0.799 to 0.850) with BMAX (p=0.005). After using BMAX, sensitivity was improved from 0.744 (95% CI; 0.697 to 0.791) to 0.802 (95% CI; 0.754 to 0.850) among non-experts (p=0.003), but not among experts (p=0.285). Specificity and accuracy were not changed after using BMAX among either non-expert or expert interpreters.
conclusionBMAX was useful for detecting fibrosing ILD-suspected chest radiographs for non-expert physicians. TRIAL REGISTRATION NUMBER: jRCT1032220090.
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