Development and validation of a deep learning algorithm detecting 10 common abnormalities on chest radiographs
Open accessAbstract
We aimed to develop a deep learning algorithm detecting 10 common abnormalities (DLAD-10) on chest radiographs, and to evaluate its impact in diagnostic accuracy, timeliness of reporting and workflow efficacy. DLAD-10 was trained with 146 717 radiographs from 108 053 patients using a ResNet34-based neural network with lesion-specific channels for 10 common radiological abnormalities (pneumothorax, mediastinal widening, pneumoperitoneum, nodule/mass, consolidation, pleural effusion, linear atelectasis, fibrosis, calcification and cardiomegaly). For external validation, the performance of DLAD-10 on a same-day computed tomography (CT)-confirmed dataset (normal:abnormal 53:147) and an open-source dataset (PadChest; normal:abnormal 339:334) was compared with that of three radiologists. Separate simulated reading tests were conducted on another dataset adjusted to real-world disease prevalence in the emergency department, consisting of four critical, 52 urgent and 146 nonurgent cases. Six radiologists participated in the simulated reading sessions with and without DLAD-10. DLAD-10 exhibited area under the receiver operating characteristic curve values of 0.895–1.00 in the CT-confirmed dataset and 0.913–0.997 in the PadChest dataset. DLAD-10 correctly classified significantly more critical abnormalities (95.0% (57/60)) than pooled radiologists (84.4% (152/180); p=0.01). In simulated reading tests for emergency department patients, pooled readers detected significantly more critical (70.8% (17/24)versus29.2% (7/24); p=0.006) and urgent (82.7% (258/312)versus78.2% (244/312); p=0.04) abnormalities when aided by DLAD-10. DLAD-10 assistance shortened the mean±sdtime-to-report critical and urgent radiographs (640.5±466.3versus3371.0±1352.5 s and 1840.3±1141.1versus2127.1±1468.2 s, respectively; all p<0.01) and reduced the mean±sdinterpretation time (20.5±22.8versus23.5±23.7 s; p<0.001). DLAD-10 showed excellent performance, improving radiologists' performance and shortening the reporting time for critical and urgent cases.
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The Lee et al. (2022) multimodal model employed a previously validated and CE-approved chest X-ray abnormality detection model (Nam et al., 2021) to infer imaging features from chest X-rays and fused this with clinical and laboratory data to predict prognosis and required interventions for COVID-19 patients.
“This multimodal model employed a previously validated and CE-approved chest X-ray abnormality detection model (Nam et al., 2021) to infer imaging features from chest X-rays and fused this with clinical and laboratory data to predict prognosis and the required interventions for patients with COVID-19.”
From Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications (2025) · cites this paper as Nam et al., 2021
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- Authors
- Nam, J. G., Kim, M., Park, J., Hwang, E. J., Lee, J. H., Hong, J. H., Goo, J. M., & Park, C. M.
- Affiliations
- Seoul National University (KR)
- Lunit (South Korea) (KR)
- Corresponding author
- Park, Chang Min
- ORCID
- Venue
- European Respiratory Journal
- Publisher
- European Respiratory Society (ERS)
- Published
- 2020-11-26
- Volume
- 57
- Issue
- 5
- Pages
- 2003061
- ISSN
- 0903-1936 (ISSN-L)
- 1399-3003
- PMC
- PMC8134811
- OpenAlex
- W3107467375
- Cited by
- 103
- References
- 31
- Citations by year
- 2026: 11 · 2025: 14 · 2024: 22 · 2023: 33 · 2022: 14 · 2021: 9
- FWCI
- 6.81
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- European Respiratory Journal (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Seoul National University Open Repository (Seoul National University) (submittedVersion)
- Funders
- National Research Foundation of Korea (grant number: NRF-2018R1A5A1060031)
- Seoul National University Hospital (grant number: 03-2019-0190)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- chest radiograph, deep learning, ResNet34, pneumothorax, mediastinal widening, pneumoperitoneum, lung consolidation, pleural effusion, atelectasis, cardiomegaly, fibrosis, emergency department, diagnostic accuracy, reporting timeliness, radiologist performance, clinical workflow
- MeSH terms
- Deep Learning, Algorithms, Humans, Lung Diseases, Radiography, Retrospective Studies, Radiography, Thoracic
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