ArticleNPJ digital medicine2021
Recalibration of deep learning models for abnormality detection in smartphone-captured chest radiograph.
Article in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep learning for pneumothorax diagnosis: a systematic review and meta-analysis.European respiratory review : an official journal of the European Respiratory Society · 2023Pooled it
- Database and AI Diagnostic Tools Improve Understanding of Lung Damage, Correlation of Pulmonary Disease and Brain Damage in COVID-19.Sensors (Basel, Switzerland) · 2022Pooled it
- Computer-Aided Analysis of Photographed Chest X-Ray Films Performs Well Compared With Trained Radiologists.Mayo Clinic proceedings. Digital health · 2026Article
- Effects of Image Degradation on Deep Neural Network Classification of Scaphoid Fracture Radiographs: Comparison Study of Different Noise Types.JMIR medical informatics · 2026Article
- AI-Powered Smartphone Application for Measuring Hallux Valgus Angle From Radiographs Displayed on a Monitor.Journal of foot and ankle research · 2025Article
- Smartphone Imaging and AI: A Commentary on Cardiac Device Classification.Radiology. Artificial intelligence · 2024Article
- Development and External Validation of an Artificial Intelligence-Based Method for Scalable Chest Radiograph Diagnosis: A Multi-Country Cross-Sectional Study.Research (Washington, D.C.) · 2024Article
- Artificial Intelligence in Paediatric Tuberculosis.Pediatric radiology · 2023Review
- Machine Learning Augmented Interpretation of Chest X-rays: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- A 178-clinical-center experiment of integrating AI solutions for lung pathology diagnosis.Scientific reports · 2023Article
- Impact of localized fine tuning in the performance of segmentation and classification of lung nodules from computed tomography scans using deep learning.Frontiers in oncology · 2023Article
- Developing and Validating Multi-Modal Models for Mortality Prediction in COVID-19 Patients: a Multi-center Retrospective Study.Journal of digital imaging · 2022Article
- Use of Artificial Intelligence-Based Software as Medical Devices for Chest Radiography: A Position Paper from the Korean Society of Thoracic Radiology.Korean journal of radiology · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Image-based teleconsultation using smartphones has become increasingly popular. In parallel, deep learning algorithms have been developed to detect radiological findings in chest X-rays (CXRs). However, the feasibility of using smartphones to automate this process has yet to be evaluated. This study developed a recalibration method to build deep learning models to detect radiological findings on CXR photographs. Two publicly available databases (MIMIC-CXR and CheXpert) were used to build the models, and four derivative datasets containing 6453 CXR photographs were collected to evaluate model performance. After recalibration, the model achieved areas under the receiver operating characteristic curve of 0.80 (95% confidence interval: 0.78-0.82), 0.88 (0.86-0.90), 0.81 (0.79-0.84), 0.79 (0.77-0.81), 0.84 (0.80-0.88), and 0.90 (0.88-0.92), respectively, for detecting cardiomegaly, edema, consolidation, atelectasis, pneumothorax, and pleural effusion. The recalibration strategy, respectively, recovered 84.9%, 83.5%, 53.2%, 57.8%, 69.9%, and 83.0% of performance losses of the uncalibrated model. We conclude that the recalibration method can transfer models from digital CXRs to CXR photographs, which is expected to help physicians' clinical works.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.