ArticleClinical imaging2020
Retraining an open-source pneumothorax detecting machine learning algorithm for improved performance to medical images.
Article in Clinical imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline 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
- Development and evaluation of a deep learning model for computer-aided diagnosis of neonatal pneumothorax on chest radiographs.Pediatric radiology · 2026Article
- Artificial Intelligence in Infectious Disease Diagnostic Technologies.Diagnostics (Basel, Switzerland) · 2025Review
- Human-centered AI as a framework guiding the development of image-based diagnostic tools in oncology: a systematic review.ESMO real world data and digital oncology · 2024Review
- Deep Learning-based Diagnosis and Localization of Pneumothorax on Portable Supine Chest X-ray in Intensive and Emergency Medicine: A Retrospective Study.Journal of medical systems · 2023Article
- The Subgroup Imperative: Chest Radiograph Classifier Generalization Gaps in Patient, Setting, and Pathology Subgroups.Radiology. Artificial intelligence · 2023Article
- Deep Neural Network Augments Performance of Junior Residents in Diagnosing COVID-19 Pneumonia on Chest Radiographs.Diagnostics (Basel, Switzerland) · 2023Article
- A 178-clinical-center experiment of integrating AI solutions for lung pathology diagnosis.Scientific reports · 2023Article
- Impact of multi-source data augmentation on performance of convolutional neural networks for abnormality classification in mammography.Frontiers in radiology · 2023Article
- Evaluation of an Artificial Intelligence Model for Detection of Pneumothorax and Tension Pneumothorax in Chest Radiographs.JAMA network open · 2022Article
- External Validation of Deep Learning Algorithms for Radiologic Diagnosis: A Systematic Review.Radiology. Artificial intelligence · 2022Article
- Open-Source Clinical Machine Learning Models: Critical Appraisal of Feasibility, Advantages, and Challenges.JMIR formative research · 2022Article
- Diagnostic Performance of a Deep Learning Model Deployed at a National COVID-19 Screening Facility for Detection of Pneumonia on Frontal Chest Radiographs.Healthcare (Basel, Switzerland) · 2022Article
- Pneumothorax detection in chest radiographs: optimizing artificial intelligence system for accuracy and confounding bias reduction using in-image annotations in algorithm training.European radiology · 2021Article
- Deep Learning Systems for Pneumothorax Detection on Chest Radiographs: A Multicenter External Validation Study.Radiology. Artificial intelligence · 2021Article
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2 authors.
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Abstract
purposeTo validate a machine learning model trained on an open source dataset and subsequently optimize it to chest X-rays with large pneumothoraces from our institution.
methodsThe study was retrospective in nature. The open-source chest X-ray (CXR8) dataset was dichotomized to cases with pneumothorax (PTX) and all other cases (non-PTX), resulting in 41,946 non-PTX and 4696 PTX cases for the training set and 11,120 non-PTX and 541 PTX cases for the validation set. A limited supervision machine learning model was constructed to incorporate both localized and unlocalized pathology. Cases were then queried from our health system from 2013 to 2017. A total of 159 pneumothorax and 682 non-pneumothorax cases were available for the training set. For the validation set, 48 pneumothorax and 1287 non-pneumothorax cases were available. The model was trained, a receiver operator curve (ROC) was created, and output metrics, including area under the curve (AUC), sensitivity and specificity were calculated.
resultsInitial training of the model using the CXR8 dataset resulted in an AUC of 0.90 for pneumothorax detection. Naively inferring our own validation dataset on the CXR8 trained model output an AUC of 0.59. After re-training the model with our own training dataset, the validation dataset inference output an AUC of 0.90.
conclusionOur study showed that even though you may get great results on open-source datasets, those models may not translate well to real world data without an intervening retraining process.
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