ArticleEmergency radiology2020
Can AI outperform a junior resident? Comparison of deep neural network to first-year radiology residents for identification of pneumothorax.
Article in Emergency radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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16 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.
- Deep learning for pneumothorax diagnosis: a systematic review and meta-analysis.European respiratory review : an official journal of the European Respiratory Society · 2023Pooled it
- Artificial Intelligence-Empowered Radiology-Current Status and Critical Review.Diagnostics (Basel, Switzerland) · 2025Review
- Potential strength and weakness of artificial intelligence integration in emergency radiology: a review of diagnostic utilizations and applications in patient care optimization.Emergency radiology · 2024Review
- Artificial Intelligence vs. Doctors: Diagnosing Necrotizing Enterocolitis on Abdominal Radiographs.Journal of pediatric surgery · 2024Article
- A Survey on Explainable Artificial Intelligence (XAI) Techniques for Visualizing Deep Learning Models in Medical Imaging.Journal of imaging · 2024Article
- The Quantitative Evaluation of Automatic Segmentation in Lumbar Magnetic Resonance Images.Neurospine · 2024Article
- Vision transformer to differentiate between benign and malignant slices inScientific reports · 2024Article
- Multi-pose-based convolutional neural network model for diagnosis of patients with central lumbar spinal stenosis.Scientific reports · 2024Article
- Radiomics-based machine learning for automated detection of Pneumothorax in CT scans.PloS one · 2024Article
- Effect of Contrast Level and Image Format on a Deep Learning Algorithm for the Detection of Pneumothorax with Chest Radiography.Journal of digital imaging · 2023Article
- Performance and Usability of Code-Free Deep Learning for Chest Radiograph Classification, Object Detection, and Segmentation.Radiology. Artificial intelligence · 2023Article
- Chest X-ray in Emergency Radiology: What Artificial Intelligence Applications Are Available?Diagnostics (Basel, Switzerland) · 2023Review
- Fully Convolutional Network for the Semantic Segmentation of Medical Images: A Survey.Diagnostics (Basel, Switzerland) · 2022Review
- Visual Transformers and Convolutional Neural Networks for Disease Classification on Radiographs: A Comparison of Performance, Sample Efficiency, and Hidden Stratification.Radiology. Artificial intelligence · 2022Article
- Limited generalizability of deep learning algorithm for pediatric pneumonia classification on external data.Emergency radiology · 2022Article
- Value of artificial intelligence model based on unenhanced computed tomography of urinary tract for preoperative prediction of calcium oxalate monohydrate stonesAnnals of translational medicine · 2021Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
purposeTo (1) develop a deep learning system (DLS) using a deep convolutional neural network (DCNN) for identification of pneumothorax, (2) compare its performance to first-year radiology residents, and (3) evaluate the ability of a DLS to augment radiology residents by detecting missed pneumothoraces.
methodsThis was a retrospective study performed in September 2018. We obtained 112,120 chest radiographs (CXRs) from the NIH ChestXray14 database, of which 4360 cases (4%) were labeled as pneumothorax by natural language processing. We utilized 111,518 CXRs to train and validate the ResNet-152 DCNN pretrained on ImageNet to identify pneumothorax. DCNN testing was performed on a hold-out set of 602 CXRs, whose groundtruth was determined by a cardiothoracic radiologist. Two first-year radiology residents evaluated the test CXRs for presence of pneumothorax. Receiver operating characteristic (ROC) curves were generated for each evaluator with area under the curve (AUC) compared using the DeLong parametric method.
resultsThe DCNN achieved AUC of 0.841 for identification of pneumothorax at a rate of 1980 images/min. In contrast, both first-year residents achieved significantly higher AUCs of 0.942 and 0.905 (p < 0.01 for both compared to DCNN), but at a slower rate of two images/min. The DCNN identified 3 of 31 (9.7%) additional pneumothoraces missed by at least one of the residents.
conclusionA DLS for pneumothorax identification had lower AUC than 1st-year radiology residents, but interpreted images > 1000× as fast and identified 3 additional pneumothoraces missed by the residents. Our findings suggest that DLS could augment radiologists-in-training to identify potential urgent findings.
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