ArticleScientific reports2021
Detection of the location of pneumothorax in chest X-rays using small artificial neural networks and a simple training process.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 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
- Large-scale evaluation of multimodal large language models for pneumothorax detection.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Article
- Classification of Lung Diseases Using an Attention-Based Modified DenseNet Model.Journal of imaging informatics in medicine · 2024Article
- Article
- 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
- RoMIA: a framework for creating Robust Medical Imaging AI models for chest radiographs.Frontiers in radiology · 2023Article
- Evaluation of an Artificial Intelligence Model for Detection of Pneumothorax and Tension Pneumothorax in Chest Radiographs.JAMA network open · 2022Article
- Research on Lung Ultrasound Image Classification Based on Compressed Sensing.Journal of healthcare engineering · 2022Article
- Prediction of locations in medical images using orthogonal neural networks.European journal of radiology open · 2021Article
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5 authors.
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Abstract
The purpose of this study was to evaluate the diagnostic performance achieved by using fully-connected small artificial neural networks (ANNs) and a simple training process, the Kim-Monte Carlo algorithm, to detect the location of pneumothorax in chest X-rays. A total of 1,000 chest X-ray images with pneumothorax were taken randomly from NIH (the National Institutes of Health) public image database and used as the training and test sets. Each X-ray image with pneumothorax was divided into 49 boxes for pneumothorax localization. For each of the boxes in the chest X-ray images contained in the test set, the area under the receiver operating characteristic (ROC) curve (AUC) was 0.882, and the sensitivity and specificity were 80.6% and 83.0%, respectively. In addition, a common currently used deep-learning method for image recognition, the convolution neural network (CNN), was also applied to the same dataset for comparison purposes. The performance of the fully-connected small ANN was better than that of the CNN. Regarding the diagnostic performances of the CNN with different activation functions, the CNN with a sigmoid activation function for fully-connected hidden nodes was better than the CNN with the rectified linear unit (RELU) activation function. This study showed that our approach can accurately detect the location of pneumothorax in chest X-rays, significantly reduce the time delay incurred when diagnosing urgent diseases such as pneumothorax, and increase the effectiveness of clinical practice and patient care.
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