ArticleEBioMedicine2019
Machine learning for detecting moyamoya disease in plain skull radiography using a convolutional neural network.
Article in EBioMedicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 51 citations in OpenAlex.
- Evaluation of deep learning algorithms in detecting moyamoya disease: a systematic review and single-arm meta-analysis.Neurosurgical review · 2024Pooled it
- A Deep-Learning Model for Stroke Subtype Identification in Moyamoya Disease Based on Digital Subtraction Angiography.Translational stroke research · 2026Article
- Imaging-Based Diagnostic Approaches in Moyamoya Disease: A Scoping Review.Journal of clinical medicine · 2026Review
- Automated meningioma detection using skull X ray images with deep learning and machine learning classifiers.Scientific reports · 2025Article
- Diagnosis of trigeminal neuralgia based on plain skull radiography using convolutional neural network.Scientific reports · 2025Article
- Deep learning for the detection of moyamoya angiopathy using T2-weighted images: a multicenter study.Quantitative imaging in medicine and surgery · 2025Article
- Research progress of artificial intelligence in moyamoya disease.Frontiers in neurology · 2025Review
- Deep learning model for automated diagnosis of moyamoya disease based on magnetic resonance angiography.EClinicalMedicine · 2024Article
- The new era of artificial intelligence in neuroradiology: current research and promising tools.Arquivos de neuro-psiquiatria · 2024Article
- Screening of Moyamoya Disease From Retinal Photographs: Development and Validation of Deep Learning Algorithms.Stroke · 2024Observational
- Multiomics and blood-based biomarkers of moyamoya disease: protocol of Moyamoya Omics Atlas (MOYAOMICS).Chinese neurosurgical journal · 2024Article
- Multi-pose-based convolutional neural network model for diagnosis of patients with central lumbar spinal stenosis.Scientific reports · 2024Article
- Machine learning-based nomogram: integrating MRI radiomics and clinical indicators for prognostic assessment in acute ischemic stroke.Frontiers in neurology · 2024Article
- High-resolution magnetic resonance vessel wall imaging provides new insights into Moyamoya disease.Frontiers in neuroscience · 2024Review
- Application of Pseudo-Three-Dimensional Residual Network to Classify the Stages of Moyamoya Disease.Brain sciences · 2023Article
- The automatic evaluation of steno-occlusive changes in time-of-flight magnetic resonance angiography of moyamoya patients using a 3D coordinate attention residual network.Quantitative imaging in medicine and surgery · 2023Article
- Diagnosis of nasal bone fractures on plain radiographs via convolutional neural networks.Scientific reports · 2022Article
- External Validation of Deep Learning Algorithms for Radiologic Diagnosis: A Systematic Review.Radiology. Artificial intelligence · 2022Article
- A Convolutional Neural Network Model for Detecting Sellar Floor Destruction of Pituitary Adenoma on Magnetic Resonance Imaging Scans.Frontiers in neuroscience · 2022Article
- CT perfusion-based delta-radiomics models to identify collateral vessel formation after revascularization in patients with moyamoya disease.Frontiers in neuroscience · 2022Article
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10 authors at 4 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundRecently, innovative attempts have been made to identify moyamoya disease (MMD) by focusing on the morphological differences in the head of MMD patients. Following the recent revolution in the development of deep learning (DL) algorithms, we designed this study to determine whether DL can distinguish MMD in plain skull radiograph images.
methodsThree hundred forty-five skull images were collected as an MMD-labeled dataset from patients aged 18 to 50 years with definite MMD. As a control-labeled data set, 408 skull images of trauma patients were selected by age and sex matching. Skull images were partitioned into training and test datasets at a 7:3 ratio using permutation. A total of six convolution layers were designed and trained. The accuracy and area under the receiver operating characteristic (AUROC) curve were evaluated as classifier performance. To identify areas of attention, gradient-weighted class activation mapping was applied. External validation was performed with a new dataset from another hospital.
findingsFor the institutional test set, the classifier predicted the true label with 84·1% accuracy. Sensitivity and specificity were both 0·84. AUROC was 0·91. MMD was predicted by attention to the lower face in most cases. Overall accuracy for external validation data set was 75·9%.
interpretationDL can distinguish MMD cases within specific ages from controls in plain skull radiograph images with considerable accuracy and AUROC. The viscerocranium may play a role in MMD-related skull features. FUND: This work was supported by grant no. 18-2018-029 from the Seoul National University Bundang Hospital Research Fund.
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