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Machine learning for detecting moyamoya disease in plain skull radiography using a convolutional neural network
- Source :
- EBioMedicine
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
Abstract
- Background Recently, 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. Methods Three 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. Findings For 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%. Interpretation DL 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.
- Subjects :
- Adult
Male
0301 basic medicine
Research paper
Computer science
Radiography
Convolutional neural network
General Biochemistry, Genetics and Molecular Biology
Machine Learning
Young Adult
03 medical and health sciences
0302 clinical medicine
Image Processing, Computer-Assisted
medicine
Humans
Receiver operating characteristic
business.industry
Deep learning
Skull
Reproducibility of Results
Pattern recognition
General Medicine
Middle Aged
030104 developmental biology
medicine.anatomical_structure
ROC Curve
Viscerocranium
Data Interpretation, Statistical
030220 oncology & carcinogenesis
Test set
Female
Neural Networks, Computer
Artificial intelligence
Moyamoya Disease
business
Classifier (UML)
Moyamoya
Algorithms
Subjects
Details
- ISSN :
- 23523964
- Volume :
- 40
- Database :
- OpenAIRE
- Journal :
- EBioMedicine
- Accession number :
- edsair.doi.dedup.....125ca9d1e90f18b13a624e1dc840df3d