In this study, we have fabricated two types of non-invasive fiber-optic respiration sensors that can measure respiratory signals during magnetic resonance (MR) image acquisition. One is a nasal-cavity attached sensor that can measure the temperature variation of air-flow using a thermochromic pigment. The other is an abdomen attached sensor that can measure the abdominal circumference change using a sensing part composed of polymethyl-methacrylate (PMMA) tubes, a mirror and a spring. We have measured modulated light guided to detectors in the MRI control room via optical fibers due to the respiratory movements of the patient in the MR room, and the respiratory signals of the fiber-optic respiration sensors are compared with those of the BIOPAC ® system. We have verified that respiratory signals can be obtained without deteriorating the MR image. It is anticipated that the proposed fiber-optic respiration sensors would be highly suitable for respiratory monitoring during surgical procedures performed inside an MRI system.

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.FundThis work was supported by grant no. 18-2018-029 from the Seoul National University Bundang Hospital Research Fund.

An efficient method for identifying subjects at high risk of an intracranial aneurysm (IA) is warranted to provide adequate radiological screening guidelines and effectively allocate medical resources. We developed a model for pre-diagnosis IA prediction using a national claims database and health examination records. Data from the National Health Screening Program in Korea were utilized as input for several machine learning algorithms: logistic regression (LR), random forest (RF), scalable tree boosting system (XGB), and deep neural networks (DNN). Algorithm performance was evaluated through the area under the receiver operating characteristic curve (AUROC) using different test data from that employed for model training. Five risk groups were classified in ascending order of risk using model prediction probabilities. Incidence rate ratios between the lowest-and highest-risk groups were then compared. The XGB model produced the best IA risk prediction (AUROC of 0.765) and predicted the lowest IA incidence (3.20) in the lowest-risk group, whereas the RF model predicted the highest IA incidence (161.34) in the highest-risk group. The incidence rate ratios between the lowest-and highestrisk groups were 49.85, 35.85, 34.90, and 30.26 for the XGB, LR, DNN, and RF models, respectively. The developed prediction model can aid future IA screening strategies.

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