Objective: The present study aimed to verify the classification performance of deep learning (DL) models for diagnosing fractures of the mandibular condyle on panoramic radiographs using data sets from two hospitals and to compare their internal and external validities. Methods: Panoramic radiographs of 100 condyles with and without fractures were collected from two hospitals and a fivefold cross-validation method was employed to construct and evaluate the DL models. The internal and external validities of classification performance were evaluated as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Results: For internal validity, high classification performance was obtained, with AUC values of >0.85. Conversely, external validity for the data sets from the two hospitals exhibited low performance. Using combined data sets from both hospitals, the DL model exhibited high performance, which was slightly superior or equal to that of the internal validity but without a statistically significant difference. Conclusion: The constructed DL model can be clinically employed for diagnosing fractures of the mandibular condyle using panoramic radiographs. However, the domain shift phenomenon should be considered when generalizing DL systems.

Endoscopy is widely applied in the examination of gastric cancer. However, extensive knowledge and experience are required, owing to the need to examine the lesion while manipulating the endoscope. Various diagnostic support techniques have been reported for this examination. In our previous study, segmentation of invasive areas of gastric cancer was performed directly from endoscopic images and the detection sensitivity per case was 0.98. This method has challenges of false positives and computational costs because segmentation was applied to all healthy images that were captured during the examination. In this study, we propose a cascaded deep learning model to perform categorization of endoscopic images and identification of the invasive region to solve the above challenges. Endoscopic images are first classified as normal, showing early gastric cancer and showing advanced gastric cancer using a convolutional neural network. Segmentation on the extent of gastric cancer invasion is performed for the images classified as showing cancer using two separate U-Net models. In an experiment, 1208 endoscopic images collected from healthy subjects, 533 images collected from patients with early stage gastric cancer, and 637 images from patients with advanced gastric cancer were used for evaluation. The sensitivity and specificity of the proposed approach in the detection of gastric cancer via image classification were 97.0% and 99.4%, respectively. Furthermore, both detection sensitivity and specificity reached 100% in a case-based evaluation. The extent of invasion was also identified at an acceptable level, suggesting that the proposed method may be considered useful for the classification of endoscopic images and identification of the extent of cancer invasion.

Diabetic retinopathy (DR) is the most frequent cause of blindness. Microaneurysm (MA) is an early symptom of DR. Therefore, the detection of MA is important for the early detection of DR. We have proposed an automated MA detection method based on double-ring filter, but it has given many false positives. In this paper, we propose an MA detection method based on eigenvalue analysis using a Hessian matrix, with an aim to improve MA detection. After image preprocessing, the MA candidate regions were detected by eigenvalue analysis using the Hessian matrix in green-channeled retinal fundus images. Then, 126 features were calculated for each candidate region. By a threshold operation based on feature analysis, false positive candidates were removed. The candidate regions were then classified either as MA or false positive using artificial neural networks (ANN) based on principal component analysis (PCA). The 126 features were reduced to 25 components by PCA, and were then inputted to ANN. When the method was evaluated on visible MAs using 25 retinal images from the retinopathy online challenge (ROC) database, the true positive rate was 73%, with eight false positives per image.

Skeletal muscle segmentation of the L3 slice can be used to estimate total body skeletal muscle mass. However, site-specific three-dimensional (3D) segmentation of each region, such as the erector spinae, quadratus lumborum, psoas major, oblique muscle, and rectus abdominis, in the L3 slice has not yet been achieved for the accurate measurement of skeletal muscle mass. Herein, we propose a method for site-specific 3D segmentation of skeletal muscles in the L3 slice from body CT images. We focused on the characteristics of the erector spinae muscle (ESM), which can be simultaneously observed with other skeletal muscles on craniocaudal slices and can be accurately segmented using machine learning. We introduce a segmentation method with ESM (w/ESM) using 2D U-Net for the simultaneous learning of the erector spinae and skeletal muscles, which are recognized as targets. In a three-fold cross-validation using 30 CT image cases, the mean Dice value of the baseline method, without ESM (wo/ESM), was 0.637, whereas that for the segmentation of skeletal muscles by w/ESM was 0.864, an improvement of 0.227. Our method improves the accuracy of site-specific segmentation of skeletal muscles within the L3 slice and helps evaluate the skeletal muscles through 3D imaging. This effect of w/ESM was confirmed for the skeletal muscles within the L3 slice as well as the trapezius and supraspinatus muscles. These results demonstrate the effectiveness of simultaneously learning the erector spinae and skeletal muscles in improving the accuracy of site-specific segmentation of skeletal muscles.