In a multicenter study, 12 radiologists assisted by deep convolutional neural network software detected malignant pulmonary nodules on chest radiographs with a higher sensitivity and fewer false-po... Background Multicenter studies are required to validate the added benefit of using deep convolutional neural network (DCNN) software for detecting malignant pulmonary nodules on chest radiographs. ...

Coronary artery disease, which is mostly caused by atherosclerotic narrowing of the coronary artery lumen, is a leading cause of death. Coronary angiography is the standard method to estimate the severity of coronary artery stenosis, but is frequently limited by intra- and inter-observer variations. We propose a deep-learning algorithm that automatically recognizes stenosis in coronary angiographic images. The proposed method consists of key frame detection, deep learning model training for classification of stenosis on each key frame, and visualization of the possible location of the stenosis. Firstly, we propose an algorithm that automatically extracts key frames essential for diagnosis from 452 right coronary artery angiography movie clips. Our deep learning model is then trained with image-level annotations to classify the areas narrowed by over 50 %. To make the model focus on the salient features, we apply a self-attention mechanism. The stenotic locations are visualized using the activated area of feature maps with gradient-weighted class activation mapping. The automatically detected key frame was very close to the manually selected key frame (average distance (1.70 ± 0.12) frame per clip). The model was trained with key frames on internal datasets, and validated with internal and external datasets. Our training method achieved high frame-wise area-under-the-curve of 0.971, frame-wise accuracy of 0.934, and clip-wise accuracy of 0.965 in the average values of cross-validation evaluations. The external validation results showed high performances with the mean frame-wise area-under-the-curve of (0.925 and 0.956) in the single and ensemble model, respectively. Heat map visualization shows the location for different types of stenosis in both internal and external data sets. With the self-attention mechanism, the stenosis could be precisely localized, which helps to accurately classify the stenosis by type. Our automated classification algorithm could recognize and localize coronary artery stenosis highly accurately. Our approach might provide the basis for a screening and assistant tool for the interpretation of coronary angiography.

OBJECTIVE. The purpose of this study was to evaluate the usefulness of C-arm conebeam CT–guided percutaneous transthoracic lung biopsy of small (≤ 2 cm) lung nodules.MATERIALS AND METHODS. Twenty-seven patients who underwent C-arm cone-beam CT–guided percutaneous transthoracic lung biopsy of small (≤ 2 cm) lung nodules were enrolled in the study. The mean nodule size was 13 ± 4 mm in diameter. These lesions were divided into two groups according to size: 10 lesions 1 cm or smaller and 17 lesions larger than 1 cm. The diagnostic accuracy, sensitivity, and specificity in the two groups were compared by use of Fisher's exact test. Radiation dose in a female humanoid phantom was measured with radiophotoluminescent glass dosimeters. The average patient dose was calculated.RESULTS. Eighteen malignant (67%) and eight benign (30%) nodules and one (3%) indeterminate nodule were found. The overall sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 94%, 89%, 94%, 89%, a...

Vaccinated patients with COVID-19 breakthrough infections showed fewer chest CT findings of pneumonia compared to unvaccinated patients. Background Since vaccines against coronavirus disease 2019 (COVID-19) became available, rare breakthrough infections have been reported despite their high efficacies. Purpose To evaluate the clinic...

Medical schools need to take the initiative and actively seek countermeasures against student infection. It is essential that medical schools keep constant communication with their index hospitals and the involved personnel. In order to assure student learning, medical schools may consider offering distant education with online technology.