The present study aimed to develop a machine learning network to diagnose middle ear diseases with tympanic membrane images and to identify its assistive role in the diagnostic process. The medical records of subjects who underwent ear endoscopy tests were reviewed. From these records, 2272 diagnostic tympanic membranes images were appropriately labeled as normal, otitis media with effusion (OME), chronic otitis media (COM), or cholesteatoma and were used for training. We developed the “ResNet18 + Shuffle” network and validated the model performance. Seventy-one representative cases were selected to test the final accuracy of the network and resident physicians. We asked 10 resident physicians to make diagnoses from tympanic membrane images with and without the help of the machine learning network, and the change of the diagnostic performance of resident physicians with the aid of the answers from the machine learning network was assessed. The devised network showed a highest accuracy of 97.18%. A five-fold validation showed that the network successfully diagnosed ear diseases with an accuracy greater than 93%. All resident physicians were able to diagnose middle ear diseases more accurately with the help of the machine learning network. The increase in diagnostic accuracy was up to 18% (1.4% to 18.4%). The machine learning network successfully classified middle ear diseases and was assistive to clinicians in the interpretation of tympanic membrane images.

Kim D, et al. J Korean Med Sci. 2021 Jun;36(27):e175. https://doi.org/10.3346/jkms.2021.36.e175

Background: This study aimed to develop an algorithm for multilabel classification according to the distance from carina to endotracheal tube (ETT) tip (absence, shallow > 70 mm, 30 mm ≤ proper ≤ 70 mm, and deep position < 30 mm) with the application of automatic segmentation of the trachea and the ETT on chest radiographs using deep convolutional neural network (CNN). Methods: This study was a retrospective study using plain chest radiographs. We segmented the trachea and the ETT on images and labeled the classification of the ETT position. We proposed models for the classification of the ETT position using EfficientNet B0 with the application of automatic segmentation using Mask R-CNN and ResNet50. Primary outcomes were favorable performance for automatic segmentation and four-label classification through five-fold validation with segmented images and a test with non-segmented images. Results: Of 1985 images, 596 images were manually segmented and consisted of 298 absence, 97 shallow, 100 proper, and 101 deep images according to the ETT position. In five-fold validations with segmented images, Dice coefficients [mean (SD)] between segmented and predicted masks were 0.841 (0.063) for the trachea and 0.893 (0.078) for the ETT, and the accuracy for four-label classification was 0.945 (0.017). In the test for classification with 1389 non-segmented images, overall values were 0.922 for accuracy, 0.843 for precision, 0.843 for sensitivity, 0.922 for specificity, and 0.843 for F1-score. Conclusions: Automatic segmentation of the ETT and trachea images and classification of the ETT position using deep CNN with plain chest radiographs could achieve good performance and improve the physician’s performance in deciding the appropriateness of ETT depth.

Recent studies utilizing deep convolutional neural networks (CNN) have described the central venous catheter (CVC) on chest radiography images. However, there have been no studies for the classification of the CVC tip position with a definite criterion on the chest radiograph. This study aimed to develop an algorithm for the automatic classification of proper depth with the application of automatic segmentation of the trachea and the CVC on chest radiographs using a deep CNN. This was a retrospective study that used plain chest supine anteroposterior radiographs. The trachea and CVC were segmented on images and three labels (shallow, proper, and deep position) were assigned based on the vertical distance between the tracheal carina and CVC tip. We used a two-stage approach model for the automatic segmentation of the trachea and CVC with U-net⁺⁺ and automatic classification of CVC placement with EfficientNet B4. The primary outcome was a successful three-label classification through five-fold validations with segmented images and a test with segmentation-free images. Of a total of 808 images, 207 images were manually segmented and the overall accuracy of the five-fold validation for the classification of three-class labels (mean (SD)) of five-fold validation was 0.76 (0.03). In the test for classification with 601 segmentation-free images, the average accuracy, precision, recall, and F1-score were 0.82, 0.73, 0.73, and 0.73, respectively. We achieved the highest accuracy value of 0.91 in the shallow position label, while the highest F1-score was 0.82 in the deep position label. A deep CNN can achieve a comparative performance in the classification of the CVC position based on the distance from the carina to the CVC tip as well as automatic segmentation of the trachea and CVC on plain chest radiographs.

Purpose: This study aimed to develop and validate an automatic segmentation algorithm for the boundary delineation of ten wrist bones, consisting of eight carpal and two distal forearm bones, using a convolutional neural network (CNN). Methods: We performed a retrospective study using adult wrist radiographs. We labeled the ground truth masking of wrist bones, and propose that the Fine Mask R-CNN consisted of wrist regions of interest (ROI) using a Single-Shot Multibox Detector (SSD) and segmentation via Mask R-CNN, plus the extended mask head. The primary outcome was an improvement in the prediction of delineation via the network combined with ground truth masking, and this was compared between two networks through five-fold validations. Results: In total, 702 images were labeled for the segmentation of ten wrist bones. The overall performance (mean (SD] of Dice coefficient) of the auto-segmentation of the ten wrist bones improved from 0.93 (0.01) using Mask R-CNN to 0.95 (0.01) using Fine Mask R-CNN (p < 0.001). The values of each wrist bone were higher when using the Fine Mask R-CNN than when using the alternative (all p < 0.001). The value derived for the distal radius was the highest, and that for the trapezoid was the lowest in both networks. Conclusion: Our proposed Fine Mask R-CNN model achieved good performance in the automatic segmentation of ten overlapping wrist bones derived from adult wrist radiographs.

Currently, protective clothing used in clinical field is the most representative example of efforts to reduce radiation exposure to radiation workers. However, lead is classified as a substance harmful to the human body that can cause lead poisoning. Therefore, research on the development of lead-free radiation shielding bodies is being conducted. In this study, the shielding body was manufactured by changing the size, layer, and height of the nozzle, using a 90.7% pure tungsten filament, a 3D printer material, and we compared its performance with existing protection tools. Our findings revealed that the shielding rate of the mixed tungsten filament was higher than that of the existing protective tools, confirming its potency to replace lead as the most protective material in clinical field.