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Backgrounds: Establishment of quantitative, objective evaluation tool for facial palsy has been a big issue for clinicians and researchers, and in an era of artificial intelligence, AI-driven video analysis can be considered a reasonable solution for this long-discussed issue. We introduced facial keypoint detection, which detects facial landmarks with 68 points, but existing models had been organized almost solely with images of healthy individuals and low accuracy was presumed in the prediction of asymmetric faces of facial palsy patients.The accuracy of existing model was assessed through applying it to the movies of 30 facial palsy patients. Qualitative review clearly showed its insufficiency; prone to detect patients' faces as symmetric, and unable to detect closure of the eyes. Thus, we decided to enhance the model through machine learning process of annotation (fine-tuning)., Methods: 1181 images extracted from the movies of 196 patients were enrolled in the training, and these images underwent manual correction of 68 keypoints. The annotated data was integrated into the previous model with a stack of two Hourglass networks combined with Channel Aggregation Block., Results: The post-annotation model showed improvement in normalized mean error from 0.026 to 0.018, and also qualitatively keypoint detection on each facial unit revealed improvements., Conclusion: Our strict control of the inter- and intra-annotator variability successfully fine-tuned the presenting model, and we consider our new model as one of promising solutions for objective assessment of facial palsy., (Copyright © 2024 by the American Society of Plastic Surgeons.)

Study Design: Retrospective diagnostic study., Objective: To automatically detect osteolytic bone metastasis lesions in the thoracolumbar region using conventional computed tomography (CT) scans, we developed a new deep learning (DL)-based computer-aided detection model., Summary of Background Data: Radiographic detection of bone metastasis is often difficult, even for orthopedic surgeons and diagnostic radiologists, with a consequent risk for pathologic fracture or spinal cord injury. If we can improve detection rates, we will be able to prevent the deterioration of patients' quality of life at the end stage of cancer., Materials and Methods: This study included CT scans acquired at Tokyo Medical and Dental University (TMDU) Hospital between 2016 and 2022. A total of 263 positive CT scans that included at least one osteolytic bone metastasis lesion in the thoracolumbar spine and 172 negative CT scans without bone metastasis were collected for the datasets to train and validate the DL algorithm. As a test data set, 20 positive and 20 negative CT scans were separately collected from the training and validation datasets. To evaluate the performance of the established artificial intelligence (AI) model, sensitivity, precision, F1-score, and specificity were calculated. The clinical utility of our AI model was also evaluated through observer studies involving six orthopaedic surgeons and six radiologists., Results: Our AI model showed a sensitivity, precision, and F1-score of 0.78, 0.68, and 0.72 (per slice) and 0.75, 0.36, and 0.48 (per lesion), respectively. The observer studies revealed that our AI model had comparable sensitivity to orthopaedic or radiology experts and improved the sensitivity and F1-score of residents., Conclusion: We developed a novel DL-based AI model for detecting osteolytic bone metastases in the thoracolumbar spine. Although further improvement in accuracy is needed, the current AI model may be applied to current clinical practice., Level of Evidence: Level III., Competing Interests: The authors report no conflicts of interest., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.)

Melanoma is the cutaneous neoplasm responsible for more patient deaths in all countries. BRAF mutations are the most common driver mutation and with the development of molecular targeted therapy, the precise knowledge of BRAF status has become increasingly important. Evaluation of BRAF mutation status has routinely been performed by polymerase chain reaction, a time consuming and expensive technique. Immunohistochemistry has been suggested as a cheaper alternative, but it has not gained general acceptance. A retrospective observational study in a cohort of 106 patients with invasive melanoma was conducted in order to develop and evaluate a machine learning approach to predict BRAF status using clinical and histologic variables. We compared the performance of different common machine learning algorithms and use SHapley Additive exPlanations (SHAP) to explain individual predictions and extract medical insights to define a heuristic model to estimate BRAF mutation probability. The Extreme Gradient Boosting algorithms obtained the best performance. Interpretability of models shows that the most important variables to estimate BRAF mutation probability are: age, Breslow thickness, and Breslow density. Based in this interpretation and medical knowledge, a simplify heuristic model is proposed to predict BRAF status using only 7 variables and obtain a performance of 0.878 of area under the curve. We propose a heuristic model that could be used by clinicians to obtain a good estimator of BRAF mutation probability., Competing Interests: The authors declare no conflict of interest., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)