BACKGROUND: This study addresses the suicide risk predicting challenge by exploring the predictive ability of machine learning (ML) models integrated with theory-driven psychological risk factors in real-time crisis hotline chats. More importantly, w...
Oral surgery, oral medicine, oral pathology and oral radiology
Feb 12, 2024
OBJECTIVE: We examined the effectiveness and feasibility of the Mask Region-based Convolutional Neural Network (Mask R-CNN) for automatic detection of cephalometric landmarks on lateral cephalometric radiographs (LCRs).
Journal of vascular and interventional radiology : JVIR
Feb 12, 2024
PURPOSE: To validate the sensitivity and specificity of a 3-dimensional (3D) convolutional neural network (CNN) artificial intelligence (AI) software for lung lesion detection and to establish concordance between AI-generated needle paths and those u...
Journal of orthopaedic research : official publication of the Orthopaedic Research Society
Feb 11, 2024
Elderly patients often have more than one disease that affects walking behavior. An objective tool to identify which disease is the main cause of functional limitations may aid clinical decision making. Therefore, we investigated whether gait pattern...
OBJECTIVES: To develop and validate a deep learning-based approach to automatically measure the patellofemoral instability (PFI) indices related to patellar height and trochlear dysplasia in knee magnetic resonance imaging (MRI) scans.
OBJECTIVES: To develop and share a deep learning method that can accurately identify optimal inversion time (TI) from multi-vendor, multi-institutional and multi-field strength inversion scout (TI scout) sequences for late gadolinium enhancement card...
OBJECTIVES: Utilising readily available clinical variables, we aimed to develop and validate a novel machine learning (ML) model to predict severe coronary calcification, and further assessed its prognostic significance.
This study aimed to enhance performance, identify additional predictors, and improve the interpretability of biopsychosocial machine learning models for low back pain (LBP). Using survey data from a 6-year nationwide study involving 17,609 adults age...
OBJECTIVE: Successful total hip arthroplasty relies on accurate preoperative planning. However, the conventional preoperative planning, a two-dimensional method using X-ray template, has shown poor reliability of predicting component size. To our kno...
PURPOSE: Identifying factors predicting the spontaneous passage of distal ureteral stones and evaluating the effectiveness of artificial intelligence in prediction.
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