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Developing a Machine Learning-Based Automated Patient Engagement Estimator for Telehealth: Algorithm Development and Validation Study.
JMIR Form Res
January 2025
Department of Computer Science, Purdue University, West Lafayett, IN, United States.
Background: Patient engagement is a critical but challenging public health priority in behavioral health care. During telehealth sessions, health care providers need to rely predominantly on verbal strategies rather than typical nonverbal cues to effectively engage patients. Hence, the typical patient engagement behaviors are now different, and health care provider training on telehealth patient engagement is unavailable or quite limited.
View Article and Find Full Text PDFEnhancing panoramic dental imaging with AI-driven arch surface fitting: Achieving improved clarity and accuracy through an optimal reconstruction zone.
Dentomaxillofac Radiol
January 2025
Department of Oral and Maxillofacial Radiology, School of Dentistry, Pusan National University, Yangsan, 50612, Korea.
Objectives: This study aimed to develop an automated method for generating clearer, well-aligned panoramic views by creating an optimized three-dimensional (3D) reconstruction zone centered on the teeth. The approach focused on achieving high contrast and clarity in key dental features, including tooth roots, morphology, and periapical lesions, by applying a 3D U-Net deep learning model to generate an arch surface and align the panoramic view.
Methods: This retrospective study analyzed anonymized cone-beam CT (CBCT) scans from 312 patients (mean age 40 years; range 10-78; 41.
Comparison of perioperative outcomes of robot-assisted radical prostatectomy among the da Vinci, hinotori, and Hugo robot-assisted surgery systems.
J Robot Surg
January 2025
Division of Urology, Department of Surgery, Faculty of Medicine, Tottori University, 86 Nishi-cho, Yonago, Tottori, 683-8503, Japan.
In recent times, innovative surgical robotics have emerged and gained widespread adoption. This study aimed to compare the perioperative outcomes associated with robot-assisted radical prostatectomy (RARP) using three different robotic surgical systems: da Vinci surgical system (DVSS), hinotori surgical robot system (HSRS), and Hugo robot-assisted surgery system (HRASS). Our study involved a retrospective analysis of clinical data from 149 individuals who received RARP from 2022 to 2024, utilizing the DVSS (n = 81), HSRS (n = 52), and HRASS (n = 16).
View Article and Find Full Text PDFscHiClassifier: a deep learning framework for cell type prediction by fusing multiple feature sets from single-cell Hi-C data.
Brief Bioinform
November 2024
School of Software, Shandong University, No. 1500, Shunhua Road, Hi-Tech Industrial Development Zone, Jinan 250100, Shandong, China.
Single-cell high-throughput chromosome conformation capture (Hi-C) technology enables capturing chromosomal spatial structure information at the cellular level. However, to effectively investigate changes in chromosomal structure across different cell types, there is a requisite for methods that can identify cell types utilizing single-cell Hi-C data. Current frameworks for cell type prediction based on single-cell Hi-C data are limited, often struggling with features interpretability and biological significance, and lacking convincing and robust classification performance validation.
View Article and Find Full Text PDFAutomatic segmentation of MRI images for brain radiotherapy planning using deep ensemble learning.
Biomed Phys Eng Express
January 2025
Department of Physics and Astronomy, Louisiana State University, Baton Rouge, LA, United States of America.
This study aimed to develop and evaluate an efficient method to automatically segment T1- and T2-weighted brain magnetic resonance imaging (MRI) images. We specifically compared the segmentation performance of individual convolutional neural network (CNN) models against an ensemble approach to advance the accuracy of MRI-guided radiotherapy (RT) planning..
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