Developing a Machine Learning-Based Automated Patient Engagement Estimator for Telehealth: Algorithm Development and Validation Study.

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.

Minimal data poisoning attack in federated learning for medical image classification: An attacker perspective.

The privacy-sensitive nature of medical image data is often bounded by strict data sharing regulations that necessitate the need for novel modeling and analysis techniques. Federated learning (FL) enables multiple medical institutions to collectively train a deep neural network without sharing sensitive patient information. In addition, FL uses its collaborative approach to address challenges related to the scarcity and non-uniform distribution of heterogeneous medical domain data.

Evaluation of Crestal Bone Loss after Placement of Bone Graft around the Oral Implant with/without Platelet-rich Plasma.

Aim: The current investigation aimed to evaluate the crestal bone loss after placement of bone graft around the oral implant with/without platelet-rich plasma (PRP).

Materials And Methods: Forty patients seeking for crown supported by dental implants to replace at least one lost tooth were included in the present study. The participants were divided into two groups at random ( = 20): Group I: Received tricalcium phosphate (TCP) along with PRP and group II: Received TCP without PRP.

ViT-HHO: Optimized vision transformer for diabetic retinopathy detection using Harris Hawk optimization.

Diabetic retinopathy (DR) is a significant cause of vision impairment globally, emphasizing the importance of timely and precise detection to prevent severe consequences. This study presents an optimized Vision Transformer (ViT) model that incorporates Harris Hawk Optimization (HHO) to improve the automated detection of diabetic retinopathy (DR). The ViT architecture utilizes self-attention mechanisms to capture local and global features in retinal images.

Demystifying the effect of receptive field size in U-Net models for medical image segmentation.

Purpose: Medical image segmentation is a critical task in healthcare applications, and U-Nets have demonstrated promising results in this domain. We delve into the understudied aspect of receptive field (RF) size and its impact on the U-Net and attention U-Net architectures used for medical imaging segmentation.

Approach: We explore several critical elements including the relationship among RF size, characteristics of the region of interest, and model performance, as well as the balance between RF size and computational costs for U-Net and attention U-Net methods for different datasets.