Artificial intelligence-assisted approach to assessing bowel wall thickness in pediatric inflammatory bowel disease using intestinal ultrasound images.

Background And Aim: Intestinal ultrasound (IUS) potentially spares patients from repeated endoscopies under sedation and eliminates the need for alternative imaging modalities like magnetic resonance enterography and computed tomography enterography scans. However, interpreting IUS images is challenging for physicians due to the time-intensive process of identifying markers indicative of inflammatory bowel disease (IBD). This study aims for fully automating the analysis of pediatric IBD to distinguish between abnormal and normal cases.

DetSegDiff: A joint periodontal landmark detection and segmentation in intraoral ultrasound using edge-enhanced diffusion-based network.

Individuals with malocclusion require an orthodontic diagnosis and treatment plan based on the severity of their condition. Assessing and monitoring changes in periodontal structures before, during, and after orthodontic procedures is crucial, and intraoral ultrasound (US) imaging has been shown a promising diagnostic tool in imaging periodontium. However, accurately delineating and analyzing periodontal structures in US videos is a challenging task for clinicians, as it is time-consuming and subject to interpretation errors.

Neural implicit surface reconstruction of freehand 3D ultrasound volume with geometric constraints.

Three-dimensional (3D) freehand ultrasound (US) is a widely used imaging modality that allows non-invasive imaging of medical anatomy without radiation exposure. Surface reconstruction of US volume is vital to acquire the accurate anatomical structures needed for modeling, registration, and visualization. However, traditional methods cannot produce a high-quality surface due to image noise.

Discriminative patterns of white matter changes in Alzheimer's.

Changes in structural connectivity of the Alzheimer's brain have not been widely studied utilizing cutting-edge methodologies. This study develops an efficient structural connectome-based convolutional neural network (CNN) to classify the AD and uses explanations of CNNs' choices in classification to pinpoint the discriminative changes in white matter connectivity in AD. A CNN architecture has been developed to classify normal control (NC) and AD subjects from the weighted structural connectome.