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Adolescent idiopathic scoliosis (AIS), which typically occurs in patients between the ages of 10 and 18, can be caused by a variety of reasons, and no definitive cause has been found. Early diagnosis of AIS or timely recognition of progression is crucial for the prevention of spinal deformity and the reduction of the risk of surgery or postponement. However, it remains a significant challenge.

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Purpose: The study aimed to develop a deep learning model for rapid, automated measurement of full-spine X-rays in adolescents with Adolescent Idiopathic Scoliosis (AIS). A significant challenge in this field is the time-consuming nature of manual measurements and the inter-individual variability in these measurements. To address these challenges, we utilized RTMpose deep learning technology to automate the process.

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AI-assisted Digital Video Analysis Reveals Changes in Gait Among Three-Day Event Horses During Competition.

J Equine Vet Sci

January 2025

University of Florida Department of Animal Sciences, 2250 Shealy Dr., Gainesville, FL, United States, 32611; UF Genetics Institute, 2033 Mowry Rd., Gainesville, FL, United States, 32611. Electronic address:

The value and welfare of a performance horse are closely tie to locomotor behaviors, but we lack objective and quantitative measures for these characteristics, and qualitative approaches for assessing gait do not provide measures suitable for large-scale biomechanical research studies. Digital video analysis utilizing artificial intelligence-based strategies promise to meet the need for an economical, accurate, repeatable and objective technique for field quantification of equine locomotion. Here we describe pilot work using a consumer-level digital video camera to capture high-resolution and high-speed videos of horses moving at the trot during mandatory inspections for international-level eventing competitions.

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Purpose: This study aims to address the challenging estimation of trajectories from freehand ultrasound examinations by means of registration of automatically generated surface points. Current approaches to inter-sweep point cloud registration can be improved by incorporating heatmap predictions, but practical challenges such as label-sparsity or only partially overlapping coverage of target structures arise when applying realistic examination conditions.

Methods: We propose a pipeline comprising three stages: (1) Utilizing a Free Point Transformer for coarse pre-registration, (2) Introducing HeatReg for further refinement using support point clouds, and (3) Employing instance optimization to enhance predicted displacements.

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Purpose: Lung fissure segmentation on CT images often relies on 3D convolutional neural networks (CNNs). However, 3D-CNNs are inefficient for detecting thin structures like the fissures, which make up a tiny fraction of the entire image volume. We propose to make lung fissure segmentation more efficient by using geometric deep learning (GDL) on sparse point clouds.

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