Numerical evaluation of internal femur osteosynthesis based on a biomechanical model of the loading in the proximal equine hindlimb.

Femoral fractures are often considered lethal for adult horses because femur osteosynthesis is still a surgical challenge. For equine femur osteosynthesis, primary stability is essential, but the detailed physiological forces occurring in the hindlimb are largely unknown. The objective of this study was to create a numerical testing environment to evaluate equine femur osteosynthesis based on physiological conditions.

Multicentric development and validation of a multi-scale and multi-task deep learning model for comprehensive lower extremity alignment analysis.

Osteoarthritis of the knee, a widespread cause of knee disability, is commonly treated in orthopedics due to its rising prevalence. Lower extremity misalignment, pivotal in knee injury etiology and management, necessitates comprehensive mechanical alignment evaluation via frequently-requested weight-bearing long leg radiographs (LLR). Despite LLR's routine use, current analysis techniques are error-prone and time-consuming.

Recommender-based bone tumour classification with radiographs-a link to the past.

Objectives: To develop an algorithm to link undiagnosed patients to previous patient histories based on radiographs, and simultaneous classification of multiple bone tumours to enable early and specific diagnosis.

Materials And Methods: For this retrospective study, data from 2000 to 2021 were curated from our database by two orthopaedic surgeons, a radiologist and a data scientist. Patients with complete clinical and pre-therapy radiographic data were eligible.

Validation of a Robotic Testbench for Evaluating Biomechanical Effects of Implant Rotation in Total Knee Arthroplasty on a Cadaveric Specimen.

In this study, we developed and validated a robotic testbench to investigate the biomechanical compatibility of three total knee arthroplasty (TKA) configurations under different loading conditions, including varus-valgus and internal-external loading across defined flexion angles. The testbench captured force-torque data, position, and quaternion information of the knee joint. A cadaver study was conducted, encompassing a native knee joint assessment and successive TKA testing, featuring femoral component rotations at -5°, 0°, and +5° relative to the transepicondylar axis of the femur.

SAM-X: sorting algorithm for musculoskeletal x-ray radiography.

Objective: To develop a two-phased deep learning sorting algorithm for post-X-ray image acquisition in order to facilitate large musculoskeletal image datasets according to their anatomical entity.

Methods: In total, 42,608 unstructured and pseudonymized radiographs were retrieved from the PACS of a musculoskeletal tumor center. In phase 1, imaging data were sorted into 1000 clusters by a self-supervised model.

Development of an Exoskeleton Platform of the Finger for Objective Patient Monitoring in Rehabilitation.

This paper presents the application of an adaptive exoskeleton for finger rehabilitation. The system consists of a force-controlled exoskeleton of the finger and wireless coupling to a mobile application for the rehabilitation of complex regional pain syndrome (CRPS) patients. The exoskeleton has sensors for motion detection and force control as well as a wireless communication module.

Development and evaluation of machine learning models based on X-ray radiomics for the classification and differentiation of malignant and benign bone tumors.

Objectives: To develop and validate machine learning models to distinguish between benign and malignant bone lesions and compare the performance to radiologists.

Methods: In 880 patients (age 33.1 ± 19.

An Adaptive Mechatronic Exoskeleton for Force-Controlled Finger Rehabilitation.

This paper presents a novel mechatronic exoskeleton architecture for finger rehabilitation. The system consists of an underactuated kinematic structure that enables the exoskeleton to act as an adaptive finger stimulator. The exoskeleton has sensors for motion detection and control.

Multitask Deep Learning for Segmentation and Classification of Primary Bone Tumors on Radiographs.

Background An artificial intelligence model that assesses primary bone tumors on radiographs may assist in the diagnostic workflow. Purpose To develop a multitask deep learning (DL) model for simultaneous bounding box placement, segmentation, and classification of primary bone tumors on radiographs. Materials and Methods This retrospective study analyzed bone tumors on radiographs acquired prior to treatment and obtained from patient data from January 2000 to June 2020.

Improving mandibular reconstruction by using topology optimization, patient specific design and additive manufacturing?-A biomechanical comparison against miniplates on human specimen.

In this study, topology optimized, patient specific osteosynthesis plates (TOPOS-implants) are evaluated for the mandibular reconstruction using fibula segments. These shape optimized implants are compared to a standard treatment with miniplates (thickness: 1.0 mm, titanium grade 4) in biomechanical testing using human cadaveric specimen.