Orthopedics

Should my recommendation letter be written by artificial intelligence?

Letters of recommendation are increasingly important for the residency match. We assessed whether an artificial intelligence (AI) tool could help in writing letters of recommendation by analyzing recommendation letters written by 3 academic staff and AI duplicate versions for 13 applicants. The preferred letters were selected by 3 blinded orthopedic program directors based on a pre-determined set ...

Skin-inspired, sensory robots for electronic implants.

Drawing inspiration from cohesive integration of skeletal muscles and sensory skins in vertebrate animals, we present a design strategy of soft robots, primarily consisting of an electronic skin (e-skin) and an artificial muscle. These robots integrate multifunctional sensing and on-demand actuation into a biocompatible platform using an in-situ solution-based method. They feature biomimetic desig...

Application of Machine Learning Methods to Investigate Joint Load in Agility on the Football Field: Creating the Model, Part I.

Laboratory studies have limitations in screening for anterior cruciate ligament (ACL) injury risk due to their lack of ecological validity. Machine le...

The Effect of Sensor Feature Inputs on Joint Angle Prediction across Simple Movements.

The use of wearable sensors, such as inertial measurement units (IMUs), and machine learning for human intent recognition in health-related areas has ...

Comparison of Machine Learning Algorithms Fed with Mobility-Related and Baropodometric Measurements to Identify Temporomandibular Disorders.

Temporomandibular disorders (TMDs) refer to a group of conditions that affect the temporomandibular joint, causing pain and dysfunction in the jaw joi...

A comprehensive approach for osteoporosis detection through chest CT analysis and bone turnover markers: harnessing radiomics and deep learning techniques.

PURPOSE: The main objective of this study is to assess the possibility of using radiomics, deep learning, and transfer learning methods for the analys...

Identification of ion channel-related genes as diagnostic markers and potential therapeutic targets for osteoarthritis through bioinformatics and machine learning-based approaches.

BACKGROUND: Osteoarthritis (OA) is a debilitating joint disorder characterized by the progressive degeneration of articular cartilage. Although the ro...

Identification of shared gene signatures and pathways for diagnosing osteoporosis with sarcopenia through integrated bioinformatics analysis and machine learning.

BACKGROUND: Prior studies have suggested a potential relationship between osteoporosis and sarcopenia, both of which can present symptoms of compromis...

Multitask Learning for Joint Diagnosis of Multiple Mental Disorders in Resting-State fMRI.

Facing the increasing worldwide prevalence of mental disorders, the symptom-based diagnostic criteria struggle to address the urgent public health con...

Prediction of Achilles Tendon Force During Common Motor Tasks From Markerless Video.

Remodeling of the Achilles tendon (AT) is partly driven by its mechanical environment. AT force can be estimated with neuromusculoskeletal (NMSK) mode...

Deep learning-based quantification of osteonecrosis using magnetic resonance images in Gaucher disease.

Gaucher disease is one of the most common lysosomal storage disorders. Osteonecrosis is a principal clinical manifestation of Gaucher disease and ofte...

Surgical factors play a critical role in predicting functional outcomes using machine learning in robotic-assisted total knee arthroplasty.

PURPOSE: Predictive models help determine predictive factors necessary to improve functional outcomes after total knee arthroplasty (TKA). However, no...

Development and Validation of a Machine Learning Model for Bone Metastasis in Prostate Cancer: Based on Inflammatory and Nutritional Indicators.

OBJECTIVE: To establish a predictive model for prostate cancer bone metastasis utilizing multiple machine learning algorithms.

A novel case-finding strategy based on artificial intelligence for the systematic identification and management of individuals with osteoporosis or at varying risk of fragility fracture.

UNLABELLED: An artificial intelligence-based case-finding strategy has been developed to systematically identify individuals with osteoporosis or at v...

Machine learning model based on radiomics features for AO/OTA classification of pelvic fractures on pelvic radiographs.

Depending on the degree of fracture, pelvic fracture can be accompanied by vascular damage, and in severe cases, it may progress to hemorrhagic shock....

Machine learning-based identification and immune characterization of ferroptosis-related molecular clusters in osteoarthritis and validation.

Osteoarthritis (OA), a degenerative joint disease, involves synovial inflammation, subchondral bone erosion, and cartilage degeneration. Ferroptosis, ...

Artificial intelligence model system for bone age assessment of preschool children.

BACKGROUD: Our study aimed to assess the impact of inter- and intra-observer variations when utilizing an artificial intelligence (AI) system for bone...

Proximal femur fracture detection on plain radiography via feature pyramid networks.

Hip fractures exceed 250,000 cases annually in the United States, with the worldwide incidence projected to increase by 240-310% by 2050. Hip fracture...

A joint training of healthcare line managers and health and safety representatives in facilitating occupational health interventions: a feasibility study protocol for the Co-pilot project.

Healthcare employees are experiencing poor wellbeing at an increasing rate. The healthcare workforce is exposed to challenging tasks and a high work p...

Enhanced reliability and time efficiency of deep learning-based posterior tibial slope measurement over manual techniques.

PURPOSE: Multifaceted factors contribute to inferior outcomes following anterior cruciate ligament (ACL) reconstruction surgery. A particular focus is...