Orthopedics

Neural network and layer-wise relevance propagation reveal how ice hockey protective equipment restricts players' motion.

Understanding the athlete's movements and the restrictions incurred by protective equipment is crucial for improving the equipment and subsequently, the athlete's performance. The task of equipment improvement is especially challenging in sports including advanced manoeuvres such as ice hockey and requires a holistic approach guiding the researcher's attention toward the right variables. The purpo...

Current status and dilemmas of osteoporosis screening tools: A narrative review.

OBJECTIVE: This review aims to explore the strengths and dilemmas of existing osteoporosis screening tools and suggest possible ways of optimization, in addition to exploring the potential of AI-integrated X-ray imaging in osteoporosis screening, especially its ability to improve accuracy and applicability to different populations. To break through the dilemma of low accessibility, poor clinical t...

Role of Artificial intelligence model in prediction of low back pain using T2 weighted MRI of Lumbar spine.

BACKGROUND: Low back pain (LBP), the primary cause of disability, is the most common musculoskeletal disorder globally and the primary cause of disabi...

Identification of endocrine-disrupting chemicals targeting key OP-associated genes via bioinformatics and machine learning.

Osteoporosis (OP), a metabolic disorder predominantly impacting postmenopausal women, has seen considerable progress in diagnosis and treatment over t...

AI in Surgery: Navigating Trends and Managerial Implications Through Bibliometric and Text Mining Odyssey.

This research employs bibliometric and text-mining analysis to explore artificial intelligence (AI) advancements within surgical procedures. The growi...

A radiographic artificial intelligence tool to identify candidates suitable for partial knee arthroplasty.

INTRODUCTION: Knee osteoarthritis is a prevalent condition frequently necessitating knee replacement surgery, with demand projected to rise substantia...

A Robotic Clamped-Kinematic System to Study Knee Ligament Injury.

Knee ligament injury is among the most common sports injuries and is associated with long recovery periods and low return-to-sport rates. Unfortunatel...

Comparison of Patients With and Without Predicted Surgical Indication Between Clusters of Adult Spine Deformity (ASD) Patients.

STUDY DESIGN: Multicentric, retrospective analysis of prospectively collected data.

Artificial Intelligence in Temporal Bone Imaging: A Systematic Review.

OBJECTIVE: The human temporal bone comprises more than 30 identifiable anatomical components. With the demand for precise image interpretation in this...

Development of the machine learning model that is highly validated and easily applicable to predict radiographic knee osteoarthritis progression.

Many models using the aid of artificial intelligence have been recently proposed to predict the progression of knee osteoarthritis. However, previous ...

Machine learning models based on CT radiomics features for distinguishing benign and malignant vertebral compression fractures in patients with malignant tumors.

BACKGROUND: Radiomics has become an important tool for distinguishing benign and malignant vertebral compression fractures (VCFs). It is more clinical...

Machine learning model outperforms the ACS Risk Calculator in predicting non-home discharge following primary total knee arthroplasty.

PURPOSE: Despite the increase in outpatient total knee arthroplasty (TKA) procedures, many patients are still discharged to non-home locations followi...

Machine learning algorithms for the evaluation of risk by tick-borne pathogens in Europe.

BACKGROUND: Tick-borne pathogens pose a major threat to human health worldwide. Understanding the epidemiology of tick-borne diseases to reduce their ...

Joint self-supervised and supervised contrastive learning for multimodal MRI data: Towards predicting abnormal neurodevelopment.

The integration of different imaging modalities, such as structural, diffusion tensor, and functional magnetic resonance imaging, with deep learning m...

Convolutional neural network based detection of early stage Parkinson's disease using the six minute walk test.

The heterogeneity of Parkinson's disease (PD) presents considerable challenges for accurate diagnosis, particularly during early-stage disease, when t...

Combining 2.5D deep learning and conventional features in a joint model for the early detection of sICH expansion.

The study aims to investigate the potential of training efficient deep learning models by using 2.5D (2.5-Dimension) masks of sICH. Furthermore, it in...

Empowering surgeons: will artificial intelligence change oral and maxillofacial surgery?

Artificial Intelligence (AI) can enhance the precision and efficiency of diagnostics and treatments in oral and maxillofacial surgery (OMS), leveragin...

Combining the probabilistic finite element model and artificial neural network to study nutrient levels in the human intervertebral discs.

BACKGROUND: Diffusion distance and diffusivity are known to affect nutrient transport rates, but the probabilistic analysis of these two factors remai...

Recurrent Neural Network Enabled Continuous Motion Estimation of Lower Limb Joints From Incomplete sEMG Signals.

Decoding continuous human motion from surface electromyography (sEMG) in advance is crucial for improving the intelligence of exoskeleton robots. Howe...

Methodology and development of a machine learning probability calculator: Data heterogeneity limits ability to predict recurrence after arthroscopic Bankart repair.

PURPOSE: The aim of this study was to develop and train a machine learning (ML) algorithm to create a clinical decision support tool (i.e., ML-driven ...