Orthopedics

XGBoost-SHAP-based interpretable diagnostic framework for knee osteoarthritis: a population-based retrospective cohort study.

OBJECTIVE: To use routine demographic and clinical data to develop an interpretable individual-level machine learning (ML) model to diagnose knee osteoarthritis (KOA) and to identify highly ranked features.

An explainable and supervised machine learning model for prediction of red blood cell transfusion in patients during hip fracture surgery.

AIM: The study aimed to develop a predictive model with machine learning (ML) algorithm, to predict and manage the need for red blood cell (RBC) transfusion during hip fracture surgery.

Enhancing knee osteoarthritis diagnosis with DMS: a novel dense multi-scale convolutional neural network approach.

BACKGROUND: Osteoarthritis (OA) of the knee is a prevalent chronic degenerative joint condition that is having a growing impact on a global scale., po...

Artificial intelligence in risk prediction and diagnosis of vertebral fractures.

With the increasing prevalence of vertebral fractures, accurate diagnosis and prognostication are essential. This study assesses the effectiveness of ...

Accuracy analysis of the new artificial anatomical marker positioning method (shoulder-to-shoulder) in preventing leg length discrepancy in total hip arthroplasty.

OBJECTIVE: By comparing the hip arthroplasty parameters planned with the AIHIP three-dimensional simulation surgery system, this study analyzes the ac...

Deep learning and radiomics-based vascular calcification characterization in dental cone beam computed tomography as a predictive tool for cardiovascular disease: a proof-of-concept study.

OBJECTIVES: This study evaluated an automated deep learning method for detecting calcifications in the extracranial and intracranial carotid arteries ...

Developing the new diagnostic model by integrating bioinformatics and machine learning for osteoarthritis.

BACKGROUND: Osteoarthritis (OA) is a common cause of disability among the elderly, profoundly affecting quality of life. This study aims to leverage b...

Deep learning algorithm enables automated Cobb angle measurements with high accuracy.

OBJECTIVE: To determine the accuracy of automatic Cobb angle measurements by deep learning (DL) on full spine radiographs.

Effect of the Ultrasound-Guided Interscalene and Supraclavicular Blocks on the C4 Dermatome.

PURPOSE: The C4 dermatome anesthesia holds significance for arthroscopic shoulder surgery. However, the reliability of achieving C4 dermatome anesthes...

Efficacy of preoperative prophylactic application of betamethasone on postoperative nausea and vomiting in patients undergoing total knee arthroplasty: a prospective randomized controlled trial.

BACKGROUND: The demand for total knee arthroplasty (TKA) is increasing, yet postoperative nausea and vomiting (PONV) significantly hinder patient reco...

Automated Bone Cancer Detection Using Deep Learning on X-Ray Images.

In recent days, bone cancer is a life-threatening health issue that can lead to death. However, physicians use CT-scan, X-rays, or MRI images to recog...

Diagnostic performance of neural network algorithms in skull fracture detection on CT scans: a systematic review and meta-analysis.

BACKGROUND AND AIM: The potential intricacy of skull fractures as well as the complexity of underlying anatomy poses diagnostic hurdles for radiologis...

Novel dilation-erosion labeling technique allows for rapid, accurate and adjustable alignment measurements in primary TKA.

BACKGROUND: Optimal implant position and alignment remains a controversial, yet critical topic in primary total knee arthroplasty (TKA). Future study ...

TransformerLSR: Attentive joint model of longitudinal data, survival, and recurrent events with concurrent latent structure.

In applications such as biomedical studies, epidemiology, and social sciences, recurrent events often co-occur with longitudinal measurements and a te...

Damage explains function in spiking neural networks representing central pattern generator.

Complex biological systems have evolved to control movement dynamics despite noisy and unpredictable inputs and processing delays that necessitate for...

Study on the application of deep learning artificial intelligence techniques in the diagnosis of nasal bone fracture.

PURPOSE: To evaluate the identification of nasal bone fractures and their clinical diagnostic significance for three-dimensional (3D) reconstruction o...

Attention and dilated convolutions inclusive deep-CNN with multiplexed texture features to diagnose Pathological and High Myopia.

In today's era, precise and timely diagnosis of ocular diseases are crucial as these disorders jeopardize millions of visions. Early detection and pro...

Multibody system dynamics for bio-robotic design and simulation based on inching-locomotion caterpillar's gait: MBD-ILAR method.

Inching-locomotion caterpillars (ILAR) inspire the design of 'inch-worm' robots with biomimicry features, that can be adapted to different environment...

Assessment of body composition and prediction of infectious pancreatic necrosis via non-contrast CT radiomics and deep learning.

AIM: The current study aims to delineate subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), the sacrospinalis muscle, and all abdominal...

Artificial intelligence in emergency neuroradiology: Current applications and perspectives.

Emergency neuroradiology provides rapid diagnostic decision-making and guidance for management for a wide range of acute conditions involving the brai...