Orthopedics

Developmental and Validation of Machine Learning Model for Prediction Complication After Cervical Spine Metastases Surgery.

STUDY DESIGN: This is a retrospective cohort study utilizing machine learning to predict postoperative complications in cervical spine metastases surgery.

Economical hybrid novelty detection leveraging global aleatoric semantic uncertainty for enhanced MRI-based ACL tear diagnosis.

This study presents an innovative hybrid deep learning (DL) framework that reformulates the sagittal MRI-based anterior cruciate ligament (ACL) tear classification task as a novelty detection problem to tackle class imbalance. We introduce a highly discriminative novelty score, which leverages the aleatoric semantic uncertainty as this is modeled in the class scores outputted by the YOLOv5-nano ob...

Towards higher load capacity: innovative design of a robotic hand with soft jointed structure.

In this paper, the innovative design of a robotic hand with soft jointed structure is carried out and a tendon-driven mechanism, a master-slave motor ...

Deep Learning-Enhanced Accelerated 2D TSE and 3D Superresolution Dixon TSE for Rapid Comprehensive Knee Joint Assessment.

OBJECTIVES: The aim of this study was to evaluate the use of a multicontrast deep learning (DL)-reconstructed 4-fold accelerated 2-dimensional (2D) tu...

Beyond pixel: Superpixel-based MRI segmentation through traditional machine learning and graph convolutional network.

BACKGROUND AND OBJECTIVE: Tendon segmentation is crucial for studying tendon-related pathologies like tendinopathy, tendinosis, etc. This step further...

Machine Learning Based Abnormal Gait Classification with IMU Considering Joint Impairment.

Gait analysis systems are critical for assessing motor function in rehabilitation and elderly care. This study aimed to develop and optimize an abnorm...

Development of an artificial intelligence model for predicting implant size in total knee arthroplasty using simple X-ray images.

BACKGROUND: Accurate estimation of implant size before surgery is crucial in preparing for total knee arthroplasty. However, this task is time-consumi...

The Fine-Tuned Large Language Model for Extracting the Progressive Bone Metastasis from Unstructured Radiology Reports.

Early detection of patients with impending bone metastasis is crucial for prognosis improvement. This study aimed to investigate the feasibility of a ...

An Examination of Temporomandibular Joint Disc Displacement through Magnetic Resonance Imaging by Integrating Artificial Intelligence: Preliminary Findings.

This research was aimed at constructing a complete automated temporomandibular joint disc position identification system that could assist with magne...

Optimizing number of Raman spectra using an artificial neural network guided Monte Carlo simulation approach to analyze human cortical bone.

This study presents a novel methodology for optimizing the number of Raman spectra required per sample for human bone compositional analysis. The meth...

Estimation of human age using machine learning on panoramic radiographs for Brazilian patients.

This paper addresses a relevant problem in Forensic Sciences by integrating radiological techniques with advanced machine learning methodologies to cr...

Diagnostic accuracy of artificial intelligence models in detecting osteoporosis using dental images: a systematic review and meta-analysis.

The current study aimed to systematically review the literature on the accuracy of artificial intelligence (AI) models for osteoporosis (OP) diagnosis...

Neural network auto-segmentation of serial-block-face scanning electron microscopy images exhibit collagen fibril structural differences with tendon type and health.

A U-Net machine learning algorithm was adapted to automatically segment tendon collagen fibril cross-sections from serial block face scanning electron...

A machine learning system for artificial ligaments with desired mechanical properties in ACL reconstruction applications.

The anterior cruciate ligament is one of the important tissues to maintain the stability of the human knee joint, but it is difficult for this ligamen...

Osteoinductive biomaterials: Machine learning for prediction and interpretation.

Biomaterials with osteoinductivity are widely used for bone defect repair due to their unique structures and functions. Machine learning (ML) is pivot...

Utility of Machine Learning, Natural Language Processing, and Artificial Intelligence in Predicting Hospital Readmissions After Orthopaedic Surgery: A Systematic Review and Meta-Analysis.

BACKGROUND: Numerous applications and strategies have been utilized to help assess the trends and patterns of readmissions after orthopaedic surgery i...

Artificial Intelligence Models Are Limited in Predicting Clinical Outcomes Following Hip Arthroscopy: A Systematic Review.

BACKGROUND: Hip arthroscopy has seen a significant surge in utilization, but complications remain, and optimal functional outcomes are not guaranteed....

Machine learning identifies clusters of the normal adolescent spine based on sagittal balance.

PURPOSE: This study applied a machine learning semi-supervised clustering approach to radiographs of adolescent sagittal spines from a single pediatri...

Enhancing diagnosis: ensemble deep-learning model for fracture detection using X-ray images.

AIM: Orthopedic trauma results in the injury of bone joints and tendons of the body. A radiologist reviews and monitors large numbers of radiographs d...

Diagnostic accuracy of chest X-ray and CT using artificial intelligence for osteoporosis: systematic review and meta-analysis.

INTRODUCTION: Artificial intelligence (AI)-based systems using chest images are potentially reliable for diagnosing osteoporosis.