Orthopedics

The value of deep learning-based X-ray techniques in detecting and classifying K-L grades of knee osteoarthritis: a systematic review and meta-analysis.

OBJECTIVES: Knee osteoarthritis (KOA), a prevalent degenerative joint disease, is primarily diagnosed through X-ray imaging. The Kellgren-Lawrence grading system (K-L) is the gold standard for evaluating KOA severity through X-ray analysis. However, this method is highly subjective and non-quantifiable, limiting its effectiveness in detecting subtle joint changes on X-rays. Recent researchers have...

A Computed Tomography-Based Fracture Prediction Model With Images of Vertebral Bones and Muscles by Employing Deep Learning: Development and Validation Study.

BACKGROUND: With the progressive increase in aging populations, the use of opportunistic computed tomography (CT) scanning is increasing, which could be a valuable method for acquiring information on both muscles and bones of aging populations.

Fair AI-powered orthopedic image segmentation: addressing bias and promoting equitable healthcare.

AI-powered segmentation of hip and knee bony anatomy has revolutionized orthopedics, transforming pre-operative planning and post-operative assessment...

Explainable Deep-Learning-Based Gait Analysis of Hip-Knee Cyclogram for the Prediction of Adolescent Idiopathic Scoliosis Progression.

Accurate prediction of scoliotic curve progression is crucial for guiding treatment decisions in adolescent idiopathic scoliosis (AIS). Traditional me...

Deep learning classification performance for diagnosing condylar osteoarthritis in patients with dentofacial deformities using panoramic temporomandibular joint projection images.

OBJECTIVE: The present study aimed to assess the consistencies and performances of deep learning (DL) models in the diagnosis of condylar osteoarthrit...

Explainable deep learning and biomechanical modeling for TMJ disorder morphological risk factors.

Clarifying multifactorial musculoskeletal disorder etiologies supports risk analysis, development of targeted prevention, and treatment modalities. De...

Predicting Secondary Vertebral Compression Fracture After Vertebral Augmentation via CT-Based Machine Learning Radiomics-Clinical Model.

RATIONALE AND OBJECTIVES: Secondary vertebral compression fractures (SVCF) are very common in patients after vertebral augmentation (VA). The aim of t...

Artificial intelligence-enhanced opportunistic screening of osteoporosis in CT scan: a scoping Review.

PURPOSE: This scoping review aimed to assess the current research on artificial intelligence (AI)--enhanced opportunistic screening approaches for str...

Classification of osteoarthritic and healthy cartilage using deep learning with Raman spectra.

Raman spectroscopy is a rapid method for analysing the molecular composition of biological material. However, noise contamination in the spectral data...

Identification of core genes in intervertebral disc degeneration using bioinformatics and machine learning algorithms.

BACKGROUND: Intervertebral Disc Degeneration (IDD) is a major cause of lower back pain and a significant global health issue. However, the specific me...

Evaluation of fragility fracture risk using deep learning based on ultrasound radio frequency signal.

BACKGROUND: It was essential to identify individuals at high risk of fragility fracture and prevented them due to the significant morbidity, mortality...

Assessing the Accuracy of Artificial Intelligence Models in Scoliosis Classification and Suggested Therapeutic Approaches.

Open-source artificial intelligence models (OSAIMs) are increasingly being applied in various fields, including IT and medicine, offering promising s...

Multimodal Deep Learning-based Radiomics Approach for Predicting Surgical Outcomes in Patients with Cervical Ossification of the Posterior Longitudinal Ligament.

STUDY DESIGN: A retrospective analysis.

Integrating Radiomics and Neural Networks for Knee Osteoarthritis Incidence Prediction.

OBJECTIVE: Accurately predicting knee osteoarthritis (KOA) is essential for early detection and personalized treatment. We aimed to develop and test a...

Utilizing Deep Feature Fusion for Automatic Leukemia Classification: An Internet of Medical Things-Enabled Deep Learning Framework.

Acute lymphoblastic leukemia, commonly referred to as ALL, is a type of cancer that can affect both the blood and the bone marrow. The process of diag...

GREnet: Gradually REcurrent Network With Curriculum Learning for 2-D Medical Image Segmentation.

Medical image segmentation is a vital stage in medical image analysis. Numerous deep-learning methods are booming to improve the performance of 2-D me...

Reconstructing Cancellous Bone From Down-Sampled Optical-Resolution Photoacoustic Microscopy Images With Deep Learning.

OBJECTIVE: Bone diseases deteriorate the microstructure of bone tissue. Optical-resolution photoacoustic microscopy (OR-PAM) enables high spatial reso...

Analyzing speed-difference impact on freeway joint injury severities of Leading-Following vehicles using statistical and data-driven models.

Rear-end (RE) crashes are notably prevalent and pose a substantial risk on freeways. This paper explores the correlation between speed difference amon...

Artificial intelligence detects awareness of functional relation with the environment in 3 month old babies.

A recent experiment probed how purposeful action emerges in early life by manipulating infants' functional connection to an object in the environment ...

Characterizing mitochondrial features in osteoarthritis through integrative multi-omics and machine learning analysis.

PURPOSE: Osteoarthritis (OA) stands as the most prevalent joint disorder. Mitochondrial dysfunction has been linked to the pathogenesis of OA. The mai...