Latest AI and machine learning research in orthopedics for healthcare professionals.
OBJECTIVES: Temporomandibular disorder (TMD) patients experience a variety of clinical symptoms, and MRI is the most effective tool for diagnosing temporomandibular joint (TMJ) disc displacement. This study aimed to develop a transformer-based deep learning model to generate T2-weighted (T2w) images from proton density-weighted (PDw) images, reducing MRI scan time for TMD patients.
Six-degree-of-freedom robotic testing is used to gain insight into knee function by measuring the biomechanics of cadaveric knees. However, it can be challenging to use cadaveric knees to validate robotic testing methodologies and to compare methodologies across laboratories because cadavers have variable properties and require lengthy preparation. Therefore, our primary objective was to develop a...
In the paper we presented the review of mathematical and numerical models of osteoarthritis (OA). As angiogenesis seems to be the most principal facto...
Accurate adult age estimation (AAE) is critical for forensic and anthropological applications, yet traditional methods relying on bone mineral density...
State-of-the-art research of traditional computer vision is increasingly leveraged in the surgical domain. A particular focus in computer-assisted sur...
OBJECTIVES: Optimize deep learning-based vertebrae segmentation in longitudinal CT scans of multiple myeloma patients using structural uncertainty ana...
Considering the high incidence of osteoarthritis (OA), especially of the knee and hip, this study explores the possible genetic associations between O...
Left-turn slip lanes, also known as channelised right-turn lanes in right-hand driving countries, are widely implemented to facilitate left-turning at...
Osteoarthritis is a degenerative condition that affects bones and cartilage, often leading to structural changes, including osteophyte formation, bone...
BACKGROUND: Hip fractures are associated with reduced mobility, and higher morbidity, mortality, and healthcare costs. Approximately 90% of hip fractu...
BACKGROUND: Obtaining total knee arthroplasty patient-reported outcomes for quality assessment is costly and difficult. We asked whether a large langu...
BACKGROUND: It is becoming increasingly important to evaluate the effectiveness of large language models (LLMs) and query-assisted platforms like Goog...
Accurate interpretation of multi-view radiographs is crucial for diagnosing fractures, muscular injuries, and other anomalies. While significant adv...
Current sparse autoencoder (SAE) approaches to neural network interpretability assume that activations can be decomposed through linear superpositio...
Consumer-grade RGB-D imaging for intraoperative orthopedic tissue tracking is a promising method with high translational potential. Unlike bone-moun...
Multimodal embedding models, built upon causal Vision Language Models (VLMs), have shown promise in various tasks. However, current approaches face ...
Automated and accurate segmentation of individual vertebra in 3D CT and MRI images is essential for various clinical applications. Due to the limita...
Current multimodal medical image fusion typically assumes that source images are of high quality and perfectly aligned at the pixel level. Its effec...
White matter hyperintensities (WMH) are radiological markers of small vessel disease and neurodegeneration, whose accurate segmentation and spatial ...
RGB-IR(RGB-Infrared) image pairs are frequently applied simultaneously in various applications like intelligent surveillance. However, as the number...