Latest AI and machine learning research in orthopedics for healthcare professionals.
Accurate and reliable selection of the appropriate acetabular cup size is crucial for restoring joint biomechanics in total hip arthroplasty (THA). This paper proposes a novel framework that integrates square-root velocity function (SRVF)-based elastic shape registration technique with an embedded deformation (ED) graph approach to reconstruct the 3D articular surface of the acetabulum by fusing...
Introduction: Bone health disorders like osteoarthritis and osteoporosis pose major global health challenges, often leading to delayed diagnoses due to limited diagnostic tools. This study presents an AI-powered system that analyzes knee X-rays to detect key pathologies, including joint space narrowing, sclerosis, osteophytes, tibial spikes, alignment issues, and soft tissue anomalies. It also g...
In this paper, we introduce a method for reconstructing 3D humans from a single image using a biomechanically accurate skeleton model. To achieve th...
Occlusion is one of the challenging issues when estimating 3D hand pose. This problem becomes more prominent when hand interacts with an object or t...
Quadrupedal robots can learn versatile locomotion skills but remain vulnerable when one or more joints lose power. In contrast, dogs and cats can ad...
This paper investigates distributed joint source-channel coding (JSCC) for correlated image semantic transmission over wireless channels. In this se...
Ermiao San (EMS), a traditional Chinese medicine composed of Atractylodes macrocephala and Cortex Phellodendron, has demonstrated therapeutic effica...
Multimodal generative models that can understand and generate across multiple modalities are dominated by autoregressive (AR) approaches, which proc...
Study Design: This study presents the development of an autonomous AI system for MRI spine pathology detection, trained on a dataset of 2 million MR...
Classifier-Free Guidance (CFG) is a fundamental technique in training conditional diffusion models. The common practice for CFG-based training is to...
Composed Image Retrieval (CIR) is a complex task that aims to retrieve images based on a multimodal query. Typical training data consists of triplet...
Image-event joint depth estimation methods leverage complementary modalities for robust perception, yet face challenges in generalizability stemming...
Purpose: To characterize the 3D structural phenotypes of the optic nerve head (ONH) in patients with glaucoma, high myopia, and concurrent high myop...
The study proposes an advanced machine learning approach to predict spine surgery outcomes by incorporating oversampling techniques and grid search ...
Over the last few decades, Artificial Intelligence (AI) scientists have been conducting investigations to attain human-level performance by a machin...
To develop and evaluate a new deep learning MR denoising method that leverages quantitative noise distribution information from the reconstruction p...
Understanding the geometric and semantic properties of the scene is crucial in autonomous navigation and particularly challenging in the case of Unm...
Osteolytic metastases located in the vertebrae reduce strength and enhance the risk of vertebral fractures. This risk can be predicted by means of v...
Computer vision has transformed medical diagnosis, treatment, and research through advanced image processing and machine learning techniques. Fractu...
The aim of this study was to devise a machine learning algorithm with superior performance in predicting bone metastasis (BM) in small cell lung cance...