Orthopedics

Enhanced breast cancer diagnosis through integration of computer vision with fusion based joint transfer learning using multi modality medical images.

Breast cancer (BC) is a type of cancer which progresses and spreads from breast tissues and gradually exceeds the entire body; this kind of cancer originates in both sexes. Prompt recognition of this disorder is most significant in this phase, and it is measured by providing patients with the essential treatment so their efficient lifetime can be protected. Scientists and researchers in numerous s...

Augmenting a spine CT scans dataset using VAEs, GANs, and transfer learning for improved detection of vertebral compression fractures.

In recent years, deep learning has become a popular tool to analyze and classify medical images. However, challenges such as limited data availability, high labeling costs, and privacy concerns remain significant obstacles. As such, generative models have been extensively explored as a solution to generate new images and overcome the stated challenges. In this paper, we augment a dataset of chest ...

Application of Machine Learning to Osteoporosis and Osteopenia Screening Using Hand Radiographs.

PURPOSE: Fragility fractures associated with osteoporosis and osteopenia are a common cause of morbidity and mortality. Current methods of diagnosing ...

ResNet-Transformer deep learning model-aided detection of dens evaginatus.

BACKGROUND: Dens evaginatus is a dental morphological developmental anomaly. Failing to detect it may lead to tubercles fracture and pulpal/periapical...

Improved Surface Electromyogram-Based Hand-Wrist Force Estimation Using Deep Neural Networks and Cross-Joint Transfer Learning.

Deep neural networks (DNNs) and transfer learning (TL) have been used to improve surface electromyogram (sEMG)-based force estimation. However, prior ...

Examining the Role of Large Language Models in Orthopedics: Systematic Review.

BACKGROUND: Large language models (LLMs) can understand natural language and generate corresponding text, images, and even videos based on prompts, wh...

Artificial intelligence in fracture detection on radiographs: a literature review.

Fractures are one of the most common reasons of admission to emergency department affecting individuals of all ages and regions worldwide that can be ...

Enhancing systematic review efficiency in hand surgery using artificial intelligence (natural language processing) for abstract screening.

The aim of the present study was to train a natural language processing model to recognize key text elements from research abstracts related to hand s...

MIU-Net: Advanced multi-scale feature extraction and imbalance mitigation for optic disc segmentation.

Pathological myopia is a severe eye condition that can cause serious complications like retinal detachment and macular degeneration, posing a threat t...

Automatic AI tool for opportunistic screening of vertebral compression fractures on chest frontal radiographs: A multicenter study.

Vertebral compression fractures (VCFs) are the most common type of osteoporotic fractures, yet they are often clinically silent and undiagnosed. Chest...

A Deep Learning-Based Approach to Characterize Skull Physical Properties: A Phantom Study.

Transcranial ultrasound imaging is a popular method to study cerebral functionality and diagnose brain injuries. However, the detected ultrasound sign...

Cultivating diagnostic clarity: The importance of reporting artificial intelligence confidence levels in radiologic diagnoses.

Accurate image interpretation is essential in the field of radiology to the healthcare team in order to provide optimal patient care. This article dis...

Task-agnostic exoskeleton control via biological joint moment estimation.

Lower-limb exoskeletons have the potential to transform the way we move, but current state-of-the-art controllers cannot accommodate the rich set of p...

Elevating Patient Care With Deep Learning: High-Resolution Cervical Auscultation Signals for Swallowing Kinematic Analysis in Nasogastric Tube Patients.

Patients with nasogastric (NG) tubes require careful monitoring due to the potential impact of the tube on their ability to swallow safely. This study...

Development and Validation of a Machine Learning-Based Nomogram for Prediction of Unplanned Reoperation Postspinal Surgery Within 30 Days.

BACKGROUND: Unplanned reoperation postspinal surgery (URPS) leads to prolonged hospital stays, higher costs, decreased patient satisfaction, and adver...

Prediction of Bone Mineral Density based on Computer Tomography Images Using Deep Learning Model.

INTRODUCTION: The problem of population aging is intensifying worldwide. Osteoporosis has become an important cause affecting the health status of old...

Height prediction of individuals with osteogenesis imperfecta by machine learning.

BACKGROUND: Osteogenesis imperfecta (OI) is a genetic disorder characterized by low bone mass, bone fragility and short stature. There is a significan...

Machine Learning Approaches for the Fusion of Near-Infrared, Mid-Infrared, and Raman Data to Identify Cartilage Degradation in Human Osteochondral Plugs.

Vibrational spectroscopy methods such as mid-infrared (MIR), near-infrared (NIR), and Raman spectroscopies have been shown to have great potential for...

Deep learning segmentation-based bone removal from computed tomography of the brain improves subdural hematoma detection.

PURPOSE: Timely identification of intracranial blood products is clinically impactful, however the detection of subdural hematoma (SDH) on non-contras...

Development and accuracy of an artificial intelligence model for predicting the progression of hip osteoarthritis using plain radiographs and clinical data: a retrospective study.

BACKGROUND: Predicting the progression of hip osteoarthritis (OA) remains challenging, and no reliable predictive method has been established. This st...