Sports Medicine

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 in vivo biomedical applications, such as arthroscopic evaluation of joint injuries and degeneration. Considering that these techniques provide complementary chemical information, in this study, we hypothesized that combining the MIR, NIR, and Raman ...

A fuzzy-logic approach for longitudinal assessment of patients' psychophysiological state: an application to upper-limb orthopedic robot-aided rehabilitation.

Understanding the psychophysiological state during robot-aided rehabilitation is crucial for assessing the patient experience during treatments. This paper introduces a psychophysiological estimation approach using a Fuzzy Logic inference model to assess patients' perception of robots during upper-limb robot-aided rehabilitation sessions. The patients were asked to perform nine cycles of 3D point-...

Custom Large Language Models Improve Accuracy: Comparing Retrieval Augmented Generation and Artificial Intelligence Agents to Noncustom Models for Evidence-Based Medicine.

PURPOSE: To show the value of custom methods, namely Retrieval Augmented Generation (RAG)-based Large Language Models (LLMs) and Agentic Augmentation,...

Phosphate-solubilizing fungus (PSF) - mediated phosphorous solubilization and validation through Artificial intelligence computation.

Phosphate-solubilizing fungus (PSF) strain alaromyces funiculosus was investigated for phosphorus solubilization, utilizing a range of pH levels and p...

AI derived ECG global longitudinal strain compared to echocardiographic measurements.

Left ventricular (LV) global longitudinal strain (LVGLS) is versatile; however, it is difficult to obtain. We evaluated the potential of an artificial...

Leather-Based Shoe Soles for Real-Time Gait Recognition and Automatic Remote Assistance Using Machine Learning.

Real-time monitoring of gait characteristics is crucial for applications in health monitoring, patient rehabilitation feedback, and telemedicine. Howe...

Comparison of deep learning approaches to estimate injury severity from the International Classification of Diseases codes.

OBJECTIVE: The injury severity classification based on the Abbreviated Injury Scale (AIS) provides information that allows for standardized comparison...

Unsupervised learning for real-time and continuous gait phase detection.

Individuals with lower limb impairment after a stroke or spinal cord injury require rehabilitation, but traditional methods can be challenging for bot...

Essential blood molecular signature for progression of sepsis-induced acute lung injury: Integrated bioinformatic, single-cell RNA Seq and machine learning analysis.

In this study, we aimed to identify an essential blood molecular signature for chacterizing the progression of sepsis-induced acute lung injury using ...

VR-Aided Ankle Rehabilitation Decision-Making Based on Convolutional Gated Recurrent Neural Network.

Traditional rehabilitation training for stroke patients with ankle joint issues typically relies on the expertise of physicians. However, when confron...

Brain Activation Pattern Caused by Soft Rehabilitation Glove and Virtual Reality Scenes: A Pilot fNIRS Study.

Clinical studies have proved significant improvements in hand motor function in stroke patients when assisted by robotic devices. However, there were ...

Effects of interval treadmill training on spatiotemporal parameters in children with cerebral palsy: A machine learning approach.

Quantifying individualized rehabilitation responses and optimizing therapy for each person is challenging. For interventions like treadmill training, ...

Implementing AI-Driven Bed Sensors: Perspectives from Interdisciplinary Teams in Geriatric Care.

Sleep is a crucial aspect of geriatric assessment for hospitalized older adults, and implementing AI-driven technology for sleep monitoring can signif...

Multimodal ultrasound deep learning to detect fibrosis in early chronic kidney disease.

We developed a multimodal ultrasound (US) deep learning (DL) fusion model to automatically classify early fibrosis in patients with chronic kidney dis...

Machine learning models of cerebral oxygenation (rcSO) for brain injury detection in neonates with hypoxic-ischaemic encephalopathy.

The present study was designed to test the potential utility of regional cerebral oxygen saturation (rcSO) in detecting term infants with brain injury...

Cost analysis of technological vs. conventional upper limb rehabilitation for patients with neurological disorders: an Italian real-world data case study.

INTRODUCTION: Most patients suffering from neurological disorders endure varying degrees of upper limb dysfunction, limiting their everyday activities...

Comparative Scoping Review: Robot-Assisted Upper Limb Stroke Rehabilitation in Low- and Middle-Income Countries Versus High-Income Nations.

OBJECTIVE: To examine robotic interventions for upper limb rehabilitation poststroke, focusing on geographic distribution, stroke chronicity, outcome ...

One-year employment outcome prediction after traumatic brain injury: A CENTER-TBI study.

BACKGROUND: Traumatic brain injury (TBI) can come with long term consequences for functional outcome that can complicate return to work.

Utilizing echocardiography and unsupervised machine learning for heart failure risk identification.

BACKGROUND: Global longitudinal strain (GLS) is recognized as a powerful predictor of heart failure (HF). However, the entire strain curve may entail ...

Effectiveness of robotic rehabilitation for gait and balance in people with multiple sclerosis: a systematic review.

This review investigated the effectiveness of robotic-assisted gait training (RAGT) in improving gait and balance performance in adults with multiple ...