Clinical biomechanics (Bristol, Avon)
Mar 30, 2025
BACKGROUND: Anterior lumbar interbody fusion is performed to fuse pathological spinal segments, generally, with a monobloc cage inserted by impact forces. Recently developed three-part modular cages attempt to reduce the impact forces, minimize the d...
Gait abnormality detection is a growing application in machine learning based health assessment due to its potential in domains from clinical health reviews to at home health monitoring. This latter application is of particular use for older adults, ...
BACKGROUND: Major bleeding is a severe complication in critically ill medical patients, resulting in significant morbidity, mortality, and healthcare costs. This study aims to assess the incidence and risk factors for major bleeding in hospitalised m...
Noise is a major global environmental issue that raises concerns about both mental and physical health. However, few studies have investigated the mediating role of emotions in the pathways linking noise exposure to health outcomes. Additionally, man...
OBJECTIVES: Early detection of diabetic retinopathy (DR) and timely intervention are critical for preventing vision loss. Recently, deep learning techniques have shown promising results in streamlining this process. The objective of this study was to...
Annals of the New York Academy of Sciences
Mar 30, 2025
Deep reinforcement learning (RL) algorithms enable the development of fully autonomous agents that can interact with the environment. Brain-computer interface (BCI) systems decipher human implicit brain signals regardless of the explicit environment....
PURPOSE: To review studies reporting the role of Machine Learning (ML) techniques in the diagnosis of keratoconus (KC) over the past decade, shedding light on recent developments while also highlighting the existing gaps between academic research and...
In this study, we propose a novel approach for breast cancer classification that integrates the Seagull Optimization Algorithm (SGA) for feature selection with the Random Forest (RF) classifier for effective data classification. The novelty of our ap...
Despite the promising performance of convolutional neural networks (CNNs) in brain tumor diagnosis from magnetic resonance imaging (MRI), their integration into the clinical workflow has been limited. That is mainly due to the fact that the features ...
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