AIMC Topic: Male

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Deep Learning and Radiomics Discrimination of Coronary Chronic Total Occlusion and Subtotal Occlusion using CTA.

Academic radiology
RATIONALE AND OBJECTIVES: Coronary chronic total occlusion (CTO) and subtotal occlusion (STO) pose diagnostic challenges, differing in treatment strategies. Artificial intelligence and radiomics are promising tools for accurate discrimination. This s...

A modular cage may prevent endplate damage and improve spinal deformity correction.

Clinical biomechanics (Bristol, Avon)
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...

A lightweight approach to gait abnormality detection for At Home health monitoring.

Computers in biology and medicine
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, ...

Natural language processing for identifying major bleeding risk in hospitalised medical patients.

Computers in biology and medicine
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...

Influence pathways of noise exposure on people's negative emotions and health across different activity contexts: A neural network-based double machine learning approach.

Health & place
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...

Multimodal contrastive learning for enhanced explainability in pediatric brain tumor molecular diagnosis.

Scientific reports
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 ...

Multimodal machine learning for predicting perioperative safety indicators in spinal surgery.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: Machine learning (ML) algorithms can utilize the large amount of tabular data in electronic health records (EHRs) to predict perioperative safety indicators. Integrating unstructured free-text inputs via natural language processin...

Artificial intelligence-quantified schisis volume as a structural endpoint for gene therapy clinical trials in X-linked retinoschisis.

Acta ophthalmologica
PURPOSE: To use artificial intelligence (AI) for quantifying schisis volume (ASV) in X-linked retinoschisis (XLRS) for use as a structural endpoint in gene therapy clinical trials.

Ultrasound-based deep learning to differentiate salivary gland tumors.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVE: Accurate preoperative diagnosis is essential for selecting appropriate surgical interventions. This study aims to develop a deep learning model based on ultrasound (US) imaging to accurately differentiate between benign and malignant saliv...