Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 37,261 to 37,270 of 223,469 articles

Deep learning-based non-contrast CT imaging markers enhance post-transfer DWI core volume prediction.

AJNR. American journal of neuroradiology Mar 31, 2026
BACKGROUND: Deep learning enables the extraction of ischemic lesion size and hypodensity imaging markers from noncontrast CT (DLNCCT) in patients with acute ischemic stroke, but it remains unclear whether those markers can predict post-transfer core ... read more 

Perspective: Depression in Persons with Parkinson's Disease.

Movement disorders : official journal of the Movement Disorder Society Mar 31, 2026
Depression is a prevalent and disabling syndrome characterized by sustained sadness and/or anhedonia, as well as cognitive and physical symptoms. In Parkinson's disease (PD), depression is both common and clinically challenging due to overlapping sym... read more 

Development and Validation of a Deep Learning-Based Facial Weakness Score for Objective Assessment in Facioscapulohumeral Muscular Dystrophy.

Muscle & nerve Mar 31, 2026
INTRODUCTION/AIMS: Facioscapulohumeral muscular dystrophy (FSHD) is a muscle disease that leads, among other manifestations, to facial weakness. This weakness can severely impact communication and quality of life, yet it remains under-researched with... read more 

Seeing and Feeling DNA Methylation: Single-Molecule Biophysics Meets Machine Learning.

Nano letters Mar 31, 2026
DNA methylation at 5-methylcytosine (5mC) is crucial for embryonic development and cellular function, while aberrant patterns strongly drive disease onset and progression. Its reversible nature offers substantial therapeutic potential, emphasizing th... read more 

Single-molecule neuromorphic device with aJ-level power consumption per switching.

Nature communications Mar 31, 2026
Artificial neural network-based machine learning provides foundations for artificial intelligence (AI), yet requires high energy costs for training. Beyond software-level simulation of neural networks, hardware-level implementation via neuromorphic d... read more 

Crashworthiness and technoeconomic assessment of bioinspired GFRP PP tubes using experiments numerical modeling and artificial neural networks.

Scientific reports Mar 31, 2026
This study presents a comprehensive experimental and computational investigation of the quasi-static axial compression behavior of glass fiber-reinforced polymer/ polypropylene (GFRP/PP) sandwich tubes inspired by bamboo and horsetail structures, des... read more 

A prospective multicenter trial of deep learning auto-segmentation for organs at risk in thoracic radiotherapy.

Nature communications Mar 31, 2026
Widespread clinical implementation of rapidly evolving auto-segmentation tools remains constrained by a scarcity of high-quality prospective evidence. Here we show the results of a prospective, multicenter, observational trial (NCT05787522) evaluatin... read more 

Cloud assisted blockchain-enabled split federated learning framework for security and privacy-preserving of IoMT in healthcare 5.0.

Scientific reports Mar 31, 2026
The rapid adoption of Internet of Medical Things (IoMT) in Federated Learning (FL)-enabled Smart Healthcare 5.0 raises pressing concerns regarding privacy, security, and real-time threat detection. FL remains vulnerable to training-phase attacks, par... read more 

Batch Distillation Data for Developing Machine Learning Anomaly Detection Methods.

Scientific data Mar 31, 2026
Machine learning (ML) holds great potential to advance anomaly detection (AD) in chemical processes. However, the development of ML-based methods is hindered by the lack of openly available experimental data. To address this gap, we have set up a lab... read more 

Calibrating deep classifiers with dynamic confidence propagation and adaptive normalization.

Scientific reports Mar 31, 2026
Although deep neural networks have made significant progress in confidence calibration, conventional methods like temperature scaling and histogram binning face notable limitations in dynamic open-world scenarios due to their reliance on global stati... read more 
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