Artificial Intelligence Medical Compendium
Explore the latest research on artificial intelligence and machine learning in medicine.
Showing 22,281 to 22,290 of 216,627 articles
Validation of remote multimodal AI screening for Parkinson disease across diverse settings.
Communications medicine
May 6, 2026
BACKGROUND: Timely detection of Parkinson's disease (PD) remains limited by reliance on in-person neurological evaluations that are often costly and geographically inaccessible. To address these barriers, we develop PARK (Parkinson's Analysis with Re... read more
Multimodal deep learning prediction of treatment response to anti-vascular endothelial growth factor in diabetic macular oedema.
Eye (London, England)
May 6, 2026
OBJECTIVE: To develop and validate a multimodal deep learning model that predicts treatment responses to intravitreal anti-vascular endothelial growth factor (anti-VEGF) injections in patients with diabetic macular oedema (DMO) by combining optical c... read more
Optimizing cervical cancer diagnosis with a hybrid deep neural network and progressive resizing on pap smear WSIs.
Scientific reports
May 6, 2026
Nowadays, computer-aided diagnostic (CAD) systems powered by artificial intelligence (AI) are becoming increasingly prevalent in cervical cancer diagnosis. Automatic selection of features by the deep convolutional neural networks (CNN) is a more prom... read more
Interpretable modeling to forecast CO2 solid phase boundaries in cryogenic methane rich systems.
Scientific reports
May 6, 2026
The unintended formation of solid carbon dioxide during the cryogenic processing of natural gas introduces severe operational hazards, pipeline blockages, and financial losses. To address this critical challenge, this study aims to develop a highly a... read more
EEG-based harmful brain activity classification using deep learning and feature fusion.
Scientific reports
May 6, 2026
The prevalence of research on harmful brain activity has increased, especially since the standardization of electroencephalography (EEG) terminologies. A continual lack of specialists leads to considerable distress and increased mortality rates among... read more
A novel hybrid NSGA-III and machine learning framework for modeling wheat yield variability using climatic, edaphic, and nutritional drivers.
Scientific reports
May 6, 2026
Accurate prediction of crop yield remains a critical research priority due to the increasing vulnerability of agricultural systems to climate change and the growing need for food security. In this study, we developed a hybrid modeling framework to pr... read more
Machine learning and response surface methodology for optimization and prediction of tribological performance of PLA/rice husk biochar composites.
Scientific reports
May 6, 2026
The present study investigates the tribological behaviour of polylactic acid (PLA) composites reinforced with rice husk biochar (RHBC) through an integrated approach combining experimentation, statistical optimization, and machine learning. PLA/RHBC ... read more
HashEye: a real-time on-drone high-resolution tiny object detection via spatial pruning.
Scientific reports
May 6, 2026
While recent deep learning-based object detection has achieved great success in various fields, it remains challenging to find tiny objects in aerial imagery on-the-fly using mobile devices. Since mobile platforms such as drones operate with limited ... read more
Machine learning-based geospatial assessment of forest structure characteristics and sequestration potential for informed carbon stocks inventories.
Scientific reports
May 6, 2026
Managed forest lands are key contributors to the carbon balance assessment needed for the greenhouse gas inventories on local, regional, national, and global levels. However, forest lands, due to size and complexity, are challenging for detailed spat... read more
Beyond NIHSS and neuroimaging: an interpretable gradient boosting model for predicting in-hospital mortality in ICU patients with acute ischemic stroke.
Scientific reports
May 6, 2026
This study aimed to develop an interpretable machine learning model for predicting in-hospital mortality among acute ischemic stroke (AIS) patients admitted to the intensive care unit (ICU) using only routine clinical variables, without requiring Nat... read more
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