Artificial Intelligence Medical Compendium

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

Showing 15,311 to 15,320 of 213,137 articles

Post-swallowing voice-based aspiration screening in dysphagia using a deep learning approach: insights from audio segmentation.

Scientific reports
Dysphagia presents a serious risk of aspiration that requires continuous monitoring. This study introduces standardized 2 s voice segments for aspiration detection, derived from physiological constraints observed during clinical video-fluoroscopic sw... read more 

Mitigating errors in satellite solar irradiation using a sequential empirical-ANN model for four cities across Central and Northern Pakistan.

Scientific reports
Accurate prediction of global horizontal irradiance (GHI) is essential for optimizing solar energy utilization. Satellite-based datasets often overestimate GHI, leading to errors that hinder reliable solar resource assessment. This study presents a s... read more 

Optimization of connectome weights for a neural network model generating both forward and backward locomotion in C. elegans.

Scientific reports
Previous studies tracking the relationship between manipulations of C. elegans neurons and the resulting behavioral changes have called for the development of a connectome-constrained neural network model that describes the cascade from neurons to be... read more 

Assessing and mitigating traffic crash risks using interpretable machine learning techniques.

Scientific reports
This study aims to examine factors associated with self-reported crash involvement among drivers in Pakistan using interpretable machine learning (ML) techniques and established driver-behavior instruments. The data used in this study were collected ... read more 

Energy-efficient, real-time detection of railway fastening systems from drone-based imagery using spiking neural networks.

Scientific reports
Rail fastener defects threaten track integrity and operational safety, making reliable automated inspection essential. This study develops an energy-efficient real-time railway fastener detection framework for UAV-based monitoring by integrating Spik... read more 

Probabilistic cancer risk assessment from heavy metal exposure in iranian rice and pasta: a novel hybrid framework integrating INAA, ICP-AES, and ensemble machine learning.

Scientific reports
This study investigates cancer risk from heavy metal exposure in rice and pasta using experimental data and machine learning approaches, based on 19 experimental samples and 1,750 simulated exposure instances. Concentrations of toxic heavy metals wer... read more 

Machine learning-enhanced modeling approach for optimally predicting household level food insecurity in Ethiopia during COVID-19.

Scientific reports
Food insecurity remains a critical global challenge, with low-income countries such as Ethiopia bearing a disproportionate burden. In settings where frequent data collection is limited, developing predictive models provides a cost-effective means of ... read more 

The AI4GH community of practice: strengthening LMIC-Led artificial intelligence for global health.

NPJ digital medicine
Efforts to research, implement and scale responsible Artificial Intelligence (AI) for addressing health challenges in low- and middle-income countries (LMICs) are often fragmented. It limits collaboration and slows progress. Communities of Practice (... read more 

An explainable machine learning framework for analyzing and predicting mental health problems among university students in Bangladesh.

Scientific reports
Mental health problems (MHPs) among university students are an increasing public health concern globally, including in Bangladesh. While machine learning (ML) methods can capture complex patterns in mental health data, their application to non-probab... read more 

BMA-YOLO: an object detection model for microscopic images of mouse embryos.

Scientific reports
In vitro embryo culture is a pivotal technology in life sciences and medical research. However, automated monitoring remains challenging due to factors such as bubble interference and the frequent omission of small or peripheral embryos. To overcome ... read more