Artificial Intelligence Medical Compendium

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

Showing 70,411 to 70,420 of 234,131 articles

Development of a predictive classification model for determining the category of fatal mine accidents using text mining techniques.

International journal of occupational safety and ergonomics : JOSE
Objectives. The mining sector is a hazardous work environment with high accident rates, causing human suffering and financial losses. Examining incident reports is crucial for preventing comparable events; however, manual classification of large acci... read more 

Deep learning for accurate tumour volume measurement and prediction of therapy response in paediatric osteosarcoma.

European radiology
OBJECTIVES: To assess treatment response in osteosarcoma, two automated convolutional neural networks (CNNs) were developed to quantify tumour volumes and predict response to induction chemotherapy using histopathology as the reference standard. MATE... read more 

Microfluidics-based label-free SERS profiling of exosomes with machine learning for osteosarcoma diagnosis.

Talanta
Osteosarcoma (OS) calls for early diagnosis to significantly improve patient survival rates. Exosomes hold significant potential as noninvasive biomarkers for the early diagnosis of cancer. Here, we design a microfluidic device to purify and analyze ... read more 

Portable and intelligent ratio fluorometry and colorimetry for dual-mode detection of dopamine based on B, N-codoped carbon dots and machine learning.

Talanta
A dual-mode approach was developed for dopamine (DA) assay based on boron (B) and nitrogen (N) co-doped carbon dots (B, N-CDs). This platform enabled highly sensitive and specific detection of DA in biological samples through collaborative ratio fluo... read more 

Enhancing breast cancer detection on screening mammogram using self-supervised learning and a hybrid deep model of Swin Transformer and convolutional neural networks.

Journal of medical imaging (Bellingham, Wash.)
PURPOSE: The scarcity of high-quality curated labeled medical training data remains one of the major limitations in applying artificial intelligence systems to breast cancer diagnosis. Deep models for mammogram analysis and mass (or micro-calcificati... read more 

Breast tumor diagnosis via multimodal deep learning using ultrasound B-mode and Nakagami images.

Journal of medical imaging (Bellingham, Wash.)
PURPOSE: We propose and evaluate multimodal deep learning (DL) approaches that combine ultrasound (US) B-mode and Nakagami parametric images for breast tumor classification. It is hypothesized that integrating tissue brightness information from B-mod... read more 

Assessing the User Experience of an LLM-Based Conversational Assistant in Diabetes Mellitus Care.

Journal of healthcare informatics research
UNLABELLED: This article presents the design, implementation, and evaluation of MarIA, a GPT-3.5-powered virtual assistant integrated into a messaging platform to support patients with type 2 diabetes mellitus (DM). MarIA employs a multi-agent archit... read more 

Multiomic insights into the MPO-mediated NET formation pathway in alcohol-induced epilepsy risk.

Genes & diseases
Epilepsy is a highly prevalent chronic central nervous system disorder that imposes substantial societal and economic burdens. Inconsistent associations of alcohol consumption, identified as a major global health risk factor, with epilepsy risk have ... read more 

Design and Validation of an Embedded Baseline Compensation Model with Wireless Sensor Network for Monitoring Total Volatile Organic Compounds.

ACS sensors
Portable and intelligent sensor systems are required for real-time air quality monitoring, and among them, the metal-oxide-semiconductor gas sensors are widely used in many scenarios. However, the inconsistency, baseline drifts, and complex off-board... read more 

Comparison of glass-based and whole-slide image-based grade assignment for clear-cell and papillary renal cell carcinoma: a multi-cohort, international reproducibility study.

Histopathology
BACKGROUND AND AIMS: Histological grading of renal cell carcinoma (RCC) is an important part of diagnostic evaluation. Reproducibility of RCC grading using whole-slide imaging (WSI) compared to glass-slide microscopy is understudied. The aim of the s... read more