Artificial Intelligence Medical Compendium

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

Showing 1,001 to 1,010 of 6,689 articles

Deep learning-based automated detection and multiclass classification of soil-transmitted helminths and Schistosoma mansoni eggs in fecal smear images.

Scientific reports
In this work, we developed an automated system for the detection and classification of soil-transmitted helminths (STH) and Schistosoma (S.) mansoni eggs in microscopic images of fecal smears. We assembled an STH and S. mansoni dataset comprising ove... read more 

Decoding the diagnostic biomarkers of mitochondrial dysfunction related gene variants in pediatric T cell acute lymphoblastic leukemia.

Scientific reports
Mitochondrial dysfunction is crucial in the pathogenesis and drug resistance of pediatric T-cell acute lymphoblastic leukemia (T-ALL), a malignant hematological disorder with unrestrained proliferation of immature T-cells. Therefore, the primary obje... read more 

AI-based CT assessment of 3117 vertebrae reveals significant sex-specific vertebral height differences.

Scientific reports
Predicting vertebral height is complex due to individual factors. AI-based medical imaging analysis offers new opportunities for vertebral assessment. Thereby, these novel methods may contribute to sex-adapted nomograms and vertebral height predictio... read more 

DSNet enables feature fusion and detail restoration for accurate object detection in foggy conditions.

Scientific reports
In real-world scenarios, adverse weather conditions can significantly degrade the performance of deep learning-based object detection models. Specifically, fog reduces visibility, complicating feature extraction and leading to detail loss, which impa... read more 

CFM-UNet: coupling local and global feature extraction networks for medical image segmentation.

Scientific reports
In medical image segmentation, traditional CNN-based models excel at extracting local features but have limitations in capturing global features. Conversely, Mamba, a novel network framework, effectively captures long-range feature dependencies and e... read more 

Comparison of AI chatbot predicted and realworld survival outcomes in hepatocellular carcinoma.

Scientific reports
This study compares survival predictions made by an artificial intelligence (AI) based chatbot with real-world data in hepatocellular carcinoma (HCC) patients. It aims to evaluate the reliability and accuracy of AI technologies in HCC prognosis. A re... read more 

A machine learning-based framework for predicting metabolic syndrome using serum liver function tests and high-sensitivity C-reactive protein.

Scientific reports
Metabolic Syndrome (MetS) comprises a clustering of conditions that significantly increase the risk of heart disease, stroke, and diabetes. Timely detection and intervention are crucial in preventing severe health outcomes. In this study, we implemen... read more 

Determination of the oral carcinoma and sarcoma in contrast enhanced CT images using deep convolutional neural networks.

Scientific reports
Oral cancer is a hazardous disease and a major cause of morbidity and mortality worldwide. The purpose of this study was to develop the deep convolutional neural networks (CNN)-based multiclass classification and object detection models for distingui... read more 

Factors influencing AI adoption by Chinese mathematics teachers in STEM education.

Scientific reports
This study refines the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) to explore the factors influencing the adoption and utilization of artificial intelligence (AI) by Chinese mathematics teachers in STEM education, aiming to p... read more 

A hybrid compound scaling hypergraph neural network for robust cervical cancer subtype classification using whole slide cytology images.

Scientific reports
Cervical cancer is a major cause of mortality among women, particularly in low-income countries with insufficient screening programs. Manual cytological examination is time-consuming, error-prone and subject to inter-observer variability. Automated a... read more