AI Medical Compendium

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

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Machine learning model for menstrual cycle phase classification and ovulation day detection based on sleeping heart rate under free-living conditions.

Computers in biology and medicine
The accurate classification of menstrual cycle phases and detection of ovulation is critical for women's health management, particularly in addressing infertility, alleviating premenstrual syndrome, and preventing hormone-related disorders. However, ...

Advancing newborn care: Precise time of birth detection using ai-driven thermal imaging with adaptive normalization.

Computers in biology and medicine
Around 5%-10% of newborns need assistance to start breathing. Currently, there is a lack of evidence-based research, objective data collection, and opportunities for learning from real newborn resuscitation emergency events. Generating and evaluating...

Deep learning image registration for cardiac motion estimation in adult and fetal echocardiography via a focus on anatomic plausibility and texture quality of warped image.

Computers in biology and medicine
Temporal echocardiography image registration is important for cardiac motion estimation, myocardial strain assessments, and stroke volume quantifications. Deep learning image registration (DLIR) is a promising way to achieve consistent and accurate r...

ExPDrug: Integration of an interpretable neural network and knowledge graph for pathway-based drug repurposing.

Computers in biology and medicine
Precision medicine aims to provide personalized therapies by analyzing patient molecular profiles, often focusing on gene expression data. However, effectively linking these data to actionable drug discovery for clinical application remains challengi...

Improved early detection accuracy for breast cancer using a deep learning framework in medical imaging.

Computers in biology and medicine
PROBLEM: The most prevalent cancer in women is breast cancer (BC), and effective treatment depends on being detected early. Many people seek medical imaging techniques to help in the early detection of problems, but results often need to be corrected...

On QSPR analysis of glaucoma drugs using machine learning with XGBoost and regression models.

Computers in biology and medicine
Glaucoma is an irreversible, progressive, degenerative eye disorder arising because of increased intraocular pressure, resulting in eventual vision loss if untreated. The QSPR relates, mathematically, by employing various algorithms, a specified prop...

A multi-scale information fusion medical image segmentation network based on convolutional kernel coupled updata mechanism.

Computers in biology and medicine
Medical image segmentation is pivotal in disease diagnosis and treatment. This paper presents a novel network architecture for medical image segmentation, termed TransDLNet, which is engineered to enhance the efficiency of multi-scale information uti...

Optimizing warfarin dosing in diabetic patients through BERT model and machine learning techniques.

Computers in biology and medicine
This study highlights the importance of evaluating warfarin dosing in diabetic patients, who require careful anticoagulation management. With rising rates of diabetes and cardiovascular diseases, understanding the factors influencing warfarin therapy...

Detecting IDH and TERTp mutations in diffuse gliomas using H-MRS with attention deep-shallow networks.

Computers in biology and medicine
BACKGROUND: Preoperative and noninvasive detection of isocitrate dehydrogenase (IDH) and telomerase reverse transcriptase gene promoter (TERTp) mutations in glioma is critical for prognosis and treatment planning. This study aims to develop deep lear...

Predicting the effectiveness of chemotherapy treatment in lung cancer utilizing artificial intelligence-supported serum N-glycome analysis.

Computers in biology and medicine
An efficient novel approach is introduced to predict the effectiveness of chemotherapy treatment in lung cancer by monitoring the serum N-glycome of patients combined with artificial intelligence-based data analysis. The study involved thirty-three l...