Primary Care

Precision Imaging Evaluation and Clinical Application Progress of Vascular Calcification in Lower Extremity Peripheral Artery Disease.

Lower extremity peripheral artery disease (PAD) affects a large and growing population worldwide and imposes a substantial clinical burden, particularly among older adults and patients with diabetes, chronic kidney disease, and other cardiovascular risk factors.Vascular calcification is a common and clinically important feature of PAD. It can impair diagnostic accuracy, reduce vascular compliance,...

Predicting health and disease: a conceptual framework for AI in preventive and precision medicine.

The conventional medical approach of treating symptoms as they appear with restricted screening often limits intervention to slowing disease progression rather than fully reversing it. A new approach leveraging artificial intelligence (AI) and computational technologies across expanding multimodal biomedical datasets holds the promise to enable predicting actionable future changes in health before...

Dual branch fundus deep learning network as an enhanced multi classification system for ocular disease detection via hybrid feature fusion.

Ophthalmic diagnosis relies heavily on the interpretation of fundus images to identify a range of debilitating diseases. However, the presence of mult...

Machine learning for risk stratification of hypertensive disorders of pregnancy: Enhancing clinical efficiency in low-resource antenatal care in Tanzania.

Maternal mortality in Tanzania remains a public health crisis, with Hypertensive Disorders of Pregnancy (HDP) causing 34% of direct obstetric deaths. ...

Imaging-Derived Sarcopenic Obesity and Cardiovascular Outcomes: Insights Into Heart Failure Risk and Muscle Biology.

BACKGROUND: The prognostic value of obesity in cardiovascular disease is complex. Measures such as body mass index and waist-to-height ratio show diff...

Reframing Cardiometabolic Risk and Frailty Through Sarcopenic Obesity.

Development of a predictive model for preeclampsia utilizing time-of-flight mass spectrometry based on novel nanomaterials integrated with machine learning.

BACKGROUND: Preeclampsia (PE) is a serious complication of pregnancy, causing irreversible damage to multiple systems and organs of both mother and ba...

Identification of ischemic stroke risk in patients with left ventricular non-compaction using echocardiography, deep learning, and radiomics.

BACKGROUND: There is a certain correlation between left ventricular non-compaction (LVNC) and ischemic stroke (IS); however, there are currently no pr...

Identification of exosomal miRNA-based predictive signatures for gestational diabetes mellitus via multi-algorithm machine learning.

BACKGROUND: Gestational diabetes mellitus (GDM) is a common metabolic disorder during pregnancy, leading to adverse maternal and neonatal outcomes. Ex...

Opportunistic screening for incident cardiometabolic disease in metabolically healthy non-obese individuals: a prospective cohort study.

BACKGROUND: Metabolically healthy non-obese (MHN) individuals are considered at low cardiometabolic risk, yet a subset may harbor unfavorable visceral...

Machine learning-based risk assessment of neonatal perinatal adverse outcomes of anemia during pregnancy: a modeling study.

BACKGROUND: Gestational anemia significantly elevates the risk of adverse maternal and neonatal outcomes, necessitating early predictive tools for tar...

Predicting unplanned 30-day hospital readmissions in patients with urogenital system disorders: a retrospective EMR-based ensemble learning study in Taiwan.

BACKGROUND: Hospital readmissions are a major burden for patients, families, and healthcare systems. Artificial intelligence (AI) and electronic medic...

Non-invasive classification of stable HFpEF using a deep learning model trained on acoustic features of sustained vowels.

BACKGROUND: Conventional identification of stable HFpEF still depends on resource-intensive clinical assessment, whereas non-invasive acoustic analysi...

Explicit Inclusion of Diabetes Mellitus Without Retinopathy Within Diabetic Retinopathy Prediction.

PURPOSE: The purpose of this study was to evaluate whether explicitly modeling diabetes mellitus (DM) without diabetic retinopathy (DR) as its own sta...

Developing a machine learning model to predict renal function decline within three years using one-year longitudinal data in patients with type 2 diabetes.

AIMS: The increasing prevalence of type 2 diabetes mellitus (T2DM) has led to an increase in diabetic kidney disease (DKD), which is presently a major...

From Cardioprotection to Trial Design: Rethinking Cardiac Safety in Oncology.

PURPOSE OF REVIEW: Cardio-oncology has emerged as a pivotal discipline aimed at preserving cardiovascular health in patients undergoing contemporary c...

Post-stroke rehabilitation in inflammatory rheumatic diseases: outcome measures, digital tools, and patient education perspectives.

Inflammatory rheumatic diseases (IRDs) represent a significant risk factor for cerebrovascular events, independent of traditional cardiovascular risk ...

Screening and Evaluation of Post-stroke Dysphagia: Insights from Neurology, Artificial Intelligence and Data Science-A Scoping Review.

Post-stroke dysphagia (PSD) affects approximately 42% of acute stroke patients, increasing hospitalization costs and length of stay. Early identificat...

The Development Path of Gynecological Cytology after Papanicolaou: from Bethesda to Artificial Intelligence and Personalized Medicine.

BACKGROUND: Cervical cancer is the fourth most common cancer whose incidence was significantly reduced with the help of Pap smear and organized cervic...

Exploring attitudes and acceptance of artificial intelligence in multiple sclerosis from the patient perspective.

Artificial intelligence (AI) is increasingly being integrated into healthcare, particularly in data-intensive chronic diseases that rely on longitudin...