Primary Care

A psychologically interpretable artificial intelligence framework for the screening of loneliness, depression, and anxiety.

Negative emotions such as loneliness, depression, and anxiety (LDA) are prevalent and pose significant challenges to emotional well-being. Traditional methods of assessing LDA, reliant on questionnaires, often face limitations because of participants' inability or potential bias. This study introduces emoLDAnet, an artificial intelligence (AI)-driven psychological framework that leverages video-re...

A machine learning approach to automate microinfarct and microhemorrhage screening in hematoxylin and eosin-stained human brain tissues.

Microinfarcts and microhemorrhages are characteristic lesions of cerebrovascular disease. Although multiple studies have been published, there is no one universal standard criteria for the neuropathological assessment of cerebrovascular disease. In this study, we propose a novel application of machine learning in the automated screening of microinfarcts and microhemorrhages. Utilizing whole slide ...

Evaluating the Impact of Changes in Artificial Intelligence-derived Case Scores over Time on Digital Breast Tomosynthesis Screening Outcomes.

Purpose To evaluate the change in digital breast tomosynthesis artificial intelligence (DBT-AI) case scores over sequential screenings. Materials and ...

Artificial Intelligence-Based Early Prediction of Acute Respiratory Failure in the Emergency Department Using Biosignal and Clinical Data.

PURPOSE: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing bi...

Using AI to Select Women with Intermediate Breast Cancer Risk for Breast Screening with MRI.

Background Combined mammography and MRI screening is not universally accessible for women with intermediate breast cancer risk due to limited MRI reso...

Non-Linear Dose-Response Relationship for Metformin in Japanese Patients With Type 2 Diabetes: Analysis of Irregular Longitudinal Data by Interpretable Machine Learning Models.

The dose-response relationship between metformin and change in hemoglobin A1c (HbA1c) shows a maximum at 1500-2000 mg/day in patients with type 2 diab...

Integrating machine learning and human use experience to identify personalized pharmacotherapy in Traditional Chinese Medicine: a case study on resistant hypertension.

OBJECTIVE: To enhance the understanding of identifying personalized pharmacotherapy options in Traditional Chinese Medicine (TCM), and further support...

AI Applications for Thoracic Imaging: Considerations for Best Practice.

Artificial intelligence (AI) technology is rapidly being introduced into thoracic radiology practice. Current representative use cases for AI in thora...

A machine learning approach for Premature Coronary Artery Disease Diagnosis according to Different Ethnicities in Iran

Premature coronary artery disease (PCAD) refers to the early onset of the disease, usually before the age of 55 for men and 65 for women. Coronary A...

Analyzing Geospatial and Socioeconomic Disparities in Breast Cancer Screening Among Populations in the United States: Machine Learning Approach

Breast cancer screening plays a pivotal role in early detection and subsequent effective management of the disease, impacting patient outcomes and s...

Digital Health Innovations for Screening and Mitigating Mental Health Impacts of Adverse Childhood Experiences: Narrative Review

This study presents a narrative review of the use of digital health technologies (DHTs) and artificial intelligence to screen and mitigate risks and...

Evaluating Spoken Language as a Biomarker for Automated Screening of Cognitive Impairment

Timely and accurate assessment of cognitive impairment is a major unmet need in populations at risk. Alterations in speech and language can be early...

Towards Transparent and Accurate Diabetes Prediction Using Machine Learning and Explainable Artificial Intelligence

Diabetes mellitus (DM) is a global health issue of significance that must be diagnosed as early as possible and managed well. This study presents a ...

LLM Assistance for Pediatric Depression

Traditional depression screening methods, such as the PHQ-9, are particularly challenging for children in pediatric primary care due to practical li...

Integrating Probabilistic Trees and Causal Networks for Clinical and Epidemiological Data

Healthcare decision-making requires not only accurate predictions but also insights into how factors influence patient outcomes. While traditional M...

Context-CrackNet: A Context-Aware Framework for Precise Segmentation of Tiny Cracks in Pavement images

The accurate detection and segmentation of pavement distresses, particularly tiny and small cracks, are critical for early intervention and preventi...

Multimodal AI on Wound Images and Clinical Notes for Home Patient Referral

Chronic wounds affect 8.5 million Americans, particularly the elderly and patients with diabetes. These wounds can take up to nine months to heal, m...

Computational modelling of biological systems now and then: revisiting tools and visions from the beginning of the century

Since the turn of the millennium, computational modelling of biological systems has evolved remarkably and sees matured use spanning basic and clini...

A CNN-Transformer for Classification of Longitudinal 3D MRI Images -- A Case Study on Hepatocellular Carcinoma Prediction

Longitudinal MRI analysis is crucial for predicting disease outcomes, particularly in chronic conditions like hepatocellular carcinoma (HCC), where ...

Enhanced Large Language Models for Effective Screening of Depression and Anxiety

Depressive and anxiety disorders are widespread, necessitating timely identification and management. Recent advances in Large Language Models (LLMs)...