Domestic Violence

Deep learning-based identification of patients at increased risk of cancer using routine laboratory markers.

Early screening for cancer has proven to improve the survival rate and spare patients from intensive and costly treatments due to late diagnosis. Cancer screening in the healthy population involves an initial risk stratification step to determine the screening method and frequency, primarily to optimize resource allocation by targeting screening towards individuals who draw most benefit. For most ...

Utilizing machine learning and geographic analysis to improve Post-crash traffic injury management and emergency response systems.

Traffic injuries are a major public health concern globally. This study uses machine learning (ML) and geographic analysis to analyse road traffic fatalities and improve traffic safety in Nakhon Ratchasima Province, Thailand. Data on road traffic fatalities were collected from forensic and hospital records. K-means clustering grouped death locations and identified cluster centres. The Ball Tree al...

Domestic violence and childhood trauma among married women using machine learning approach: a cross-sectional study.

BACKGROUND: Globally, 27% of ever-partnered women aged 15-49 have experienced physical, sexual, or intimate partner violence at least once in their li...

Aligning, Autoencoding and Prompting Large Language Models for Novel Disease Reporting.

Given radiology images, automatic radiology report generation aims to produce informative text that reports diseases. It can benefit current clinical ...

Artificial Intelligence in Dentistry: A Narrative Review of Diagnostic and Therapeutic Applications.

Advancements in digital and precision medicine have fostered the rapid development of artificial intelligence (AI) applications, including machine lea...

Improving the usability of large emergency 911 data reporting systems: A machine learning case study using emergency incident descriptions.

INTRODUCTION: Emergency 9-1-1 incident data are recorded voluntarily within fire-department-specific computer-aided dispatch systems. The National Fir...

Transformer-based deep learning ensemble framework predicts autism spectrum disorder using health administrative and birth registry data.

Early diagnosis and access to resources, support and therapy are critical for improving long-term outcomes for children with autism spectrum disorder ...

Virtual lung screening trial (VLST): An in silico study inspired by the national lung screening trial for lung cancer detection.

Clinical imaging trials play a crucial role in advancing medical innovation but are often costly, inefficient, and ethically constrained. Virtual Imag...

Parametric-MAA: fast, object-centric avoidance of metal artifacts for intraoperative CBCT.

PURPOSE: Metal artifacts remain a persistent issue in intraoperative CBCT imaging. Particularly in orthopedic and trauma applications, these artifacts...

Machine learning of clinical phenotypes facilitates autism screening and identifies novel subgroups with distinct transcriptomic profiles.

Autism spectrum disorder (ASD) presents significant challenges in diagnosis and intervention due to its diverse clinical manifestations and underlying...

Generating evidence to support the role of AI in diabetic eye screening: considerations from the UK National Screening Committee.

Screening for diabetic retinopathy has been shown to reduce the risk of sight loss in people with diabetes, because of early detection and treatment o...

GCN-BBB: Deep Learning Blood-Brain Barrier (BBB) Permeability PharmacoAnalytics with Graph Convolutional Neural (GCN) Network.

The Blood-Brain Barrier (BBB) is a selective barrier between the Central Nervous System (CNS) and the peripheral system, regulating the distribution o...

Can Artificial Intelligence Language Models Effectively Address Dental Trauma Questions?

BACKGROUND/AIM: Artificial intelligence (AI) chatbots, also known as large language models (LLMs), have become increasingly common educational tools i...

An Early Thyroid Screening Model Based on Transformer and Secondary Transfer Learning for Chest and Thyroid CT Images.

IntroductionThyroid cancer is a common malignant tumor, and early diagnosis and timely treatment are crucial to improve patient prognosis. With the in...

Evaluating multimodal ChatGPT for emergency decision-making of ocular trauma cases.

PURPOSE: This study aimed to evaluate the potential of ChatGPT in diagnosing ocular trauma cases in emergency settings and determining the necessity f...

Physiological Sensor Modality Sensitivity Test for Pain Intensity Classification in Quantitative Sensory Testing.

Chronic pain is prevalent and disproportionately impacts adults with a lower quality of life. Although subjective self-reporting is the "gold standard...

Development of an external quality assurance (EQA) structure to evaluate the quality of genetic pathology reporting.

A standard for reporting genetic pathology results currently does not exist as a consensus. While effective reports are produced, there is lack of con...

Predictive power of artificial intelligence for malignant cerebral edema in stroke patients: a CT-based systematic review and meta-analysis of prevalence and diagnostic performance.

Malignant cerebral edema (MCE) is a severe complication of acute ischemic stroke, with high mortality rates. Early and accurate prediction of MCE is c...

Deep Learning-Based Detection of Aflatoxin B1 Contamination in Almonds Using Hyperspectral Imaging: A Focus on Optimized 3D Inception-ResNet Model.

Aflatoxin B1, a toxic carcinogen frequently contaminating almonds, nuts, and food products, poses significant health risks. Therefore, a rapid and non...

Optimizing deep learning models for glaucoma screening with vision transformers for resource efficiency and the pie augmentation method.

Glaucoma is the leading cause of irreversible vision impairment, emphasizing the critical need for early detection. Typically, AI-based glaucoma scree...