Geriatrics

EGID: A Comprehensive Multi-label Endoscopic Image Dataset for Gastritis Classification.

Chronic atrophic gastritis is a well-recognized precancerous condition, emphasizing the need for accurate endoscopic classification. However, existing endoscopic datasets lack comprehensive multi-dimensional annotations for systematic gastritis classification. We present the Endoscopic Gastritis Image Dataset (EGID), comprising 5,883 high-quality white-light endoscopic images from 229 patients ret...

Interpretable deep learning for rotator cuff calcific tendinopathy diagnosis: a multi-center study.

The reliable deployment of artificial intelligence systems in medical imaging requires high diagnostic performance, robustness and interpretability. In this study, we developed and evaluated two automated frameworks for binary classification of shoulder radiographs (XRs) using deep learning (DL) and hybrid DL-machine learning (ML) approaches. A convolutional neural network (CNN) based on a fine-tu...

Predicting piRNA-Disease Associations Based on Dual-View Learning and Multi-head Self-Attention Mechanism Fusion.

PIWI-interacting RNAs (piRNAs) are an important class of non-coding RNA molecules in epigenetic regulation. It plays a crucial role in maintaining gen...

Machine learning-guided risk stratification in elderly AML based on genomic, immunophenotypic and therapeutic profiles.

BACKGROUND: Elderly patients with acute myeloid leukemia (AML) exhibit considerable biological and clinical heterogeneity, hindering precise prognosis...

Anthropometrics, physical fitness, and sport-specific performance of young German canoe sprint athletes (U13-U17) to predict senior performance level: a machine-learning approach.

The aim of this study was to evaluate whether machine learning models comprising anthropometric, physical fitness, and sport-specific performance data...

GreenAid: a confidence-weighted ensemble deep learning system for real-time plant disease detection and management.

Plant diseases cause 20-40% annual crop losses worldwide, yet conventional detection methods remain slow, subjective, and inaccessible to smallholder ...

Clinical pathways matter for multimodal deep learning in early Alzheimer's disease detection.

Identifying individuals at risk of Alzheimer's disease (AD), particularly in the preclinical and early stages, remains challenging. Although deep lear...

Contribution of resting pulse rate to fall risk prediction in patients with glaucoma: a nationwide retrospective study based on an XGBoost model.

BACKGROUND: Falls are among the most common safety concerns in people with visual impairment and can lead to serious consequences, including fractures...

Early thermal pulses drive interfacial pre-aging and plastisphere pre-shaping: An overlooked pre-conditioning pathway in PLA microplastic aging during composting.

Polylactic acid (PLA), promoted as a "compostable" alternative to petroleum-based plastics, often persists as microplastics (MPs) in compost that re-e...

Increasing the Reliability of Functional Connectivity by Predicting Long-Scan Functional Connectivity based on Short-Scan Functional Connectivity: Model Exploration, Explanation, Validation, and Application.

Functional connectivity (FC) is a widely used metric in functional magnetic resonance imaging (fMRI) research. However, its reliability has long been ...

Predicting Age-Related Facial Hyperpigmentation via Dermatologist Knowledge Elicitation and Generative Modeling.

INTRODUCTION: Facial hyperpigmentation is a primary marker of skin aging in Asian populations. While many artificial intelligence (AI)-based aging sim...

Development of an anemia detection model in emergency departments using lip region images based on medical knowledge and deep learning technology.

Anemia's high global prevalence and socio-economic burden necessitate early diagnosis, yet reliance on invasive blood testing creates significant barr...

Explainable epigenetic aging clocks: an overview of existing AI models and approaches.

INTRODUCTION: There is a growing demand to make epigenetic aging clocks that are interpretable and actionable. Here, we identify and describe explaina...

RDoC-informed explainable AI as a paradigm for multilevel Alzheimer's disease diagnosis and progression prediction: a systematic review.

Explainable Artificial Intelligence (XAI) is gaining popularity in early diagnosis and monitoring of dementia. Herein, we recommend the incorporation ...

Full-spectrum EEM end-member unmixing with statistical validation: A deep learning framework for organic pollution source apportionment in complex watersheds.

Accurately apportioning organic pollution sources in mixed land-use watersheds remains challenging due to the limited tracer capacity of conventional ...

AutoRV: Automated Assessment of Right Ventricular Structure and Function Using Transesophageal Echocardiography in Mechanically Ventilated Patients.

OBJECTIVE: Right ventricle (RV) dysfunction has therapeutic implications for the management of mechanically ventilated patients in intensive care unit...

Epigenetic entropy, socioeconomic differences, and health and lifespan in the Women's Health Initiative.

BACKGROUND: Aging is marked by changes in DNA methylation (DNAm) including an increase in randomness or entropy in the epigenome. Here, we computed ep...

Explainable AI for MRI Alzheimer's disease classification: A comparative analysis.

We introduce a quantitative pipeline for region-level explanations of an Alzheimer's prediction model using four post hoc explainable AI (XAI) methods...

Large-scale transcriptomic data mining using explainable XGBoost and SHAP reveals shared biomarkers and molecular mechanisms between type-2 diabetes and triple-negative breast cancer for drug repurposing.

Type 2 diabetes (T2D) is considered as a risk factor of triple-negative breast cancer (TNBC). So, there is a significant chance of their co-existence....

AI-derived bone mineral density from standard radiographs compared with DXA for fracture prediction in a 10-year real-world cohort study.

UNLABELLED: AI-derived bone mineral density from routine radiographs showed strong agreement with DXA and comparable ability to predict incident fract...