+ HEALTH Background Discharge letters are essential for continuity of care, yet patients often misunderstand their con…± HEALTH Hematoxylin and Eosin (H&E) glass slide preparation is fundamental to histopathology. Pathologists' expertise…+ CLIMATE Traditional soil heavy metal risk assessments suffer from fragmented indices and poor logical integration. Co…± HEALTH Background Neonatal pneumothorax can progress rapidly, yet detection on supine chest radiographs remains chal…+ HEALTH T cell exhaustion has widespread implications for the progression and treatment of chronic diseases including…+ HEALTH Background Standard pulmonary arterial hypertension (PAH) risk models may not fully apply to PAH associated w…+ HEALTH Objectives To map and synthesize the scientific evidence regarding the use of Augmented Reality (AR) and Mixe…± HEALTH Objective To examine whether the outputs of machine learning algorithms designed to predict risk of cardiovas…
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454 stories · page 18 of 31

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Exploratory machine learning-based early post-treatment assessment of willingness to reuse rubber dam isolation after microscopic root canal treatment

ObjectiveTo explore factors associated with willingness to reuse rubber dam isolation after microscopic root canal treatment and develop an exploratory machine learning-based early post-treatment assessment model.MethodsThis retrospective cross-sectional study included 306 patients who underwent microscopic root canal treatment with rubber dam isolation from May 2025 to November 2025. The outcome was the willingness to reuse rubber dam isolation at the 1-week follow-up. Forty-seven newly enrolled patients were r…

Journal of International Medical Research · Health

Exploratory machine learning-based early post-treatment assessment of willingness to reuse rubber dam isolation after microscopic root canal treatment
From machine learning to deep learning in attention deficit hyperactivity disorder diagnosis: A bibliometric analysis of global trends (2011-2024)
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From machine learning to deep learning in attention deficit hyperactivity disorder diagnosis: A bibliometric analysis of global trends (2011-2024)

Background The diagnosis of attention deficit hyperactivity disorder (ADHD) has traditionally relied on subjective clinical interviews. Recent years have witnessed a paradigm shift toward objective, data-driven diagnostics powered by artificial intelligence (AI). Objective This study provides a comprehensive bibliometric review of AI and machine learning (ML) applications in ADHD prediction to map the field's evolution, current trends, and future directions. Methods A structured search of the Scopus database ret…

Health
Large Language Model-Based Localization of Premature Ventricular Contraction Origins: A Retrospective Diagnostic Accuracy Study
Evidence-backed gain

Large Language Model-Based Localization of Premature Ventricular Contraction Origins: A Retrospective Diagnostic Accuracy Study

Background Accurate localization of premature ventricular contraction (PVC) origin from 12-lead electrocardiography (ECG) is important for procedural planning in catheter ablation. Although convolutional neural network (CNN)-based models have shown promising diagnostic performance, they require task-specific training and remain limited in interpretability. We evaluated whether large language model (LLM)-based ECG image interpretation could perform binary left-versus-right PVC origin localization from 12-lead ECG…

Health
Prediction of coronary atherosclerosis progression in type 2 diabetes mellitus based on AI-derived CCTA parameters and clinical factors: a follow-up study
Evidence-backed gain

Prediction of coronary atherosclerosis progression in type 2 diabetes mellitus based on AI-derived CCTA parameters and clinical factors: a follow-up study

Objective To develop a predictive model for coronary atherosclerosis progression in patients with type 2 diabetes mellitus (T2DM) based on Artificial intelligence(AI)-derived coronary computed tomography angiography (CCTA) parameters combined with clinical indicators. Methods This retrospective study enrolled 114 patients with T2DM and non-obstructive coronary artery disease (1%-49% stenosis) who underwent CCTA at our hospital between September 2019 and September 2024. After follow-up of 1-5 years, patients were…

Health
Performance of federated learning models in health services research: A systematic review and meta-analysis
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Performance of federated learning models in health services research: A systematic review and meta-analysis

Purpose This study aimed to assess the performance of federated learning (FL) models and compare their performance with local and centralized models. Methods We conducted a systematic search of Ovid MEDLINE and PubMed from inception to June 10, 2025, to identify studies using patient data to train or validate FL algorithms and reporting at least one model performance outcome. Two reviewers independently screened articles and extracted data on study characteristics, FL frameworks and model training methodologies,…

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Artificial intelligence-based photographic detection of pink esthetic score attributes using a hybrid deep learning segmentation pipeline: a method development study
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Artificial intelligence-based photographic detection of pink esthetic score attributes using a hybrid deep learning segmentation pipeline: a method development study

This study aimed to develop and internally validate a novel anatomy-driven artificial intelligence (AI) system for automated postoperative Pink Esthetic Score (PES) evaluation from intraoral photographs. Unlike most existing AI approaches, which rely on end-to-end prediction, the proposed pipeline derives PES attributes from anatomically grounded measurements. A hybrid analytical pipeline integrating instance segmentation and rule-based measurement was developed. Tooth crown segmentation was performed using Mask…

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Integrating network toxicology, machine learning, and single-cell sequencing systems to analyze autophagy core genes in lung adenocarcinoma
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Integrating network toxicology, machine learning, and single-cell sequencing systems to analyze autophagy core genes in lung adenocarcinoma

The heterogeneity and complex tumor microenvironment of lung adenocarcinoma lead to poor prognosis. Autophagy, as a key cellular process, interacts with tumor immune infiltration and jointly affects the progression of lung adenocarcinoma, but its core regulatory genes and mechanisms are still unclear.This study integrated three lung adenocarcinoma transcriptome datasets from the GEO database and performed cross-analysis with the human autophagy gene set to screen for differentially expressed autophagy-related ge…

Health

Local Recurrence Prediction After Carbon-ion Radiotherapy for Early-stage Non-small Cell Lung Cancer Using Machine Learning

Background/aim Predicting local recurrence remains challenging in carbon-ion radiotherapy (CIRT) for non-small cell lung cancer (NSCLC). In this study, we aimed to develop and validate a machine learning model to predict local recurrence after CIRT for early-stage peripheral NSCLC. Patients and methods We retrospectively analyzed patients treated with CIRT at our institution between 2010 and 2020. An Extreme Gradient Boosting classifier using clinical parameters was developed to predict local recurrence within 2…

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Local Recurrence Prediction After Carbon-ion Radiotherapy for Early-stage Non-small Cell Lung Cancer Using Machine Learning

Screening efficiency over experience: Rapid target detection in low-power field as a modifiable cognitive biomarker for diagnostic accuracy in digital cytology

Background Traditionally, cytology expertise has been equated with professional experience. However, the transition to whole-slide imaging and artificial intelligence (AI) necessitates a shift from exhaustive screening to rapid verification. The goal of this study was to identify cognitive biomarkers associated with diagnostic accuracy and evaluate their modifiability. Methods In phase 1, 100 cytotechnologists with 1-40 years of experience diagnosed 30 digital cytology images using eye-tracking. Gaze metrics acr…

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Screening efficiency over experience: Rapid target detection in low-power field as a modifiable cognitive biomarker for diagnostic accuracy in digital cytology

The Sydney Triage to Admission Risk Tool With Artificial Intelligence (START-AI) to Support Decision Making in Emergency Departments: Model Explainability and Feature Importance Analysis

Objective Evaluate the importance of specific variables contributing to a recently reported Artificial Intelligence (AI) prediction model called Sydney Triage to Admission Risk Tool with Artificial Intelligence (START-AI) to predict inpatient admission from the Emergency Department (ED). Methods A model explainability analysis was undertaken using single-centre ED electronic medical record data over 2 years. The START-AI model, which comprises ensemble machine learning and a transformer-based algorithm to enhanc…

Health
The Sydney Triage to Admission Risk Tool With Artificial Intelligence (START-AI) to Support Decision Making in Emergency Departments: Model Explainability and Feature Importance Analysis

[Artificial intelligence in hypertension: where do we stand?]

Artificial intelligence (AI) is entering the study of hypertension, serving both primarily clinical purposes - to assist practicing physicians - and research objectives. Hypertension presents certain specific characteristics that should be carefully considered when using AI. The measured value of blood pressure is an extremely variable parameter that is difficult to standardize and measure with precision. At present, AI is able to provide very simple, clear, and well-documented answers to clinical questions rega…

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[Artificial intelligence in hypertension: where do we stand?]

Harnessing Exhaled Breath for Lung Cancer Early Detection-Results From the ExPeL Study

Scalable, non-invasive tools are critically needed to improve early lung cancer detection and optimize primary care referral pathways. We evaluated Inflammacheck, a point-of-care device utilizing exhaled breath condensate (EBC) H 2 O 2 and physiological parameters with machine learning for non-invasive lung cancer detection in a real-world screening population. Exhaled Hydrogen Peroxide for Early Lung Cancer Detection (ExPeL) study participants, from the UK Targeted Lung Health Check (TLHC) programme, included i…

Health
Harnessing Exhaled Breath for Lung Cancer Early Detection-Results From the ExPeL Study

Multiomics Profiling Identifies Blood-Based Diagnostic Markers for Sepsis

Sepsis, characterized by a rapid transition to systemic immune dysregulation and multiorgan failure, poses a formidable clinical challenge. The lack of spatiotemporally stable biomarkers severely impedes early diagnosis and risk stratification. By integrating large-scale transcriptomic profiling with machine learning algorithms, this study identified a robust three-gene diagnostic signature (TLR5, HMGB2, and C19orf59). Single-cell RNA sequencing precisely localized the sepsis-induced specific upregulation of the…

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Multiomics Profiling Identifies Blood-Based Diagnostic Markers for Sepsis

Large Language Models for Individualized Psychoeducational Tools for Psychosis: A Cross-Sectional Study

Objective This study aimed to evaluate the quality of GPT-4-generated responses to commonly asked psychosis-related psychoeducational questions from patients, caregivers and relatives in a first-episode psychosis programme. Evaluation focused on accuracy, clarity, inclusivity, completeness, clinical utility and overall quality. Design This cross-sectional study employed a qualitative evaluation design. GPT-4, accessed via the ChatGPT interface, generated responses to 20 psychosis-related psychoeducational questi…

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Large Language Models for Individualized Psychoeducational Tools for Psychosis: A Cross-Sectional Study

Advancing Radiology Education with Artificial Intelligence: Curriculum Planning, Implementation, and Evaluation

Artificial intelligence (AI) is rapidly integrating into clinical radiology, creating a continuous emphasis on the necessity of teaching its principles to radiology trainees. However, the potential of AI to transform radiology education remains underexplored. The authors review how AI can be leveraged to enhance radiology education, from curriculum planning to its implementation and evaluation. Guided by Harden's 10-step framework for curriculum development, they systematically examine current and potential futu…

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Advancing Radiology Education with Artificial Intelligence: Curriculum Planning, Implementation, and Evaluation