+ HEALTH Aims Intramuscular fat (IMF) is increasingly recognized as a marker of ectopic adiposity and adverse cardiome…+ HEALTH Periapical lesions are challenging to detect on intraoral radiographs because of anatomical superimposition a…± HEALTH Ultrasound imaging is an indispensable diagnostic tool, yet its profound reliance on operator expertise inher…+ HEALTH Objectives We adapted the individualized polysocial risk score (iPsRS), a machine learning model originally d…± HEALTH Melioidosis is a life-threatening infectious disease caused by Burkholderia pseudomallei ( Bp ). Rapid diagno…+ HEALTH Classification is a core machine learning task, and ensemble voting methods are widely used to improve predic…+ CLIMATE Microplastic (MP) pollution poses escalating environmental risks, demanding efficient and reproducible tools…± HEALTH Medicinal chemistry sits at the center of modern drug discovery, yet translating molecular designs into appro…
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339 stories · page 12 of 23

Evidence-backed gain

Interpretable Machine Learning for Population-Level Tooth Loss Prediction

Machine learning can support population-level severe tooth loss (STL; ≥6 missing teeth) risk stratification; however, a lack of calibration under domain shift, limited interpretability of conventional black-box models, and inadequate handling of complex survey designs constrain responsible public health interpretation and implementation. We implemented and evaluated an interpretable, survey-weighted Multiple Imputation by Chained Equations-Explainable Boosting Machine (MICE-EBM) framework for population-level ST…

Journal of Dental Research · Health

Interpretable Machine Learning for Population-Level Tooth Loss Prediction
Automated classification of dental implant brands and prosthetic platform sizes on panoramic radiographs using deep learning
Evidence-backed gain

Automated classification of dental implant brands and prosthetic platform sizes on panoramic radiographs using deep learning

Statement of problem Incomplete clinical records can be a significant hurdle in implant dentistry, transforming routine maintenance or a restorative task into a complex search. When the primary documentation-such as the implant passport or surgical report-is missing, the clinician is forced to rely on radiographic identification and trial-and-error, which increases the risk of component mismatch and patient dissatisfaction. Purpose The purpose of this study was to develop and validate an artificial intelligence…

Health
Identification of Methionine Metabolism-Driven Heterogeneous Subtypes in Colorectal Cancer and Their Associated Immune Microenvironment
Evidence-backed gain

Identification of Methionine Metabolism-Driven Heterogeneous Subtypes in Colorectal Cancer and Their Associated Immune Microenvironment

Aim Tumor heterogeneity, driven by metabolic reprogramming, challenges colorectal cancer (CRC) treatment. Methionine metabolism is crucial for tumor progression, but its role in CRC heterogeneity and the tumor immune microenvironment (TIME) requires systematic investigation. Methods A systematic evaluation of 101 combinations of machine learning and statistical algorithms was conducted within a 10-fold cross-validation framework to develop and validate the optimal model, termed the methionine metabolism-related…

Health
Deep learning models for predicting clinical activity and characteristics in thyroid eye disease using magnetic resonance imaging
Evidence-backed gain

Deep learning models for predicting clinical activity and characteristics in thyroid eye disease using magnetic resonance imaging

Purpose This project aims to develop and evaluate deep learning models using orbital magnetic resonance imaging for the prediction of continuous clinical activity score and key patient characteristics in thyroid eye disease. Methods The publicly available TOM500 dataset, consisting of orbital magnetic resonance imaging scans and clinical data from 500 thyroid eye disease patients, was split into training ( n = 360), validation ( n = 100), and test ( n = 40) sets. A ResNet-50 convolutional neural network pretrain…

Health
Machine learning approaches to predict early cardiac immune-related adverse events in patients receiving immune checkpoint inhibitors
Evidence-backed gain

Machine learning approaches to predict early cardiac immune-related adverse events in patients receiving immune checkpoint inhibitors

Purpose Immune checkpoint inhibitor (ICI)-induced cardiac immune-related adverse events (cardiac irAEs) are rare yet serious complications. Clinical assessment tools to identify at-risk patients would allow for more effective prevention strategies, thus improving clinical outcomes. We constructed various machine learning (ML) models to predict these events among patients receiving ICI therapy. Methods A cohort of patients receiving ICI therapy from 2010 to 2023 was identified from the TriNetX database. Cardiac i…

Health
Metabolites stratify future major depressive disorder risk in obese population: a longitudinal machine learning analysis
Evidence-backed gain

Metabolites stratify future major depressive disorder risk in obese population: a longitudinal machine learning analysis

Background Obesity is a well-established risk factor for major depressive disorder (MDD), yet the risk is not uniform, highlighting the need for precise risk stratification. This study aimed to develop a metabolomics-based prediction model to identify high-risk metabolic phenotypes among obese participants and to elucidate the causal metabolic pathways involved. Methods Forty-one-thousand-four-hundred-fifty-nine obese participants were followed for a median of 14.4 years. We integrated multiple machine learning…

Health
Clinical justification for osteoporosis investigation: transitioning from opportunistic to diagnostic referrals from alternative forms of imaging
Evidence-backed gain

Clinical justification for osteoporosis investigation: transitioning from opportunistic to diagnostic referrals from alternative forms of imaging

Clinical justification remains fundamental to the safe use of imaging involving ionising radiation, requiring a favourable balance between diagnostic benefit and stochastic risk. Concurrently, advances in imaging technology and artificial intelligence have enabled opportunistic identification of additional pathologies beyond the primary indication for imaging. This opinion article discusses how emerging opportunistic osteoporosis detection technologies may eventually transition into clinically justified diagnost…

Health

An explainable machine learning approach for cervical cancer screening: decoding morphological diagnostic drivers

Background Cervical cancer screening in primary care is hindered by expert pathologist shortages and heavy diagnostic workloads, leading to fatigue-induced misdiagnoses. This study evaluated the diagnostic capacity, subpopulation robustness, and operational efficiency of an interpretable machine learning (ML) tool within a large Chinese healthcare network. Methods A retrospective database of 5,000 women was audited. Archived liquid-based cytology (LBC) digital slides were evaluated via a parallel validation chan…

Health
An explainable machine learning approach for cervical cancer screening: decoding morphological diagnostic drivers

Artificial intelligence and machine learning in pharmaceutical research and healthcare: Ethical challenges and a framework for responsible implementation

Background Artificial intelligence (AI) and machine learning (ML) are transforming pharmaceutical research and healthcare by enabling analysis of large-scale biomedical data and supporting data-driven decision-making. However, their rapid integration has introduced significant ethical, governance, and implementation challenges that remain insufficiently synthesized within a unified framework. Objective This work aims to synthesize the central ethical challenges and paradoxes associated with AI and ML in pharmace…

Health
Artificial intelligence and machine learning in pharmaceutical research and healthcare: Ethical challenges and a framework for responsible implementation

Research and application of machine learning models based on multimodal big data for precise transfusion management in acute myeloid leukaemia

Acute myeloid leukaemia (AML) is a highly heterogeneous haematologic malignancy in which transfusion support represents an essential component of comprehensive patient care. This review aims to provide an updated synthesis of recent progress in the development and clinical application of machine learning models based on multimodal big data for precision transfusion management in AML, addressing the persistent limitations of conventional, empirically guided transfusion practices. We systematically reviewed the li…

Health
Research and application of machine learning models based on multimodal big data for precise transfusion management in acute myeloid leukaemia

Beyond Thresholds: Can Machine Learning Improve Trauma Field Triage?

BackgroundAccurate triage of trauma patients by Emergency Medical Services (EMS) is essential for optimal outcomes and resource allocation. The 2021 National Field Triage Guidelines (FTG) assist EMS in prehospital triage; however, its collective performance has never been evaluated using a national database. We aimed to evaluate an FTG surrogate and develop a predictive model to identify patients at risk for serious injury.MethodsThe Trauma Quality Improvement Program National Trauma Databank (2017-2020) was que…

Health
Beyond Thresholds: Can Machine Learning Improve Trauma Field Triage?

AI-based clinician decision support system for diagnosis of inherited retinal diseases: a multicenter, randomized trial

The accurate and timely diagnosis of inherited retinal diseases (IRDs) represents an unmet clinical need in ophthalmology, as the current pathways rely on resource-intensive phenotyping, multidisciplinary expertise and genetic testing. Here we developed Retina4IRD, an artificial intelligence (AI)-based clinician decision support system (CDSS) that predicts 17 genotype categories from retina images. Retina4IRD uses a Vision Transformer model pretrained with RETFound. We then trained and validated Retina4IRD using…

Health
AI-based clinician decision support system for diagnosis of inherited retinal diseases: a multicenter, randomized trial

Machine Learning Prediction of Hoehn and Yahr Scores at 5-Years Post-<sup>123</sup>I-Ioflupane SPECT Imaging in a Real-World Parkinson's Disease Dataset

Background Parkinson's disease (PD) progression is highly heterogeneous, complicating clinical management and prognostication. While machine learning models have been developed using research datasets such as Parkinson's Precision Medicine Initiative (PPMI) and Parkinson's Disease Biomarkers Program (PDBP), their clinical translatability is limited due to differences in routinely collected data. The Hoehn and Yahr (H&Y) scale is commonly used in clinical practice to stage PD, yet most predictive models focus on…

Health
Machine Learning Prediction of Hoehn and Yahr Scores at 5-Years Post-<sup>123</sup>I-Ioflupane SPECT Imaging in a Real-World Parkinson's Disease Dataset

Early Identification of Recovery Potential After Acute Brain Injury Using Functional Near-Infrared Spectroscopy

Background Accurate early prognostication in patients with acute brain injury remains a major challenge in neurocritical care. Conventional bedside assessments provide limited insight into long-term outcomes and may not fully capture preserved brain function that supports recovery. Functional neuroimaging can detect brain activity not evident at the bedside, but its use in intensive care remains constrained by cost, logistics, and the need for stronger evidence supporting its value. Functional near-infrared spec…

Health
Early Identification of Recovery Potential After Acute Brain Injury Using Functional Near-Infrared Spectroscopy

Treatment-Effect-Based Versus Risk-Based Targeting of Care Management Outreach in Medicaid: A Retrospective Cohort Study with Machine Learning

Medicaid care-management programs typically allocate scarce outreach capacity to beneficiaries with the highest predicted risk of an acute event, assuming that risk and responsiveness are aligned and stable across short intervals. The authors tested whether targeting outreach by predicted individualized treatment effect-the conditional average treatment effect (CATE) recomputed each calendar month-outperforms risk-based targeting. The authors analyzed 164,063 adult Medicaid beneficiaries (2,670,806 person-months…

Health
Treatment-Effect-Based Versus Risk-Based Targeting of Care Management Outreach in Medicaid: A Retrospective Cohort Study with Machine Learning