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640 stories · page 19 of 43

[Artificial intelligence in cardiovascular prevention: a strategic opportunity for risk prediction and early diagnosis]
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[Artificial intelligence in cardiovascular prevention: a strategic opportunity for risk prediction and early diagnosis]

Cardiovascular diseases remain the leading cause of mortality and morbidity worldwide, with substantial impact in Italy. Cardiovascular prevention is a strategic priority, yet a significant gap persists between evidence-based guideline recommendations and their actual implementation in clinical practice. Artificial intelligence (AI), through machine learning and deep learning models, is emerging as a potentially transformative technology to bridge this gap, enabling more precise, dynamic, and personalized cardio…

Giornale Italiano di Cardiologia
Multisite Implementation of a Digital Wound Model of Care: A Post-Implementation Multimethods Evaluation of Patient and Clinician Perspectives and Lessons Learned
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Multisite Implementation of a Digital Wound Model of Care: A Post-Implementation Multimethods Evaluation of Patient and Clinician Perspectives and Lessons Learned

This study evaluated district-wide implementation of a digital wound model of care combining an artificial intelligence-enabled application with a virtual command centre across four hospitals and five community health centres in Australia. A post-implementation multimethods evaluation (January 2024-January 2026) of patients (n = 94), frontline clinicians (n = 75), and senior wound nurses (n = 9) and a product manager (n = 1) using surveys, semi-structured interviews and analysis of governance meeting minutes. Pa…

A machine learning-derived sarcopenia index is associated with survival and nonrelapse mortality in DLBCL
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A machine learning-derived sarcopenia index is associated with survival and nonrelapse mortality in DLBCL

Abstract Body composition analysis (BCA) provides an objective assessment of metabolic states, but its prognostic value in diffuse large B-cell lymphoma (DLBCL) remains unclear. We applied machine learning-supported BCA to computed tomography imaging from patients with newly diagnosed DLBCL enrolled in the prospective phase 3 PETAL trial to quantify radiologic sarcopenia. We assessed BCA results in relation to survival after first-line immunochemotherapy, treatment-related hematologic toxicities, and molecular d…

Explainable Plasma Proteomics-Based Machine Learning for Osteoporosis Diagnosis, Prognosis, and Protein Biomarker Discovery in the UK Biobank
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Explainable Plasma Proteomics-Based Machine Learning for Osteoporosis Diagnosis, Prognosis, and Protein Biomarker Discovery in the UK Biobank

Osteoporosis (OP) is often underdiagnosed, highlighting the need for tools that can both detect existing disease and predict future risk; large-scale plasma proteomics combined with explainable machine learning enables integrated diagnostic and prognostic modeling while prioritizing clinically relevant protein markers. This study aims to develop and validate an explainable plasma proteomics machine-learning framework for osteoporosis diagnosis, future risk prediction, and biomarker discovery. We further tested w…

Advancing hirschsprung disease diagnosis: a systematic review of the development and application of artificial intelligence in histopathological analysis
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Advancing hirschsprung disease diagnosis: a systematic review of the development and application of artificial intelligence in histopathological analysis

Hirschsprung's disease (HD) is characterised by absence of ganglion cells in the distal large intestine, requiring accurate histopathological diagnosis. Conventional diagnostic methods are time-consuming, subjective, and demand specialised expertise. While artificial intelligence (AI) shows promise for improving diagnostic capacity, its clinical utility requires rigorous evaluation. Following PRISMA 2020 guidelines, this systematic review evaluated machine and deep learning techniques for HD diagnosis from histo…

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Technology and Obesity: A Year in Review

SMART technological advancements help diagnose, treat, and monitor various diseases at the earliest stages. It presents an opportunity to maintain the key components of conventional obesity management programming while reducing costs and provider time inputs. Various machine learning models have helped predict the risks of obesity and metabolic syndrome. Additionally trained convolutional neural networks can now automatically segment and quantify different adipose tissue compartments. Various multicenter series…

Technology and Obesity: A Year in Review
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Harnessing machine learning, docking and molecular dynamics for the virtual screening of compounds as CDK4/6 dual inhibitors

Cyclin-dependent kinases 4 and 6 (CDK4/6) are pivotal regulators of the G1-to-S phase transition, and their dysregulation is a hallmark of numerous malignancies. Despite the clinical success of existing CDK4/6 inhibitors, there remains a persistent need for chemically diverse scaffolds with potent dual-target affinity. In this study, we developed and implemented a virtual screening workflow that synergistically integrates ligand-based machine learning with structure-based molecular docking. By benchmarking multi…

Harnessing machine learning, docking and molecular dynamics for the virtual screening of compounds as CDK4/6 dual inhibitors
Health

The Role of Artificial Intelligence Models in Predicting Post-Prosthetic Facial Esthetics in Edentulous Patients: Clinical and Anthropometric Comparative Study

Objectives Artificial intelligence (AI) is increasingly used in esthetic dentistry; however, its accuracy in predicting post-prosthetic facial outcomes in edentulous patients remains unclear. This study aimed to evaluate the ability of AI models to simulate post-denture facial esthetics compared with actual clinical outcomes. Materials and methods A prospective within-subject observational study was conducted on 14 completely edentulous patients receiving new complete dentures. Standardized facial photographs we…

The Role of Artificial Intelligence Models in Predicting Post-Prosthetic Facial Esthetics in Edentulous Patients: Clinical and Anthropometric Comparative Study
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Ai-based multimodal analysis of ECG and clinical data for evaluation for competitive sports participation: The VALETUDO trial

Aims Pre-participation cardiovascular screening (PPS) is essential for preventing SCD in athletes, yet ECG interpretation requires expertise and remains resource-intensive. We aimed to evaluate the feasibility and diagnostic performance of a deep learning (DL) model for analysis of clinical data and resting 12‑lead ECG obtained during routine PPS in competitive athletes. Methods In this prospective single center observational study, competitive athletes aged 18 to 60 years and undergoing routine PPS were enrolle…

Ai-based multimodal analysis of ECG and clinical data for evaluation for competitive sports participation: The VALETUDO trial
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Anticipating health and care trajectories from routinely collected social care records

Predictive modelling in healthcare has advanced rapidly, yet social care systems, despite their central role in supporting vulnerable populations, remain underexplored in this domain. In this study, we apply machine learning to a large, pseudonymised dataset of social care records from 27,590 adults in Oxfordshire, encompassing around 90% of individuals receiving care in the region. We developed models to predict three outcomes of interest: future care plan needs, hospital admissions, and all-cause mortality, ev…

Anticipating health and care trajectories from routinely collected social care records
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Development of a machine-learning risk stratification tool for vasoactive medication need after two-bolus fluid resuscitation in pediatric suspected sepsis

Background Timely vasopressor initiation is critical in fluid-refractory pediatric septic shock, yet clinicians lack objective tools to identify children requiring early hemodynamic escalation after fluid resuscitation. Methods We performed a retrospective multicenter study using electronic health record data from five pediatric emergency departments (March 2022-February 2025). Children aged 3 months-17 years screened for sepsis who received ≥2 fluid boluses and were vasopressor-naïve at the second bolus were in…

Development of a machine-learning risk stratification tool for vasoactive medication need after two-bolus fluid resuscitation in pediatric suspected sepsis
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Automated artificial intelligence performance for longitudinal pulmonary nodule matching in lung cancer screening

Accurate longitudinal nodule matching is a critical technical prerequisite for automated growth rate (volume doubling time) assessment in lung cancer screening. This study evaluated an artificial intelligence (AI) pulmonary nodule analysis system in all 361 UK Lung Cancer Screening (UKLS) trial participants who underwent a 3-month follow-up low-dose computed tomography (LDCT) scan. The pulmonary AI independently evaluated these baseline scans using an updated volume threshold (solid component ≥ 100 mm³ per NELSO…

Automated artificial intelligence performance for longitudinal pulmonary nodule matching in lung cancer screening