Truace
Subscribe

Health

640 stories · page 16 of 43

An integrative clinical-molecular model as an auxiliary predictive tool for glioma malignancy grade
HealthBoth readings

An integrative clinical-molecular model as an auxiliary predictive tool for glioma malignancy grade

Objective An integrative auxiliary predictive model incorporating clinical parameters, serum biomarkers, and molecular pathological markers was developed to assess glioma malignancy grade. Methods This single-center retrospective observational study consecutively enrolled 400 glioma patients. A total of 26 variables, including demographic characteristics, clinical parameters, laboratory indicators, and serum biomarkers, were analyzed. Predictors were selected using univariate analysis, followed by Least Absolute…

Neurological Research
AI will help find cure for cancer ‘within our lifetimes’, says Arm Holdings chief
Evidence-backed gain

AI will help find cure for cancer ‘within our lifetimes’, says Arm Holdings chief

The boss of one of the UK’s biggest chip companies has claimed AI will be able to find a cure for cancer “in our lifetime”. Rene Haas, chief executive of the chip designer Arm Holdings, said that, while modelling how a DNA marker is affected by cancer was currently “too complex” a problem for either humans or technology, computers were “going to solve it” in the future. Haas told the BBC: “AI is going to … find a cure for cancer that today you and I, other humans [could] not in our lifetimes. I believe in our li…

Select large language models outperform hip preservation experts on consensus-based hip preservation questionnaire
Both readings

Select large language models outperform hip preservation experts on consensus-based hip preservation questionnaire

Artificial intelligence (AI) is increasingly utilized in medical education and clinical contexts, yet few studies compare the performance of large language models (LLMs) to subspecialized experts in providing guideline-based medical information on hip preservation. The purpose of this study was to evaluate the performance of three LLMs compared to a panel of international hip preservation experts in answering guideline-based questions related to femoroacetabular impingement syndrome, hip dysplasia and microinsta…

From Correlation to Clinical Translation: The Biological-Grounding×Translational-Readiness Framework for Artificial Intelligence in Non-Small-Cell Lung Cancer
Evidence-backed problem

From Correlation to Clinical Translation: The Biological-Grounding×Translational-Readiness Framework for Artificial Intelligence in Non-Small-Cell Lung Cancer

Non-small-cell lung cancer (NSCLC) remains the leading cause of cancer death worldwide, and clinicians now face a rapidly expanding array of artificial intelligence (AI) tools promising earlier detection, better treatment selection, and more precise radiotherapy, yet few have altered what happens at the bedside. The problem is not poor benchmark performance; it is that strong benchmark performance has repeatedly failed to translate into demonstrable patient benefit, because most published NSCLC models are retros…

NDIB-Sim: A Multimodal Bidirectional PINN Model for Simulating Brain Dynamics
Evidence-backed gain

NDIB-Sim: A Multimodal Bidirectional PINN Model for Simulating Brain Dynamics

Constructing dynamic virtual brain models is essential for understanding brain functions and pathological mechanisms, crucial in computational neuroscience. Current modeling methods can be grouped into two paradigms: deep learning models for accurate simulation, and neural dynamics models emphasizing physiological interpretability. However, these methods entail a fundamental tradeoff between accuracy and interpretability. To address this challenge, we introduce the neurodynamics-informed brain simulator (NDIB-Si…

Health

AI‐Driven Personalized Nutrition: Integrating Omics, Ethics, and Digital Health

Personalized nutrition (PN) aims to prevent and manage chronic diseases by providing individualized dietary guidance based on genetic, metabolic, and lifestyle data. Artificial intelligence (AI) has become a key enabler in PN by analyzing large-scale, multiomics datasets in obesity, diabetes, cardiovascular, and gastrointestinal disorders, where digital twins and health knowledge graphs support personalized interventions. Current findings demonstrate that AI models can guide microbiome-based dietary intervention…

AI‐Driven Personalized Nutrition: Integrating Omics, Ethics, and Digital Health
Health

GIN-CRC-Pareto: A graph-based pareto-optimized multi-task learning framework to identify miRNA-target interactions in colorectal cancer

Background Colorectal cancer (CRC) ranks as the third highest incidence among malignancies for human and the second most common cause of cancer-related mortality in the United States. Accumulating evidence has established microRNAs (miRNAs) as critical regulators of cancer development and therapeutic response. Understanding miRNA-mRNA interactions is critical for elucidating the molecular mechanisms driving CRC and other malignancies. However, accurately modeling miRNA-mRNA interactions and their binding pattern…

GIN-CRC-Pareto: A graph-based pareto-optimized multi-task learning framework to identify miRNA-target interactions in colorectal cancer
Health

Improving 10-year cardiovascular disease risk prediction using automated machine learning

Aims To develop a cardiovascular disease (CVD) risk prediction model with improved accuracy and interpretability by integrating diverse risk factors and applying Automated Machine Learning (AutoML), thereby enhancing clinical utility over conventional models. Methods This is a prospective cohort study. Data were obtained from the Multi-Ethnic Study of Atherosclerosis (MESA), including baseline and fifth follow-up visits, comprising 4713 participants. Exercise and dietary data were harmonized via Metabolic Equiva…

Improving 10-year cardiovascular disease risk prediction using automated machine learning
Health

Dual-Approach AI for Pediatric Supracondylar Fractures: Multiclass Radiograph Classification with Explainable AI and Diagnostic Meta-analysis of AI-Based Computational Approaches

Rationale and objectives Pediatric supracondylar fractures (SCFs) are the most common elbow injury in children, yet radiographic diagnosis remains challenging due to complex developmental anatomy, with initially missed fracture rates of 17-77%. Prior artificial intelligence (AI) studies have been limited to binary classification frameworks without Gartland subtype differentiation, and no diagnostic test accuracy meta-analysis specific to supracondylar fractures exists. This study aimed to develop the first multi…

Dual-Approach AI for Pediatric Supracondylar Fractures: Multiclass Radiograph Classification with Explainable AI and Diagnostic Meta-analysis of AI-Based Computational Approaches
Health

The Translational Gap in AI for Oropharyngeal Squamous Cell Carcinoma: A TRIPOD+AI Scoping Review of Methodological Barriers to Treatment Deintensification

Rationale and objectives To map artificial intelligence (AI) and radiomics applications in computed tomography (CT), magnetic resonance imaging (MRI), and fluorodeoxyglucose positron emission tomography/CT (FDG-PET/CT) for oropharyngeal squamous cell carcinoma (OPSCC) in the context of human papillomavirus (HPV) status and treatment deintensification, evaluate reporting quality using TRIPOD+AI (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis + Artificial Intelligen…

The Translational Gap in AI for Oropharyngeal Squamous Cell Carcinoma: A TRIPOD+AI Scoping Review of Methodological Barriers to Treatment Deintensification
Health

Ultrasound Habitat Radiomics for Preoperative Prediction of Invasive Breast Cancer With a DCIS Component: A Dual-center Retrospective Study

Purpose To develop and externally validate an ultrasound-based habitat subregional radiomics model for preoperative prediction of invasive breast cancer with concomitant ductal carcinoma in situ (IBC-DCIS). Methods A total of 1063 pathologically confirmed breast cancer patients from two centers were retrospectively enrolled and divided into a training cohort (n = 637) and an external validation cohort (n = 426). Tumor regions of interest were manually delineated on two-dimensional ultrasound images and further p…

Ultrasound Habitat Radiomics for Preoperative Prediction of Invasive Breast Cancer With a DCIS Component: A Dual-center Retrospective Study
Health

Artificial Intelligence Accurately Assists in Billing for Orthopaedic Lower Extremity Surgery: Performance of the Mistral-NeMo Language Model

Purpose To elucidate Mistral-NeMo's proficiency as a novel artificial intelligence assistant to verify coding accuracy and improve efficiency in manual medical coding practices in orthopaedic surgery. Methods This study tested Mistral-NeMo on 1000 operative notes labeled with the Current Procedural Terminology (CPT) codes from 177 providers. In total, there were 46 unique CPT codes; the most common were 29881 (knee arthroscopy with meniscectomy for both medial and lateral menisci), 29880 (knee arthroscopy with m…

Artificial Intelligence Accurately Assists in Billing for Orthopaedic Lower Extremity Surgery: Performance of the Mistral-NeMo Language Model