AIMC Topic: Aged

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AI-Safe-C score: Assessing liver-related event risks in patients without cirrhosis after successful direct-acting antiviral treatment.

Journal of hepatology
BACKGROUND & AIMS: Direct-acting antivirals (DAAs) have considerably improved chronic hepatitis C (HCV) treatment; however, follow-up after sustained virological response (SVR) typically neglects the risk of liver-related events (LREs). This study in...

Exploring Deep Learning Applications using Ultrasound Single View Cines in Acute Gallbladder Pathologies: Preliminary Results.

Academic radiology
RATIONALE AND OBJECTIVES: In this preliminary study, we aimed to develop a deep learning model using ultrasound single view cines that distinguishes between imaging of normal gallbladder, non-urgent cholelithiasis, and acute calculous cholecystitis r...

Development of machine learning models to predict papillary carcinoma in thyroid nodules: The role of immunological, radiologic, cytologic and radiomic features.

Auris, nasus, larynx
OBJECTIVE: Approximately 30 % of thyroid nodules yield an indeterminate diagnosis through conventional diagnostic strategies. The aim of this study was to develop machine learning (ML) models capable of identifying papillary thyroid carcinomas using ...

Identification of Nocturnal Leg Cramps and Affecting Factors in COPD Patients: Logistic Regression and Artificial Neural Network.

Clinical nursing research
Although there are many sleep-related complaints in chronic obstructive pulmonary disease (COPD) patients, nocturnal leg cramps have not been adequately and extensively studied. This study fills a significant gap in the literature by determining the ...

Enhanced Osteoporosis Detection Using Artificial Intelligence: A Deep Learning Approach to Panoramic Radiographs with an Emphasis on the Mental Foramen.

Medical sciences (Basel, Switzerland)
Osteoporosis, a skeletal disorder, is expected to affect 60% of women aged over 50 years. Dual-energy X-ray absorptiometry (DXA) scans, the current gold standard, are typically used post-fracture, highlighting the need for early detection tools. Pano...

Effects of high-definition tDCS targeting individual motor hotspot with EMG-driven robotic hand training on upper extremity motor function: a pilot randomized controlled trial.

Journal of neuroengineering and rehabilitation
BACKGROUND: Delivering HD-tDCS on individual motor hotspot with optimal electric fields could overcome challenges of stroke heterogeneity, potentially facilitating neural activation and improving motor function for stroke survivors. However, the inte...

Personalized Prediction of Long-Term Renal Function Prognosis Following Nephrectomy Using Interpretable Machine Learning Algorithms: Case-Control Study.

JMIR medical informatics
BACKGROUND: Acute kidney injury (AKI) is a common adverse outcome following nephrectomy. The progression from AKI to acute kidney disease (AKD) and subsequently to chronic kidney disease (CKD) remains a concern; yet, the predictive mechanisms for the...

Machine Learning of Laboratory Data in Predicting 30-Day Mortality for Adult Hemophagocytic Lymphohistiocytosis.

Journal of clinical immunology
BACKGROUND: Hemophagocytic Lymphohistiocytosis (HLH) carries a high mortality rate. Current existing risk-evaluation methodologies fall short and improved predictive methods are needed. This study aimed to forecast 30-day mortality in adult HLH patie...

Nuclear magnetic resonance-based metabolomics with machine learning for predicting progression from prediabetes to diabetes.

eLife
BACKGROUND: Identification of individuals with prediabetes who are at high risk of developing diabetes allows for precise interventions. We aimed to determine the role of nuclear magnetic resonance (NMR)-based metabolomic signature in predicting the ...