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Dynamic thumb localization and its adaptation: quantification with a novel robotic task.

Experimental brain research
The thumb plays a crucial role in hand function, yet its proprioceptive abilities remain poorly understood. Here we quantified dynamic thumb localization ability, as well as how this ability adapts to a perturbation, in unimpaired participants. For t...

Machine Learning Analysis of Retrospective Data From 503 Hospitalized Older Patients With Type 2 Diabetes to Identify Factors Associated With Cognitive Impairment.

Medical science monitor : international medical journal of experimental and clinical research
BACKGROUND Diabetes is increasingly prevalent among older adults; mild cognitive impairment (MCI) comorbidity in this group represents a major concern. Existing MCI prediction methods are often inaccurate, but machine learning (ML) offers improved po...

Machine learning-based integration of tumor deposit molecular signatures improves prognostic stratification in colon adenocarcinoma.

International journal of colorectal disease
BACKGROUND: Colon adenocarcinoma (COAD) remains a leading cause of cancer-related mortality worldwide. Although tumor deposits (TDs) are established prognostic indicators, their molecular characteristics and potential for improving risk stratificatio...

An interpretable machine learning model based on MRI radiomics and GAME score for predicting early recurrence after thermal ablation in colorectal liver metastases.

International journal of colorectal disease
OBJECTIVE: To develop and validate machine learning models based on preoperative magnetic resonance imaging(MRI) and baseline clinical characteristics for predicting early recurrence(ER) in patients with colorectal liver metastases(CRLM) treated with...

Ensemble deep learning architectures for detecting pulmonary tuberculosis in chest X-rays.

Scientific reports
Tuberculosis (TB) remains a major global health challenge, causing approximately 1.4 million deaths annually. In many high-burden regions, limited access to expert radiological interpretation leads to delayed or missed diagnoses. To address this, we ...

Early Prediction of Cardiac Arrest Based on Time-Series Vital Signs Using Deep Learning: Retrospective Study.

JMIR formative research
BACKGROUND: Cardiac arrest (CA), characterized by an extremely high mortality rate, remains one of the most pressing global public health challenges. It not only causes a substantial strain on health care systems but also severely impacts individual ...

Physical Activity Recommendations Tailored by a Predictive Model for Adults With High Blood Pressure: Observational Study.

Journal of medical Internet research
BACKGROUND: Whether the benefits of identical physical activity (PA) patterns for adults with high blood pressure (BP) vary according to an individual's characteristics has not been adequately studied.

Selective classification with machine learning uncertainty estimates improves ACS prediction: a retrospective study in the prehospital setting.

Scientific reports
Accurate identification of acute coronary syndrome (ACS) in the prehospital setting is important for timely treatments that reduce damage to the compromised myocardium. Current machine learning approaches lack sufficient performance to safely rule-in...

AI-enabled electrocardiogram alert for potassium imbalance treatment: a pragmatic randomized controlled trial.

Nature communications
Life-threatening dyskalemia, defined as an abnormal serum potassium concentration, is common in emergency settings that requires timely recognition and treatment and can be detected via AI-enabled electrocardiography. We conducted a pragmatic, open-l...