Refractive Surgery

Interpretable Machine Learning for Stroke Recovery: Predicting Discharge and 3-Month Functional Outcomes.

IntroductionStroke is a leading cause of disability worldwide. This study uses Machine Learning models to investigate factors influencing modified Rankin Scale scores at discharge and three months post-discharge.MethodsData from 116 stroke patients were analyzed using four predictive models: Logistic Regression, Support Vector Machine, Random Forest, and Extreme Gradient Boosting (XGB). Shapley Ad...

Robust and interpretable unit level causal inference in neural networks for pediatric myopia.

Understanding causal mechanisms in deep learning is essential for clinical adoption, where interpretability and reliability are critical. Most existing AI systems act as black boxes, limiting transparency in medicine. We propose a causal inference framework integrated into neural networks to assess the influence of individual features on predictions. Using a prospective pediatric ophthalmology coh...

Longitudinal brain-age predictions comprising long-duration spaceflight missions.

Our study investigates the effects of long-duration spaceflight on brain aging in spacefarers using structural MRI and machine learning models. Pre-, ...

Characterising processing conditions that artifactually bias human brain tissue transcriptomes.

Autopsy-derived brain tissue analysis is crucial for understanding neurobiology, but post-mortem handling can introduce artifacts. We studied adult hu...

Identification and Validation of Liver Transplantation-Induced Acute Lung Injury Biomarkers Using a Bioinformatics and Experimental Approach.

BACKGROUND Acute lung injury (ALI) after liver transplantation (LT) is a critical complication negatively affecting transplant outcomes and patient su...

Prediction of 9 Artificial Intelligence-based Intraocular Lens Power Calculation Formulas in Long Caucasian Eyes.

PURPOSE: To compare accuracy of nine artificial intelligence (AI)- based intraocular lens (IOL) power calculation formulas in eyes with axial length >...

Artificial Intelligence-Driven Three-Dimensional Reconstruction in Lung Cancer Surgery: Current Status and Future Perspectives.

BACKGROUND: Three-dimensional (3D) reconstruction is an important adjunct in lung cancer surgery for visualizing pulmonary anatomy, supporting preoper...

AI-assisted video analysis of the Trendelenburg test: a feasibility study.

The Trendelenburg test is widely used to assess hip abductor function, but interpretation is typically subjective and only moderately reliable. Compen...

Artificial intelligence for surgical care in war-torn sudan: Feasibility, barriers, and ethical perspectives from a conflict zone.

BACKGROUND: Artificial intelligence (AI) offers transformative potential for clinical care, yet its deployment in conflict-affected, low-resource sett...

Clinical integration of artificial intelligence and mixed reality-based dynamic navigation for implant placement in the anterior maxilla.

OBJECTIVE: To report the first clinical application of an integrated workflow combining artificial intelligence (AI) with mixed reality-based dynamic ...

Peripheral Retinal Vessel Density in Children and Adolescents with Myopia: A Deep Learning Assessment.

INTRODUCTION: This study investigated the effect of axial length (AL) on peripheral retinal vessel density in children and adolescents and assessed wh...

Effect of Brain-Computer Interface-Controlled Ankle Robot Training on Post-Stroke Motor Rehabilitation and Resting QEEG Neuroplasticity: A Randomized Controlled Trial.

BACKGROUND: Persistent post-stroke ankle impairment hinders functional recovery. Brain-computer interface (BCI)-controlled ankle robot show rehabilita...

Deep Learning and Machine Learning for Differentiation Between Contrast Extravasation and Hemorrhagic Transformation in Post-Thrombectomy Stroke CT.

BACKGROUND: Mechanical thrombectomy (MT) improves outcomes in acute ischemic stroke (AIS) but often results in hyperdensities on non-contrast CT (NCCT...

Enhancing dental clinical education through AI: insights into post-clinical summaries.

BACKGROUND: Artificial Intelligence (AI) is increasingly being introduced into clinical education, including dentistry, as a supplement to traditional...

Predicting health-related quality of life two years post-diagnosis across seven cancer types: using machine learning to identify vulnerable patients.

PURPOSE: Cancer survivors often experience long-term consequences affecting their Health-Related Quality of Life (HRQoL). Sociodemographic factors, cl...

Recognizing EEG responses to active TMS vs. sham stimulations in different TMS-EEG datasets: A machine learning approach.

Transcranial Magnetic Stimulation with simultaneous Electroencephalogram (TMS-EEG) allows for the assessment of neurophysiological properties of corti...

Commercially Available Artificial Intelligence Score on Preoperative Mammography for Prediction of Future Breast Cancer After DCIS Treatment.

Background: Mammographic artificial intelligence (AI) systems have been explored for future breast cancer risk prediction. Objective: To investigate a...

Clinical evaluation of MiADE: a natural language processing system for assisting structured diagnosis recording at the point of care.

OBJECTIVES: To evaluate the usability, usefulness and impact of a novel point of care natural language processing (NLP) system, Medical information AI...

The human metabolome and machine learning improves predictions of the post-mortem interval.

An accurate prediction of the time since death, known as the post-mortem interval, remains a critical research question in forensic and police investi...

Rethinking multivariate modeling in long-term forecasting: an efficient univariate framework with power decomposition and post-Calibration.

Long-term time series forecasting (LTSF) is critical to industrial applications. While recent advances mainly focus on modeling complex multivariate i...