Ophthalmology

Refractive Surgery

Latest AI and machine learning research in refractive surgery for healthcare professionals.

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Leveraging advanced graph neural networks for the enhanced classification of post anesthesia states to aid surgical procedures.

Anesthesia plays a pivotal role in modern surgery by facilitating controlled states of unconsciousne...

Facial surgery preview based on the orthognathic treatment prediction.

BACKGROUND AND OBJECTIVE: Orthognathic surgery consultations are essential for helping patients unde...

Deep learning-based post hoc denoising for 3D volume-rendered cardiac CT in mitral valve prolapse.

We hypothesized that deep learning-based post hoc denoising could improve the quality of cardiac CT ...

Post-stroke spontaneous motor recovery in mice can be predicted from acute-phase local field potential using machine learning.

Stroke remains a leading cause of long-term disability, underscoring the urgent need for effective p...

Deep learning unlocks the true potential of organ donation after circulatory death with accurate prediction of time-to-death.

Increasing the number of organ donations after circulatory death (DCD) has been identified as one of...

Artificial intelligence in the diagnosis and management of refractive errors.

Refractive error is among the leading causes of visual impairment globally. The diagnosis and manage...

Comparative performance analysis of global and chinese-domain large language models for myopia.

BACKGROUND: The performance of global large language models (LLMs), trained largely on Western data,...

Utilizing machine learning and geographic analysis to improve Post-crash traffic injury management and emergency response systems.

Traffic injuries are a major public health concern globally. This study uses machine learning (ML) a...

Development of an explainable prediction model for portal vein system thrombosis post-splenectomy in patients with cirrhosis.

BACKGROUND: Portal vein system thrombosis (PVST) is a common and potentially life-threatening compli...

Clinical implementation of AI-based screening for risk for opioid use disorder in hospitalized adults.

Adults with opioid use disorder (OUD) are at increased risk for opioid-related complications and rep...

Emerging Models of Care Using IT in Long-Term/Post-Acute Care: A Comparative Analysis of Human and AI-Driven Qualitative Insights.

PURPOSE: As the global population ages, long-term/post-acute care (LTPAC) systems face challenges in...

Circulating microRNA profiles are associated with acute pain and stress in castrated and tail docked lambs.

Maintaining animal welfare is an essential component of animal production systems. However, multiple...

Automated segmentation of brain metastases in T1-weighted contrast-enhanced MR images pre and post stereotactic radiosurgery.

BACKGROUND AND PURPOSE: Accurate segmentation of brain metastases on Magnetic Resonance Imaging (MRI...

Post-Anesthesia Care Unit (PACU) readiness predictions using machine learning: a comparative study of algorithms.

INTRODUCTION: Accurate and timely discharge from the Post-Anesthesia Care Unit (PACU) is essential t...

Transforming neurodegenerative disorder care with machine learning: Strategies and applications.

Neurodegenerative diseases (NDs), characterized by progressive neuronal degeneration and manifesting...

Personalized prediction of psoriasis relapse post-biologic discontinuation: a machine learning-driven population cohort study.

BACKGROUND: Identifying the risk of psoriasis relapse after discontinuing biologics can help optimiz...

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