Ophthalmology

Refractive Surgery

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

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Comparison of mask R-CNN and YOLOv8-seg for improved monitoring of the PCB surface during laser cleaning.

Potting compounds and coatings protect electronic components in harsh environments, requiring carefu...

Dynamic Predictive Models of Cardiogenic Shock in STEMI: Focus on Interventional and Critical Care Phases.

: While early risk stratification in STEMI is essential, the threat of cardiogenic shock (CS) persis...

The Ethics of Speaking (of) AIs Through the Lens of Natural Language.

This theoretical essay offers a critical exploration of the ethics involved in interacting with and ...

Evaluating efficacy of 0.125% atropine using a myopia progression machine learning model.

PURPOSE: To investigate the usefulness of a machine learning (ML) model that can predict the natural...

Heat to hypoxia cross-adaptation: Effects of 6-week post-exercise hot-water immersion on exercise performance in acute hypoxia.

Cross-adaptation occurs when exposure to one environmental stressor (e.g., heat) induces protective ...

Using machine learning models to predict post-revascularization thrombosis in PAD.

BACKGROUND: Graft/ stent thrombosis after lower extremity revascularization (LER) is a serious compl...

CLEAR-Shock: Contrastive LEARning for Shock.

Shock is a life-threatening condition characterized by generalized circulatory failure, which can ha...

Adopting machine learning to predict nomogram for small incision lenticule extraction (SMILE).

PURPOSE: To predict nomogram for small incision lenticule extraction (SMILE) using machine learning ...

Machine learning models for acute kidney injury prediction and management: a scoping review of externally validated studies.

Despite advancements in medical care, acute kidney injury (AKI) remains a major contributor to adver...

Leveraging AI to explore structural contexts of post-translational modifications in drug binding.

Post-translational modifications (PTMs) play a crucial role in allowing cells to expand the function...

Interpretable machine learning model for predicting post-hepatectomy liver failure in hepatocellular carcinoma.

Post-hepatectomy liver failure (PHLF) is a severe complication following liver surgery. We aimed to ...

Development and external validation of a model for post-endoscopic retrograde cholangiopancreatography pancreatitis.

Post-endoscopic retrograde cholangiopancreatography (ERCP) pancreatitis (PEP) is a common complicati...

Using machine learning involving diagnoses and medications as a risk prediction tool for post-acute sequelae of COVID-19 (PASC) in primary care.

BACKGROUND: The aim of our study was to determine whether the application of machine learning could ...

Bayesian-optimized recursive machine learning for predicting human-induced changes in suspended sediment transport.

The suspended sediment load (SSL) of a river is a key indicator of water resource management, river ...

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