Ophthalmology

Refractive Surgery

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

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Machine learning based prediction of geotechnical parameters affecting slope stability in open-pit iron ore mines in high precipitation zone.

Rainfall and its interaction with soil, rock, and environmental factors such as soil moisture conten...

Auto-Segmentation via deep-learning approaches for the assessment of flap volume after reconstructive surgery or radiotherapy in head and neck cancer.

Reconstructive flap surgery aims to restore the substance and function losses associated with tumor ...

Personalized prediction model generated with machine learning for kidney function one year after living kidney donation.

Living kidney donors typically experience approximately a 30% reduction in kidney function after don...

Machine learning-driven national analysis for predicting adverse outcomes in intramedullary spinal cord tumor surgery.

UNLABELLED: Spinal tumors represent 15% of all central nervous system malignancies, with intramedull...

h-calibration: Rethinking Classifier Recalibration with Probabilistic Error-Bounded Objective.

Deep neural networks have demonstrated remarkable performance across numerous learning tasks but oft...

A 0.66-mm 0.49 pJ/SOP SNN Processor with Temporal-Spatial Post-Neuron-Processing and Model-Adaptive Crossbar in 40-nm CMOS.

This paper presents a Spiking Neural Network (SNN) processor specifically designed to overcome the l...

Ultrasound Displacement Tracking Techniques for Post-Stroke Myofascial Shear Strain Quantification.

OBJECTIVE: Ultrasound shear strain is a potential biomarker of myofascial dysfunction. However, the ...

The value of multimodal neuroimaging in the diagnosis and treatment of post-traumatic stress disorder: a narrative review.

Post-traumatic stress disorder (PTSD) is a delayed-onset or prolonged persistent psychiatric disorde...

An In-depth overview of artificial intelligence (AI) tool utilization across diverse phases of organ transplantation.

Artificial Intelligence (AI) offers a revolutionary approach to improve decision-making in medicine ...

Long-term COVID-19 symptoms and post-vaccination reactions among prolonged COVID-19 patients in the Kurdistan region of Iraq.

Vaccination has long been recognized as the most effective means for disease prevention, yet concern...

Research on the correlation between retinal vascular parameters and axial length in children using an AI-based fundus image analysis system.

OBJECTIVE: This study aims to utilize artificial intelligence technology to conduct an in-depth anal...

A Post-Quantum Blockchain and Autonomous AI-Enabled Scheme for Secure Healthcare Information Exchange.

Secure healthcare information exchange (HIE) is critical to improving medical services, enabling dat...

Precision-Optimised Post-Stroke Prognoses.

BACKGROUND: Current medicine cannot confidently predict who will recover from post-stroke impairment...

Interpretable machine learning models for predicting childhood myopia from school-based screening data.

This study assessed the efficacy of various diagnostic indicators and machine learning (ML) models i...

A Synergistic Approach Using Photoacoustic Spectroscopy and AI-Based Image Analysis for Post-Harvest Quality Assessment of Conference Pears.

This study presents a non-invasive approach to monitoring post-harvest fruit quality by applying CO ...

Predicting Resistance and Survival of HCC Patients Post-HAIC: Based on Shapley Additive exPlanations and Machine Learning.

PURPOSE: To establish prediction models using Shapley Additive exPlanations (SHAP) and multiple mach...

Identifying and predicting headache trajectories among those with acute post-traumatic headache.

OBJECTIVES/BACKGROUND: Post-traumatic headache (PTH) is a common symptom following mild traumatic br...

Machine Learning-Based Prediction of Post-Operative Systemic Inflammatory Response Syndrome Following Pediatric Percutaneous Nephrolithotripsy.

OBJECTIVE: This study aimed to develop and validate a machine learning-based model for predicting sy...

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