Refractive Surgery

Predicting and Diagnosing Pneumonia in Patients Undergoing Elective Cardiac Surgery via Machine Learning Analysis of Exhaled Volatile Carbonyl Compounds.

OBJECTIVE: Pneumonia remains one of the most common post-operative complications after elective cardiac surgery. Early intervention could lead to improved patient outcomes, including lower rates of ICU admissions, and shorter hospital stays. Volatile organic compounds (VOCs) in exhaled breath have shown promise in diagnosis and classification for various lung-related conditions. The study aims to ...

Deep learning strategies for semantic segmentation of pediatric brain tumors in multiparametric MRI.

Automated segmentation of pediatric brain tumors (PBTs) can support precise diagnosis and treatment monitoring, but it is still poorly investigated in literature. This study proposes two different Deep Learning approaches for semantic segmentation of tumor regions in PBTs from MRI scans. Two pipelines were developed for segmenting enhanced tumor (ET), tumor core (TC), and whole tumor (WT) in pedia...

Parsimonious and explainable machine learning for predicting mortality in patients post hip fracture surgery.

Hip fractures among the elderly population continue to present significant risks and high mortality rates despite advancements in surgical procedures....

AI in Learning Anatomy and Restoring Central Incisors: A Comparative Study.

More than 1 billion individuals worldwide have experienced dental trauma, particularly children aged 7 to 12 y, predominantly affecting the anterior t...

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 donation, although the degree of reduction varies amo...

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 resection. Automatic flap segmentation could allow...

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 content, temperature variations, groundwater levels, and...

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 intramedullary spinal cord tumors (IMSCTs) being rare. Predom...

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

Deep neural networks have demonstrated remarkable performance across numerous learning tasks but often suffer from miscalibration, resulting in unreli...

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 through the use of advanced computational tools. I...

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 analysis of fundus data from myopic children and adole...

FairICP: identifying biases and increasing transparency at the point of care in post-implementation clinical decision support using inductive conformal prediction.

OBJECTIVES: Fairness concerns stemming from known and unknown biases in healthcare practices have raised questions about the trustworthiness of Artifi...

Precision-Optimised Post-Stroke Prognoses.

BACKGROUND: Current medicine cannot confidently predict who will recover from post-stroke impairments. Researchers have sought to bridge this gap by t...

Robotic-assisted gait training for spinal cord injury neuropathic pain: A systematic review.

CONTEXT: Neuropathic pain (NP), can be a debilitating consequence of spinal cord injury. Robotic-assisted gait training (RAGT) is an effective rehabil...

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 in predicting childhood myopia. A total of 2,365 ch...

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 laser photoacoustic spectroscopy (COLPAS) to study...

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 brain injury (mTBI). Currently, there is no identifi...

Suitability of just-in-time adaptive intervention in post-COVID-19-related symptoms: A systematic scoping review.

Patients with post-COVID-19-related symptoms require active and timely support in self-management. Just-in-time adaptive interventions (JITAI) seem pr...

MicroRNA signature predicts post operative atrial fibrillation after coronary artery bypass grafting.

Early detection of atrial fibrillation (AFib) is crucial for altering its natural progression and complication profile. Traditional demographic and li...

Prediction of Myopia Among Undergraduate Students Using Ensemble Machine Learning Techniques.

BACKGROUND AND AIMS: Myopia is a prevalent refractive error, particularly among young adults, and is becoming a growing global concern. This study aim...