Refractive Surgery

Artificial intelligence models predicting abnormal uterine bleeding after COVID-19 vaccination.

The rapid deployment of COVID-19 vaccines has necessitated the ongoing surveillance of adverse events, with abnormal uterine bleeding (AUB) emerging as a reported concern in vaccinated females. We aimed to develop a machine learning (ML) model to predict post-vaccination AUB in women aged less than 50 years. A large-scale national cohort, the Korean Nationwide Cohort (K-COV-N cohort), was utilized...

Post-Mortem imaging biobanks: Building data for reproducibility, standardization, and AI integration.

In recent years, post-mortem imaging has advanced with techniques such as Post-Mortem Computed Tomography (PMCT) and Post-Mortem Magnetic Resonance imaging (PMMR). PMCT is particularly useful for assessing skeletal injuries, vascular lesions, and estimating post-mortem intervals. These analyses are based on radiomics principles, a growing field that requires specialized expertise, which is still l...

Role of artificial intelligence in data-centric additive manufacturing processes for biomedical applications.

The role of additive manufacturing (AM) for healthcare applications is growing, particularly in the aspiration to meet subject-specific requirements. ...

Multiscale Dissection of Spatial Heterogeneity by Integrating Multi-Slice Spatial and Single-Cell Transcriptomics.

The spatial structure of cells is highly organized at multiscale levels from global spatial domains to local cell type heterogeneity. Existing methods...

Machine-learning random forest algorithms predict post-cycloplegic myopic corrections from noncycloplegic clinical data.

SIGNIFICANCE: Machine learning random forest algorithms were used to predict objective refractive outcomes after cycloplegic refraction using noncyclo...

ADFQ-ViT: Activation-Distribution-Friendly post-training Quantization for Vision Transformers.

Vision Transformers (ViTs) have exhibited exceptional performance across diverse computer vision tasks, while their substantial parameter size incurs ...

Soft-tissue metastasis in esophageal cancer managed by dose escalation radiation therapy: a clinical case and review of literature.

Soft tissue metastasis in esophageal cancer is a very rare entity. A 76-year-old man was referred for a week's history of dysphagia. Upper gastrointes...

Predicting PTSD development with early post-trauma assessments: a proof-of-concept for a concise tree-based classification method.

Approximately 70% of individuals globally experience at least one traumatic event in their lifetimes, potentially leading to posttraumatic stress dis...

Identifying plastic materials in post-consumer food containers and packaging waste using terahertz spectroscopy and machine learning.

Accurate identification of plastic materials from post-consumer food container and packaging waste is crucial for enhancing the purity and added value...

Towards clinical prediction with transparency: An explainable AI approach to survival modelling in residential aged care.

BACKGROUND AND OBJECTIVE: Scalable, flexible and highly interpretable tools for predicting mortality in residential aged care facilities for the purpo...

Managerial myopia and its barrier to green innovation in high-pollution enterprises: A machine learning approach.

Green technology innovation has become a vital remedy in response to the world's growing ecological problems and the urgent need for sustainable devel...

Biopsychosocial based machine learning models predict patient improvement after total knee arthroplasty.

Total knee arthroplasty (TKA) is an effective treatment for end stage osteoarthritis. However, biopsychosocial features are not routinely considered i...

Comparison of time-to-event machine learning models in predicting biliary complication and mortality rate in liver transplant patients.

Post-Liver transplantation (LT) survival rates stagnate, with biliary complications (BC) as a major cause of death. We analyzed longitudinal data with...

Deep Learning Model Compression and Hardware Acceleration for High-Performance Foreign Material Detection on Poultry Meat Using NIR Hyperspectral Imaging.

Ensuring the safety and quality of poultry products requires efficient detection and removal of foreign materials during processing. Hyperspectral ima...

Constructing a machine learning model for systemic infection after kidney stone surgery based on CT values.

This study aims to develop a machine learning model utilizing Computed Tomography (CT) values to predict systemic inflammatory response syndrome (SIRS...

Age and gender-related changes in choroidal thickness: Insights from deep learning analysis of swept-source OCT images.

BACKGROUND: The choroid is a vital vascular layer of the eye, essential for maintaining ocular health. Understanding its structural variations, partic...

Identification of hub biomarkers in liver post-metabolic and bariatric surgery using comprehensive machine learning (experimental studies).

BACKGROUND: The global prevalence of non-alcoholic fatty liver disease (NAFLD) is approximately 30%, and the condition can progress to non-alcoholic s...

Using machine learning to predict outcomes following transcarotid artery revascularization.

Transcarotid artery revascularization (TCAR) is a relatively new and technically challenging procedure that carries a non-negligible risk of complicat...

Exploring the assessment of post-cardiac valve surgery pulmonary complication risks through the integration of wearable continuous physiological and clinical data.

BACKGROUND: Postoperative pulmonary complications (PPCs) following cardiac valvular surgery are characterized by high morbidity, mortality, and econom...

Improving explainability of post-separation suicide attempt prediction models for transitioning service members: insights from the Army Study to Assess Risk and Resilience in Servicemembers - Longitudinal Study.

Risk of U.S. Army soldier suicide-related behaviors increases substantially after separation from service. As universal prevention programs have been ...