AIMC Topic: Follow-Up Studies

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Prediction of Poor Visual Outcomes at Idiopathic Intracranial Hypertension Diagnosis Using a Supervised Machine Learning Algorithm.

Journal of neuro-ophthalmology : the official journal of the North American Neuro-Ophthalmology Society
BACKGROUND: Idiopathic intracranial hypertension (IIH) is a vision-threatening disorder mainly affecting women of a reproductive age. Prompt diagnosis and intervention are vital to prevent vision loss, but validated tools to predict visual outcomes a...

Prediction of perimetric progression in ocular hypertension and open angle glaucoma based on corneal biomechanics.

European journal of ophthalmology
PurposeTo identify parameters that are significant risk predictors of visual field (VF) progression in patients with ocular hypertension (OHT) or early primary open-angle glaucoma (POAG), using Goldmann applanation tonometry intraocular pressure (IOP...

Foam sclerotherapy for symptomatic cysts in ADPKD, ADPLD and solitary cysts.

Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association
BACKGROUND: This medical center migrated from alcohol to sotradecol foam sclerotherapy (SFS) because of perceived improved efficacy in managing symptomatic kidney and liver cysts. We report technical aspects, change in short- and long-term cyst volum...

Alternations of Gut Microbiome and Serum Metabolome With Prolongation of the Course of Type 1 Diabetes Mellitus.

Diabetes/metabolism research and reviews
AIMS: We aimed to explore the gut microbial and serum metabolic disturbances associated with the course of type 1 diabetes mellitus (T1DM), and identify potential biomarkers for discriminating T1DM from normoglycemia individuals by machine learning.

Assessment of outcomes and machine Learning-based models to predict local failure risk following stereotactic radiosurgery for small brain metastases.

Journal of neuro-oncology
INTRODUCTION: We assessed the outcomes of stereotactic radiosurgery (SRS) for small intact brain metastases (SBM) (≤ 2 cm) and developed machine learning (ML) algorithms to predict the probability of local failure (LF).

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

Japanese journal of ophthalmology
PURPOSE: To investigate the usefulness of a machine learning (ML) model that can predict the natural course of childhood myopia in evaluation of the inhibitory effects of 0.125% atropine on the progression of childhood myopia.

Development and Validation of a Machine Learning-Based Predictive Model for Postoperative Frailty in Patients with Non-Small Cell Lung Cancer and Its Relation to Early Recovery.

Annals of surgical oncology
PURPOSE: This study was designed to evaluate the postoperative frailty status of patients with non-small cell lung cancer, identify influencing factors, establish a machine learning-based prediction model, and explore the correlation between frailty ...

A supervised machine learning approach for predicting the need for postsurgical intervention in acromegaly.

Neurosurgical focus
OBJECTIVE: Patients with growth hormone (GH)-secreting pituitary adenomas (PAs) experience various symptoms and comorbidities, which can ultimately lead to increased mortality. This study aimed to develop and validate a machine learning (ML) model fo...

Fundus Refraction Offset as a Personalized Biomarker for 12-Year Risk of Retinal Detachment.

Investigative ophthalmology & visual science
PURPOSE: The purpose of this study was to investigate the potential of a novel anatomical metric of ametropia-fundus refraction offset (FRO)-in stratifying the risk of retinal detachment (RD) or breaks, beyond the influence of risk factors including ...