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Идёт набор NCT07690813

DL Models Predicting Cycloplegic Refractive Error Based on Non-Cycloplegic Parameters in Myopic Adults

— Наблюдательное Refractive Errors Cycloplegic Refraction Accommodation

Ориентир для пациента и семьи

Простыми словами

Автоматическая сводка по структурированным данным реестра. Она помогает сориентироваться, но не заменяет официальный протокол или оценку врача.

Что изучают
В протоколе указаны: Machine learning model for predicting cycloplegic refraction.
Кому может быть актуально
Состояния в реестре: Refractive Errors, Cycloplegic Refraction, Accommodation. Базовые параметры: 18 лет — 47 лет · Все.
Что важно проверить
Возраст, диагноз и пол — только базовые ориентиры. Предыдущее лечение, анализы и другие обязательные условия указаны ниже в критериях участия.
Где проводится
Китай
Следующий шаг
Сохраните исследование, покажите его лечащему врачу и уточните актуальный статус у исследовательского центра. Расходы, документы и поездка →

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Официальное название

Efficacy of Deep Learning Models for Predicting Cycloplegic Refractive Error Based on Non-Cycloplegic Parameters in Adults With Myopia

Обзор

This study presents a machine learning model that predicts cycloplegic refraction in adults with myopia using standard non-cycloplegic eye measurements, aiming to reduce the need for cycloplegic drops while still identifying patients who require them.

Подробное описание

Myopia is a highly prevalent, irreversible refractive disorder with substantial impact on quality of life. Cycloplegic refraction is the gold standard for assessing refractive error in adults considering optical or surgical correction, but it is time-consuming, slow to recover from, and frequently associated with ocular discomfort. Non-cycloplegic refraction is therefore used routinely in clinical practice, despite known differences from cycloplegic values in a subset of adult myopes.

Critically, this discrepancy varies substantially between individuals and cannot be anticipated from non-cycloplegic measurements alone. Clinicians have no reliable way to identify, prior to dilation, which patients are likely to be overcorrected if cycloplegia is omitted, potentially leading to overcorrected prescriptions, asthenopia, and myopic progression.

Machine learning approaches that capture non-linear relationships between clinical predictors and refractive outcomes have shown promise in children, but comparable models for adults remain largely unexplored, and most rely on axial length, which is unavailable in routine optometric settings. Refractive surgery centers offer a uniquely suitable data source, as every candidate undergoes standardized paired non-cycloplegic and cycloplegic refraction with detailed anterior segment biometry during routine preoperative evaluation. This study leverages such data to develop and validate models estimating cycloplegic refractive error from non-cycloplegic parameters, providing a decision-support tool that reduces unnecessary cycloplegia while flagging patients for whom dilated refraction remains indicated.

Вмешательства

  • Диагностический тест Machine learning model for predicting cycloplegic refraction
    The machine learning model was applied to each participant's non-cycloplegic parameters to predict cycloplegic spherical equivalent.

Первичные конечные точки

  • Accuracy of predicted cycloplegic spherical equivalent [Срок оценки: Day 0]
Вторичные конечные точки (2)
  • Diagnostic performance for identifying patients requiring cycloplegic refraction [Срок оценки: Day 0]
  • Agreement between predicted and measured cycloplegic refraction [Срок оценки: Day 0]

Критерии участия

Критерии включения

  • Age 18 to 60 years, of either sex;
  • Spherical equivalent between -0.50 diopters and -10.00 diopters, with myopia in one or both eyes, and with cylinder of 4.00 diopters or less;
  • Best-corrected visual acuity of 20/25 or better in each eye;
  • Clear cornea, no keratoconus, corneal scarring, or other pathologies; clear lens;
  • Intraocular pressure of 21 mmHg or less, with no history of glaucoma;
  • No history of ocular surgery, especially corneal refractive surgery or cataract surgery;
  • Time interval between non-cycloplegic refraction and cycloplegic refraction of 7 days or less, with complete data.

Критерии исключения

  • Incomplete clinical data to support the diagnosis;
  • Ocular conditions such as subclinical keratoconus, keratoconus, or moderate-to-severe corneal haze or leukoma;
  • Allergy or contraindication to cycloplegic agents;
  • Refusal to participate in the study.

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Модель наблюдения
Другое

Центры проведения

Китай · 1 центр
  • The Second Affiliated Hospital of Nanchang University, Nanchang, JiangXi 330000 — Jiangxi

Идентификаторы

NCT: NCT07690813 · [2026] NO.(123)

Первоисточники (государственные реестры)

Открыть это исследование на ClinicalTrials.gov ↗