DL Models Predicting Cycloplegic Refractive Error Based on Non-Cycloplegic Parameters in Myopic Adults
Efficacy of Deep Learning Models for Predicting Cycloplegic Refractive Error Based on Non-Cycloplegic Parameters in Adults With Myopia
연구 개요
상태
상세 설명
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.
연구 유형
등록 (추정된)
연락처 및 위치
연구 연락처
- 이름: Jian Xiong
- 전화번호: 18170906556
- 이메일: 894040417@qq.com
연구 연락처 백업
- 이름: Fu Gui
- 전화번호: 1387910191
- 이메일: 564436578@qq.com
연구 장소
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Jiangxi, 중국
- 모병
- The Second Affiliated Hospital of Nanchang University, Nanchang, JiangXi 330000
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연락하다:
- Jian Xiong
- 전화번호: 18170906556
- 이메일: 894040417@qq.com
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연락하다:
- Fu Gui
- 전화번호: 13879101919
- 이메일: 564436578@qq.com
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참여기준
자격 기준
공부할 수 있는 나이
- 성인
건강한 자원 봉사자를 받아들입니다
샘플링 방법
연구 인구
설명
Inclusion Criteria:
- 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.
Exclusion Criteria:
- 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.
공부 계획
연구는 어떻게 설계됩니까?
디자인 세부사항
코호트 및 개입
그룹/코호트 |
개입 / 치료 |
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Group with spherical equivalent change ≥0.50 diopters after cycloplegic refraction
Adult myopes with a non-cycloplegic versus cycloplegic spherical equivalent difference of ≥0.50 diopters, for whom cycloplegic refraction is clinically warranted, received routine cycloplegic refraction with tropicamide; no other intervention was given.
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The machine learning model was applied to each participant's non-cycloplegic parameters to predict cycloplegic spherical equivalent.
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Group with spherical equivalent change <0.50 diopters after cycloplegic refraction
Adult myopes with an absolute difference of less than 0.50 diopters between non-cycloplegic and cycloplegic spherical equivalent, for whom non-cycloplegic refraction is considered sufficient, received routine cycloplegic refraction with tropicamide; no additional intervention was applied.
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The machine learning model was applied to each participant's non-cycloplegic parameters to predict cycloplegic spherical equivalent.
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연구는 무엇을 측정합니까?
주요 결과 측정
결과 측정 |
측정값 설명 |
기간 |
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Accuracy of predicted cycloplegic spherical equivalent
기간: Day 0
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Accuracy of the machine learning model in predicting cycloplegic spherical equivalent in the validation dataset, evaluated by mean absolute error, root mean square error, and coefficient of determination, expressed for spherical equivalent in diopters.
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Day 0
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2차 결과 측정
결과 측정 |
측정값 설명 |
기간 |
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Diagnostic performance for identifying patients requiring cycloplegic refraction
기간: Day 0
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Area under the receiver operating characteristic curve, sensitivity, and specificity of the model for classifying patients with an absolute difference of 0.50 diopters or more between non-cycloplegic and cycloplegic spherical equivalent in the validation dataset.
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Day 0
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Agreement between predicted and measured cycloplegic refraction
기간: Day 0
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Agreement between predicted and measured cycloplegic spherical equivalent assessed by Bland-Altman analysis with mean bias and 95% limits of agreement, and by the intraclass correlation coefficient in the validation dataset.
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Day 0
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연구 기록 날짜
연구 주요 날짜
연구 시작 (실제)
기본 완료 (추정된)
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최초 제출
QC 기준을 충족하는 최초 제출
처음 게시됨 (실제)
연구 기록 업데이트
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