Clinical trial NCT06669728: Study on the Diagnostic Efficacy of ICL Selection and Prediction Depth Model Based on Eye Images
Official title: Diagnostic Efficacy of Deep Neural Network Algorithm Based on Preoperative Scheimpflug-based Anterior Segment Image for Implantable Collamer Lens Selection and Prediction
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Posterior Chamber Phakic Intraocular Lens
Vault
Deep Neural Network
Myopia
Anterior Chamber Angle
Keywords: Posterior Chamber Phakic Intraocular Lens
Vault
Deep Neural Network
Lead sponsor: Second Affiliated Hospital of Nanchang University
Sponsor class: OTHER
Collaborators: name: Nanchang Bright Eye Hospital
First posted: 2024-11-01
Enrollment: 326
Sex: ALL
Minimum age (years): 18
Maximum age (years): 45
Standard ages: ADULT
Accepts healthy volunteers: yes
Observational model: OTHER
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients from clinics in different eye centers across China. Each subject must with complete surgical video recording and medical records.

Brief summary: To evaluate the diagnostic efficacy of deep learning network model in implantable collamer lens selection and prediction in a multicenter cross-sectional study

Detailed description: Posterior chamber intraocular lens implantation is an main choice for myopia correction. Implantable collamer lens (ICL) is currently the most widely used, and the official reference index is mainly based on biological parameters obtained from eye images. The parameter acquisition and selection of ICL design are often controversial, forcing the doctors to synthesize multiple modal data, making the optimization of ICL formula being a focus of attention in refractive surgery. This research aimed to build an image-based ICL prediction algorithm to assist human physicians in decision-making and improve the accuracy, safety and predictability of ICL implantation.

Eligibility criteria: Inclusion Criteria:

1. Aged 18-45 years ;
2. Myopia, with or without astigmatism, annual diopter change ≤ 0.50 D for 2 consecutive years ;
3. Anterior chamber depth ≥ 2.80 mm ;
4. Corneal endothelial cell count ≥ 2000 / mm2, stable cell morphology ;
5. There were no other ocular diseases that significantly affected vision and / or systemic organic lesions that affected surgical recovery.

Exclusion Criteria:

1. There were no other ocular diseases that significantly affected vision and / or systemic organic lesions that affected surgical recovery;
2. Have a history of corneal refractive surgery or intraocular surgery ;
3. Corneal endothelial cell count is low ;
4. Those with systemic diseases ;
5. Lactating or pregnant women.

Primary outcomes:
measure: AUROC of convolutional neural network in predicting vault after ICL surgery
timeFrame: Day 7
measure: AUROC of convolutional neural network in predicting anterior chamber angle after ICL implantation
timeFrame: Day 7
Secondary outcomes:
measure: Sensitivity and specificity of convolutional neural network in predicting Vault after ICL implantation
timeFrame: Day 7
measure: Sensitivity and specificity of convolutional neural network in predicting anterior chamber angle after ICL implantation
timeFrame: Day 7
Other outcomes:
Locations:
city: Nanchang
country: China
facility: The Second Affiliated Hospital of Nanchang University
state: Jiangxi
Study officials:
References: