Trials / Recruiting
RecruitingNCT06669728
Study on the Diagnostic Efficacy of ICL Selection and Prediction Depth Model Based on Eye Images
Diagnostic Efficacy of Deep Neural Network Algorithm Based on Preoperative Scheimpflug-based Anterior Segment Image for Implantable Collamer Lens Selection and Prediction
- Status
- Recruiting
- Phase
- —
- Study type
- Observational
- Enrollment
- 326 (estimated)
- Sponsor
- Second Affiliated Hospital of Nanchang University · Academic / Other
- Sex
- All
- Age
- 18 Years – 45 Years
- Healthy volunteers
- Accepted
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.
Conditions
Interventions
| Type | Name | Description |
|---|---|---|
| DIAGNOSTIC_TEST | AI diagnostic algorithm | The ICL procedures collected would be assessed by the algorithm. The performance of the algorithm would be assessed, including accuracy, AUC, sensitivity and specificity. |
Timeline
- Start date
- 2021-01-02
- Primary completion
- 2025-08-31
- Completion
- 2025-08-31
- First posted
- 2024-11-01
- Last updated
- 2025-08-22
Locations
1 site across 1 country: China
Source: ClinicalTrials.gov record NCT06669728. Inclusion in this directory is not an endorsement.