Posterior Chamber Phakic Intraocular LensNCT06669728
Study on the Diagnostic Efficacy of ICL Selection and Prediction Depth Model Based on Eye Images
An observational study of Posterior Chamber Phakic Intraocular Lens and Vault, run by Second Affiliated Hospital of Nanchang University at 1 site in China.
Trial summary
To evaluate the diagnostic efficacy of deep learning network model in implantable collamer lens selection and prediction in a multicenter cross-sectional study Read more about this study
- Who is it for?
- Ages 18 to 45 yearsHealthy volunteers can take part
- Conditions the study is for
- Posterior Chamber Phakic Intraocular Lens, Vault, Deep Neural Network
- What is being tested?
- AI diagnostic algorithm
- How many people
- 326 participants
- Where
- 1 siteChina
- Study dates
- 2 Jan 2021to 31 Aug 2027
- Phase
- Observational
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 45 years
- All sexes
- Healthy volunteers accepted
- Adult
Study population: Patients from clinics in different eye centers across China. Each subject must with complete surgical video recording and medical records.
Who may be able to join
- Aged 18-45 years ;
- Myopia, with or without astigmatism, annual diopter change ≤ 0.50 D for 2 consecutive years ;
- Anterior chamber depth ≥ 2.80 mm ;
- Corneal endothelial cell count ≥ 2000 / mm2, stable cell morphology ;
- There were no other ocular diseases that significantly affected vision and / or systemic organic lesions that affected surgical recovery.
Who cannot join
- There were no other ocular diseases that significantly affected vision and / or systemic organic lesions that affected surgical recovery;
- Have a history of corneal refractive surgery or intraocular surgery ;
- Corneal endothelial cell count is low ;
- Those with systemic diseases ;
- Lactating or pregnant women.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
The Second Affiliated Hospital of Nanchang University
Nanchang, Jiangxi, 330000
Jian Xiong doctor, +8618170906556, 894040417@qq.com
Fu Gui doctor, +8613879101919, 564436578@qq.com
About this study
Official title: Diagnostic Efficacy of Deep Neural Network Algorithm Based on Preoperative Scheimpflug-based Anterior Segment Image for Implantable Collamer Lens Selection and Prediction
To evaluate the diagnostic efficacy of deep learning network model in implantable collamer lens selection and prediction in a multicenter cross-sectional study
Read the 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
- Posterior Chamber Phakic Intraocular Lens, Vault, Deep Neural Network, Myopia, Anterior Chamber Angle
- Being tested
- AI diagnostic algorithm
- Keywords
- Posterior Chamber Phakic Intraocular Lens, Vault, Deep Neural Network
- Medical subject headings
- Myopia
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI diagnostic algorithmDiagnostic test
The ICL procedures collected would be assessed by the algorithm. The performance of the algorithm would be assessed, including accuracy, AUC, sensitivity and specificity.
| Study group | Role | What they receive |
|---|---|---|
| Eyes with ICL surgeries | Eyes with SMILE surgeries which were performed by surgeons with experiences. Receives: Diagnostic Test: AI diagnostic algorithm |
What is being measured
The primary outcome is the main question the study is designed to answer.
AUROC of convolutional neural network in predicting vault after ICL surgery
The area under the receiver operating characteristic of convolutional neural network in predicting vault after ICL surgery
Measured: Day 7
AUROC of convolutional neural network in predicting anterior chamber angle after ICL implantation
The area under the receiver operating characteristic of convolutional neural network in predicting anterior chamber angle after ICL implantation
Measured: Day 7
Secondary outcomes (2)
Sensitivity and specificity of convolutional neural network in predicting Vault after ICL implantation
Sensitivity and specificity of convolutional neural network in predicting Vault after ICL implantation
Measured: Day 7
Sensitivity and specificity of convolutional neural network in predicting anterior chamber angle after ICL implantation
Sensitivity and specificity of convolutional neural network in predicting anterior chamber angle after ICL implantation
Measured: Day 7
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 Nov 2024
- Study start2 Jan 2021
- Primary completion31 Aug 2027 (estimated)
- Study completion31 Aug 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Posterior Chamber Phakic Intraocular LensStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Cross-sectional
- Enrolment
- 326 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Second Affiliated Hospital of Nanchang University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Jian Xiong, Second Affiliated Hospital of Nanchang University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06669728
- Sponsor's protocol ID
- [2024] NO.(93)
- Status last verified
- Jan 2026
- First submitted
- 30 Oct 2024
- Last updated
- 24 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No