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NCT07022444
Research Based on IOLMaster700 Cataract Diagnosis and Classification System
trial testing IOL-MASTER 700 in Cataract in 2,000 participants. Not yet recruiting.
31 August 2025
Quick facts
| Lead sponsor | Shanghai 10th People's Hospital |
|---|---|
| Status | Not yet recruiting |
| Study type | OBSERVATIONAL |
| Enrollment | 2,000 |
| Start date | 15 June 2025 |
| Primary completion | 31 August 2025 |
| Estimated completion | 31 December 2025 |
Drugs / interventions tested
- IOL-MASTER 700
Conditions studied
- Cataract — all drugs for Cataract →
- Artificial Intelligence (AI) — all drugs for Artificial Intelligence (AI) →
Sponsor
Shanghai 10th People's Hospital
Who can join
40 and older, any sex, with Cataract or Artificial Intelligence (AI). Patients with the condition only — healthy volunteers not accepted.
Sponsor's own description
Cataract is a major cause of blindness due to eye diseases. Methods for evaluating the degree of lens opacification in cataracts are divided into subjective and objective methods. The commonly used subjective method is the Lens Opacification Classification System (LOCS Ⅲ), while the objective methods mainly include the Dysfunctional Lens Index (DLI) of the Ray Tracing aberration analysis system, the PNS score of the Pentacam anterior segment analysis system, etc. Subjective diagnosis may lead to certain misjudgments, which have affected clinical diagnosis and treatment. There is an urgent need to add objective diagnostic measures to assist clinical work. The Scanning Source Optical Coherence Tomography (SS - OCT) biometer - IOL Master 700 forms an OCT imaging of the eye based on the swept - source optical coherence tomography (OCT) biometric technology. It can visually show the longitudinal section of the entire lens, and the clear display of the patient's lens tomographic OCT image is obtained through image visualization measurement. The main purpose of this study is to analyze the lens images obtained by the IOLmaster 700. Based on the current mainstream algorithm models such as ResNet - 34 and XGBoost, develop a heterogeneous accelerated artificial intelligence algorithm according to our research needs to accurately calculate the degree of lens opacification. And write image analysis software by ourselves to automatically calculate the required indicators and output them. Establish a heterogeneous accelerated artificial intelligence - assisted lens opacification grading and prediction system, supporting software for biometer equipment, and a cataract lens image database. The software provides online service functions, and all researchers can use the image analysis function of the software after logging in, truly realizing the sharing of large instrument supporting software operations. Thereby improving the accuracy and efficiency of clinical diagnosis and treatment, the prognostic prediction level of patients after cataract surgery, guiding clinical diagnosis and treatment more accurately, and at the same time, it can be used as a tool for community screening.
Publications & conference data
No peer-reviewed publications indexed yet for this trial.
Verify or expand the search:
- PubMed search for NCT07022444
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Verify against primary sources
- ClinicalTrials.gov — authoritative US registry record
- WHO ICTRP — international registry index
- EU Clinical Trials Register
- Sponsor press releases (Google)
- Trial protocol + status: ClinicalTrials.gov NCT07022444 (US National Library of Medicine, public domain)
- Drug + disease cross-links: matched in real time against Drug Landscape's normalised drug + company + condition tables
- Sponsor: as reported to ClinicalTrials.gov by Shanghai 10th People's Hospital
- Last refreshed: 15 June 2025
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