Clinical trial NCT07022444: Research Based on IOLMaster700 Cataract Diagnosis and Classification System
Official title: Research on Heterogeneous Intelligent Algorithm Based on IOLMaster700 Cataract Diagnosis and Classification System
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Cataract
Artificial Intelligence (AI)
Lead sponsor: Shanghai 10th People's Hospital
Sponsor class: OTHER
First posted: 2025-06-15
Enrollment: 2000
Sex: ALL
Minimum age (years): 40
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: patients who are diagnosed cataract at first visit with no eye surgery history.

Brief summary: 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.


Eligibility criteria: Inclusion Criteria:

* A. Age between 18 and 90 years B. Diagnosed with age-related and/or complicated cataract (diagnosed according to LOCS III classification) C. The patient has signed an informed consent form

Exclusion Criteria:

* A. Exclude patients with corneal diseases, uveitis, vitreoretinal diseases, or refractive media opacities caused by conditions such as retinal detachment with silicone oil tamponade B. History of previous ophthalmic disease treatment or surgery C. Poor-quality or missing imaging data D. Pupil diameter \< 2.5 mm or loss of fixation during examination, resulting in inability to obtain sufficient lens data

Primary outcomes:
measure: Cataract Grade
timeFrame: 3 months
measure: AI predicted cataract grade
timeFrame: 3 months
measure: AI model performance
timeFrame: 3 months
Secondary outcomes:
Other outcomes:
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