CataractNCT07022444
Research Based on IOLMaster700 Cataract Diagnosis and Classification System
An observational study of Cataract and Artificial Intelligence (AI), run by Shanghai 10th People's Hospital.
Trial 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. Read more about this study
- Who is it for?
- Ages 40 years and older
- Conditions the study is for
- Cataract, Artificial Intelligence (AI)
- What is being tested?
- IOL-MASTER 700
- Type of treatment
- Medical device
- How many people
- 2,000 participants
- Where
- No sites listed yet
- Study dates
- 15 Jun 2025to 31 Dec 2025
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 40 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: patients who are diagnosed cataract at first visit with no eye surgery history.
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: Research on Heterogeneous Intelligent Algorithm Based on IOLMaster700 Cataract Diagnosis and Classification System
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.
- Conditions
- Cataract, Artificial Intelligence (AI)
- Being tested
- IOL-MASTER 700
- Medical subject headings
- Cataract
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
IOL-MASTER 700Device
patients who were diagnosed cataract would go through tests with IOL-MASTER 700 to achieve ocular biometry parameters.
| Study group | Role | What they receive |
|---|---|---|
| Cataract patients | Patients with cataract diagnosed in hospital from October 2019 to October 2024. Receives: Device: IOL-MASTER 700 |
What is being measured
The primary outcome is the main question the study is designed to answer.
Cataract Grade
Achieved cataract images from IOL-MASTER 700 will be graded by several experienced doctors. And each image will be graded in three parts: cortical, nuclear and posterior subcapsular opacification.
Measured: 3 months
AI predicted cataract grade
Achieved cataract images from IOL-MASTER 700 will be used to train, validate and test the AI algorithm. Predicted grade will be carried out and each image will be graded by AI in three parts: cortical, nuclear and posterior subcapsular opacification.
Measured: 3 months
AI model performance
To evaluate the model performance, several indices are introduced: Sensitivity, Specificity, Accuracy, Precision, F1 score, receiver operating characteristic curve and area under the curve.
Measured: 3 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted15 Jun 2025
- Study start15 Jun 2025 (estimated)
- Primary completion31 Aug 2025 (estimated)
- Study completion31 Dec 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for CataractStudies 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
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 2,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Shanghai 10th People's Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Sun Yilin, Shanghai 10th People's Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07022444
- Sponsor's protocol ID
- SHSY-IEC-4.1/21-314/01
- Status last verified
- Jun 2025
- First submitted
- 8 Jun 2025
- Last updated
- 15 Jun 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 15 Jun 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Research on Heterogeneous Intelligent Algorithm Based on IOLMaster700 Cataract Diagnosis and Classification System
- ClinicalTrials.gov ID
- NCT07022444
- Acronym / sponsor ID
- SHSY-IEC-4.1/21-314/01
- Conditions
- Cataract, Artificial Intelligence (AI)
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 2,000 participants (estimated)
- Lead sponsor
- Shanghai 10th People's HospitalUniversity, hospital or other
- Study start
- 15 Jun 2025 (estimated)
- Primary completion
- 31 Aug 2025 (estimated)
- Study completion
- 31 Dec 2025 (estimated)
- First posted
- 15 Jun 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Cataract patients | – | Device: IOL-MASTER 700Patients with cataract diagnosed in hospital from October 2019 to October 2024. |
Who may qualify
- Ages
- 40 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- patients who are diagnosed cataract at first visit with no eye surgery history.
Key inclusion criteria (1 listed)
- 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
Key exclusion criteria (1 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| IOL-MASTER 700patients who were diagnosed cataract would go through tests with IOL-MASTER 700 to achieve ocular biometry parameters. | Device | Cataract patients |
Primary outcome
Cataract Grade
Time frame: 3 months
Achieved cataract images from IOL-MASTER 700 will be graded by several experienced doctors. And each image will be graded in three parts: cortical, nuclear and posterior subcapsular opacification.
AI predicted cataract grade
Time frame: 3 months
Achieved cataract images from IOL-MASTER 700 will be used to train, validate and test the AI algorithm. Predicted grade will be carried out and each image will be graded by AI in three parts: cortical, nuclear and posterior subcapsular opacification.
AI model performance
Time frame: 3 months
To evaluate the model performance, several indices are introduced: Sensitivity, Specificity, Accuracy, Precision, F1 score, receiver operating characteristic curve and area under the curve.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 Jun 2025.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Questions about this trial
What is this clinical trial studying?
An observational study of Cataract and Artificial Intelligence (AI), run by Shanghai 10th People's Hospital.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 15 Jun 2025. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Yiwen Hu (+86 18019320181, 1006108590@qq.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Cataract Grade (time frame: 3 months). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 15 Jun 2025.