CataractNCT07713069
OCT-PRO Model vs. Clinicians: Cataract Surgery Outcome Prediction
A study testing OCT-PRO prediction model and Routine Preoperative Counseling in people with Cataract, run by Zhongshan Ophthalmic Center, Sun Yat-sen University.
Trial summary
Cataract is the leading cause of blindness worldwide, yet 5-20% of patients fail to achieve satisfactory visual recovery after surgery. Current methods for predicting postoperative visual acuity lack accuracy, particularly in patients with co-morbid fundus diseases. Read more about this study
- Who is it for?
- Ages 18 to 90 years
- Conditions the study is for
- Cataract
- What is being tested?
- OCT-PRO prediction model and Routine Preoperative Counseling
- Type of treatment
- Medical device, Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 534 participants
- Where
- No sites listed yet
- Study dates
- 20 Jul 2026to 31 Dec 2026
- Phase
- Interventional
- 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 18 to 90 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Age ≥18 years scheduled to undergo phacoemulsification with intraocular lens (Phaco+IOL) implantation.
- Outpatient diagnosis of senile, complicated, or metabolic cataract.
- For bilateral cataracts, the eye with more advanced disease will be included.
Who cannot join
- History of amblyopia or neuro-ophthalmic disease in the operative eye.
- Poor-quality OCT images precluding clear visualization of fundus structures.
- Previous intraocular surgery in the operative eye.
- Hearing or intellectual impairment preventing adequate cooperation.
About this study
Official title: A Randomized Controlled Trial Comparing the Accuracy of the Postoperative Vision Prediction Model for Cataract Surgery (OCT-PRO) With Clinicians' Predictions of Surgical Outcomes
Cataract is the leading cause of blindness worldwide, yet 5-20% of patients fail to achieve satisfactory visual recovery after surgery. Current methods for predicting postoperative visual acuity lack accuracy, particularly in patients with co-morbid fundus diseases. The OCT-PRO model, developed by our team, uses artificial intelligence (AI) to integrate optical coherence tomography (OCT) images and clinical data to forecast surgical outcomes. This multi-center, randomized, single-blind trial aims to compare the predictive accuracy of OCT-PRO-assisted predictions versus standard clinician predictions. A total of 534 participants will be randomized 1:1 to either the experimental group (OCT-PRO-assisted prediction) or the control group (routine care). The primary outcome is the mean absolute error (MAE) between predicted and actual postoperative best-corrected visual acuity (BCVA). Secondary outcomes include patient satisfaction, informed decision-making scores, and clinician acceptance of the AI tool. This study will provide high-level evidence on the clinical utility of AI in optimizing cataract surgical decision-making and patient communication.
Read the detailed description
Cataract is the leading cause of blindness worldwide, yet 5-20% of patients fail to achieve satisfactory visual recovery after surgery. Accurate preoperative prediction remains challenging, particularly for eyes with co-morbid retinal pathologies, as current methods relying on clinician experience and traditional tests (e.g., laser interferometry) often lack reproducibility. Although AI models like OCT-PRO show promise, prospective RCT evidence comparing their accuracy against clinicians is lacking.
This multi-center, randomized, assessor-blinded trial will enroll 534 adults scheduled for cataract surgery. Participants are allocated 1:1 to either the Experimental Group or the Control Group via centralized randomization. In the Experimental Group, clinicians use the OCT-PRO model-integrating OCT images and clinical data-to obtain a predicted postoperative BCVA. Physicians may confirm or adjust this prediction, and the final value is communicated to patients during preoperative counseling. The Control Group receives standard care, where predictions are based solely on conventional clinical assessments without AI assistance. Outcome assessors will be blinded to group allocation.
The primary endpoint is the Mean Absolute Error (MAE) between predicted and actual postoperative BCVA. Secondary endpoints include patient-reported outcomes (expectations, informed choice, satisfaction), clinician acceptance of the model, and correlation analyses. Analysis will follow the Intention-to-Treat principle. This study aims to provide high-level evidence on integrating AI into clinical workflows to enhance prognostic accuracy and optimize shared decision-making in cataract surgery.
- Conditions
- Cataract
- Being tested
- OCT-PRO prediction model, Routine Preoperative Counseling
- Keywords
- Cataract, Post-operative prognosis, Artificial intelligence, Multimodal data fusion, Clinical decision support
- Medical subject headings
- Cataract
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
OCT-PRO prediction modelDevice
The OCT-PRO model integrates optical coherence tomography (OCT) images and clinical data to predict postoperative best-corrected visual acuity (BCVA). In the experimental group, clinicians input preoperative data into the model, confirm or adjust the prediction, and communicate the final value to patients during preoperative counseling.
Routine Preoperative CounselingBehavioural
Standard preoperative communication based on clinical experience and conventional examinations without AI assistance.
| Study group | Role | What they receive |
|---|---|---|
| OCT-PRO Assisted Prediction | Experimental | Clinicians input preoperative OCT images and clinical data into the OCT-PRO model to generate a predicted postoperative BCVA. Physicians may confirm or adjust this AI prediction to determine a final value. This final prediction value is then communicated to the patient as supplementary information during routine preoperative counseling. Receives: Device: OCT-PRO prediction model |
| Routine Clinical Prediction | Active comparator | Clinicians perform standard preoperative assessments based on clinical experience and examination results. Predictions of postoperative visual acuity are made solely by physician judgment without AI assistance. Patients receive routine preoperative counseling regarding surgical risks and expected outcomes. Receives: Behavioral: Routine Preoperative Counseling |
What is being measured
The primary outcome is the main question the study is designed to answer.
The Mean Absolute Error (MAE) between the predicted postoperative BCVA and the actual measured BCVA at 1 month post-surgery.
Measured: Baseline, 1 month post-surgery
Secondary outcomes (6)
Patient-reported consistency between surgical outcomes and expectations
Patient-perceived alignment between actual surgical outcomes and preoperative expectations, measured via a validated questionnaire using a 5-point Likert scale (strongly disagree to strongly agree).
Measured: Baseline, 1 month post-surgery
Patient-reported psychological impact of preoperative prognostic disclosure
Psychological response to receiving preoperative prognostic information (e.g., anxiety, reassurance), measured via a validated questionnaire using a 5-point Likert scale (strongly disagree to strongly agree).
Measured: Baseline, 1 month post-surgery
Patient-reported willingness to recommend prognostic information to others
Willingness to recommend cataract surgery prognostic information to others (e.g., family or friends), measured via a validated questionnaire using a 5-point Likert scale (strongly disagree to strongly agree).
Measured: Baseline, 1 month post-surgery
Patient-reported satisfaction with healthcare services
Patient satisfaction with overall healthcare services received during cataract surgery (e.g., communication, care quality, information clarity), measured via a validated questionnaire using a 5-point Likert scale (strongly disagree to strongly agree).
Measured: Baseline, 1 month post-surgery
Clinician-reported outcomes assessing the satisfaction of using OCT-PRO in cataract treatment decision-making
Clinician-reported satisfaction with incorporating OCT-PRO into preoperative decision-making (e.g., ease of use, confidence in prediction, communication aid), measured via a validated questionnaire using a 5-point Likert scale (strongly disagree to strongly agree).
Measured: Baseline, 1 month post-surgery
Correlation coefficients (Pearson/Spearman) between predicted and actual BCVA in both groups
Measured: From before surgery to 1 month (±1 week) post-surgery
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 Jul 2026
- Study start20 Jul 2026 (estimated)
- Primary completion31 Oct 2026 (estimated)
- Study completion31 Dec 2026 (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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 534 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Zhongshan Ophthalmic Center, Sun Yat-sen University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Haotian Lin, Zhongshan Ophthalmic Center, Sun Yat-sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07713069
- Sponsor's protocol ID
- 2026KYPJ063
- Status last verified
- Jul 2026
- First submitted
- 14 Jul 2026
- Last updated
- 20 Jul 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 20 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Randomized Controlled Trial Comparing the Accuracy of the Postoperative Vision Prediction Model for Cataract Surgery (OCT-PRO) With Clinicians' Predictions of Surgical Outcomes
- ClinicalTrials.gov ID
- NCT07713069
- Acronym / sponsor ID
- 2026KYPJ063
- Conditions
- Cataract
- Keywords
- Cataract, Post-operative prognosis, Artificial intelligence, Multimodal data fusion, Clinical decision support
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 534 participants (estimated)
- Lead sponsor
- Zhongshan Ophthalmic Center, Sun Yat-sen UniversityUniversity, hospital or other
- Study start
- 20 Jul 2026 (estimated)
- Primary completion
- 31 Oct 2026 (estimated)
- Study completion
- 31 Dec 2026 (estimated)
- First posted
- 20 Jul 2026 (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 the leading cause of blindness worldwide, yet 5-20% of patients fail to achieve satisfactory visual recovery after surgery. Current methods for predicting postoperative visual acuity lack accuracy, particularly in patients with co-morbid fundus diseases. The OCT-PRO model, developed by our team, uses artificial intelligence (AI) to integrate optical coherence tomography (OCT) images and clinical data to forecast surgical outcomes. This multi-center, randomized, single-blind trial aims to compare the predictive accuracy of OCT-PRO-assisted predictions versus standard clinician predictions. A total of 534 participants will be randomized 1:1 to either the experimental group (OCT-PRO-assisted prediction) or the control group (routine care). The primary outcome is the mean absolute error (MAE) between predicted and actual postoperative best-corrected visual acuity (BCVA). Secondary outcomes include patient satisfaction, informed decision-making scores, and clinician acceptance of the AI tool. This study will provide high-level evidence on the clinical utility of AI in optimizing cataract surgical decision-making and patient communication.
Detailed description
Cataract is the leading cause of blindness worldwide, yet 5-20% of patients fail to achieve satisfactory visual recovery after surgery. Accurate preoperative prediction remains challenging, particularly for eyes with co-morbid retinal pathologies, as current methods relying on clinician experience and traditional tests (e.g., laser interferometry) often lack reproducibility. Although AI models like OCT-PRO show promise, prospective RCT evidence comparing their accuracy against clinicians is lacking.
This multi-center, randomized, assessor-blinded trial will enroll 534 adults scheduled for cataract surgery. Participants are allocated 1:1 to either the Experimental Group or the Control Group via centralized randomization. In the Experimental Group, clinicians use the OCT-PRO model-integrating OCT images and clinical data-to obtain a predicted postoperative BCVA. Physicians may confirm or adjust this prediction, and the final value is communicated to patients during preoperative counseling. The Control Group receives standard care, where predictions are based solely on conventional clinical assessments without AI assistance. Outcome assessors will be blinded to group allocation.
The primary endpoint is the Mean Absolute Error (MAE) between predicted and actual postoperative BCVA. Secondary endpoints include patient-reported outcomes (expectations, informed choice, satisfaction), clinician acceptance of the model, and correlation analyses. Analysis will follow the Intention-to-Treat principle. This study aims to provide high-level evidence on integrating AI into clinical workflows to enhance prognostic accuracy and optimize shared decision-making in cataract surgery.
Study design
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| OCT-PRO Assisted Prediction | Experimental | Device: OCT-PRO prediction modelClinicians input preoperative OCT images and clinical data into the OCT-PRO model to generate a predicted postoperative BCVA. Physicians may confirm or adjust this AI prediction to determine a final value. This final prediction value is then communicated to the patient as supplementary information during routine preoperative counseling. |
| Routine Clinical Prediction | Active comparator | Behavioral: Routine Preoperative CounselingClinicians perform standard preoperative assessments based on clinical experience and examination results. Predictions of postoperative visual acuity are made solely by physician judgment without AI assistance. Patients receive routine preoperative counseling regarding surgical risks and expected outcomes. |
Who may qualify
- Ages
- 18 to 90 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- Age ≥18 years scheduled to undergo phacoemulsification with intraocular lens (Phaco+IOL) implantation.
- Outpatient diagnosis of senile, complicated, or metabolic cataract.
- For bilateral cataracts, the eye with more advanced disease will be included.
Key exclusion criteria (4 listed)
- History of amblyopia or neuro-ophthalmic disease in the operative eye.
- Poor-quality OCT images precluding clear visualization of fundus structures.
- Previous intraocular surgery in the operative eye.
- Hearing or intellectual impairment preventing adequate cooperation.
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 |
|---|---|---|
| OCT-PRO prediction modelThe OCT-PRO model integrates optical coherence tomography (OCT) images and clinical data to predict postoperative best-corrected visual acuity (BCVA). In the experimental group, clinicians input preoperative data into the model, confirm or adjust the prediction, and communicate the final value to patients during preoperative counseling. | Device | OCT-PRO Assisted Prediction |
| Routine Preoperative CounselingStandard preoperative communication based on clinical experience and conventional examinations without AI assistance. | Behavioural | Routine Clinical Prediction |
Primary outcome
The Mean Absolute Error (MAE) between the predicted postoperative BCVA and the actual measured BCVA at 1 month post-surgery.
Time frame: Baseline, 1 month post-surgery
6 secondary outcomes
Patient-reported consistency between surgical outcomes and expectations
Time frame: Baseline, 1 month post-surgery
Patient-reported psychological impact of preoperative prognostic disclosure
Time frame: Baseline, 1 month post-surgery
Patient-reported willingness to recommend prognostic information to others
Time frame: Baseline, 1 month post-surgery
Patient-reported satisfaction with healthcare services
Time frame: Baseline, 1 month post-surgery
Clinician-reported outcomes assessing the satisfaction of using OCT-PRO in cataract treatment decision-making
Time frame: Baseline, 1 month post-surgery
Correlation coefficients (Pearson/Spearman) between predicted and actual BCVA in both groups
Time frame: From before surgery to 1 month (±1 week) post-surgery
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 20 Jul 2026.
- Individual participant dataSharing planned: No
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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.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
A study testing OCT-PRO prediction model and Routine Preoperative Counseling in people with Cataract, run by Zhongshan Ophthalmic Center, Sun Yat-sen University.
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 3 inclusion and 4 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 20 Jul 2026. 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?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The Mean Absolute Error (MAE) between the predicted postoperative BCVA and the actual measured BCVA at 1 month post-surgery. (time frame: Baseline, 1 month post-surgery). 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 20 Jul 2026.