CataractNCT07183891
Performance of Large Language Models for Structured Recognition and Refractive Prediction
An observational study of Cataract, run by Jin Yang at 1 site in China.
Trial summary
We conducted a single-center, retrospective observational study to evaluate large language models (ChatGPT 4o, GPT-5, DeepSeek) for automated interpretation of de-identified IOLMaster 700 reports provided as raster images. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Cataract
- How many people
- 100 participants
- Where
- 1 siteChina
- Study dates
- 1 Aug 2025to 31 Dec 2035
- 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 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: cataract patients who had undergone phacoemulsification and implantation of an AcrySof IQ Toric monofocal intraocular lens
Who may be able to join
- -postoperative corrected distance visual acuity (CDVA) of 0.10 logMAR or better -an absolute IOL rotational stability of less than 10∘ at the 1-month follow-up examination
Who cannot join
- incomplete biometric data on the examination report;
- a history of previous ocular surgery or ocular trauma
- the occurrence of intraoperative complications, such as an anterior capsular tear or posterior capsular rupture
- the development of significant postoperative complications, including but not limited to severe intraocular infection or inadequate pupillary dilation.
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
Eye and ENT hospital of Fudan University
Shanghai, Shanghai Municipality, 200000
Xuanqiao Lin, 15088920668, 1532483480@qq.com
About this study
Official title: Head-to-Head Evaluation of ChatGPT 4o, GPT-5, and DeepSeek for Structured Extraction, Toric IOL Recommendation, and Refractive Prediction
We conducted a single-center, retrospective observational study to evaluate large language models (ChatGPT 4o, GPT-5, DeepSeek) for automated interpretation of de-identified IOLMaster 700 reports provided as raster images. Models produced structured biometric extraction, toric IOL recommendation, and refractive predictions (sphere, cylinder, axis). Primary outcomes included parameter-level agreement and refractive error metrics; secondary outcomes included decision-support performance for toric IOL selection and agreement on ordered T-codes. No clinical intervention was performed.
Read the detailed description
This study compares three large language models accessed in their native configurations, without fine-tuning or external tools. For each examination, the original IOLMaster 700 report image was supplied without manual annotation or pre-processing. A standardized instruction required: (i) structured extraction of AL, ACD, LT, WTW, K1/K2 and axes, ΔK, TK1/TK2 and axes, and ΔTK; (ii) binary toric candidacy and T-code according to institutional ALCON mapping; and (iii) refractive recommendations (sphere, cylinder, implantation axis). Each model generated three independent outputs per case. De-identification and IRB oversight (waiver of consent) were implemented according to institutional policy. The unit of enrollment is participants (n=54), with outcomes analyzed per eye (162 eyes) and per model generation where applicable.
- Conditions
- Cataract
- Keywords
- cataract, toric IOL, astigmatism
- Medical subject headings
- Cataract, Astigmatism
What is being measured
The primary outcome is the main question the study is designed to answer.
Refractive prediction error for sphere
Mean absolute error (MAE, diopters) of model-predicted sphere versus clinical reference
Measured: At index examination
Cohen's kappa with 95% CIs between model
Cohen's kappa with 95% CIs between model outputs and clinician-validated reference for per-parameter
Measured: At index examination (single time point)
Secondary outcomes (2)
Cylinder prediction error
Mean absolute error (MAE, diopters) of model-predicted Cylinder
Measured: At index examination
Axis prediction error
Mean absolute error (MAE, diopters) of model-predicted Axis
Measured: At index examination
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Sep 2025
- Study start1 Aug 2025
- Primary completion31 Dec 2030 (estimated)
- Study completion31 Dec 2035 (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
- Case only
- Time perspective
- Retrospective
- Enrolment
- 100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Jin Yang
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Jin Yang, Eye & ENT Hospital of Fudan University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07183891
- Sponsor's protocol ID
- Totic-2025-001
- Status last verified
- Sep 2025
- First submitted
- 14 Sep 2025
- Last updated
- 19 Sep 2025
- Results posted
- No
- Data monitoring committee
- No
- 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 19 Sep 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Head-to-Head Evaluation of ChatGPT 4o, GPT-5, and DeepSeek for Structured Extraction, Toric IOL Recommendation, and Refractive Prediction
- ClinicalTrials.gov ID
- NCT07183891
- Acronym / sponsor ID
- Totic-2025-001
- Conditions
- Cataract
- Keywords
- cataract, toric IOL, astigmatism
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- Jin YangUniversity, hospital or other
- Study start
- 1 Aug 2025 (actual)
- Primary completion
- 31 Dec 2030 (estimated)
- Study completion
- 31 Dec 2035 (estimated)
- First posted
- 19 Sep 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.
We conducted a single-center, retrospective observational study to evaluate large language models (ChatGPT 4o, GPT-5, DeepSeek) for automated interpretation of de-identified IOLMaster 700 reports provided as raster images. Models produced structured biometric extraction, toric IOL recommendation, and refractive predictions (sphere, cylinder, axis). Primary outcomes included parameter-level agreement and refractive error metrics; secondary outcomes included decision-support performance for toric IOL selection and agreement on ordered T-codes. No clinical intervention was performed.
Detailed description
This study compares three large language models accessed in their native configurations, without fine-tuning or external tools. For each examination, the original IOLMaster 700 report image was supplied without manual annotation or pre-processing. A standardized instruction required: (i) structured extraction of AL, ACD, LT, WTW, K1/K2 and axes, ΔK, TK1/TK2 and axes, and ΔTK; (ii) binary toric candidacy and T-code according to institutional ALCON mapping; and (iii) refractive recommendations (sphere, cylinder, implantation axis). Each model generated three independent outputs per case. De-identification and IRB oversight (waiver of consent) were implemented according to institutional policy. The unit of enrollment is participants (n=54), with outcomes analyzed per eye (162 eyes) and per model generation where applicable.
Study design
- Observational model
- Case only
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- cataract patients who had undergone phacoemulsification and implantation of an AcrySof IQ Toric monofocal intraocular lens
Key inclusion criteria (1 listed)
- -postoperative corrected distance visual acuity (CDVA) of 0.10 logMAR or better -an absolute IOL rotational stability of less than 10∘ at the 1-month follow-up examination
Key exclusion criteria (4 listed)
- incomplete biometric data on the examination report;
- a history of previous ocular surgery or ocular trauma
- the occurrence of intraoperative complications, such as an anterior capsular tear or posterior capsular rupture
- the development of significant postoperative complications, including but not limited to severe intraocular infection or inadequate pupillary dilation.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Refractive prediction error for sphere
Time frame: At index examination
Mean absolute error (MAE, diopters) of model-predicted sphere versus clinical reference
Cohen's kappa with 95% CIs between model
Time frame: At index examination (single time point)
Cohen's kappa with 95% CIs between model outputs and clinician-validated reference for per-parameter
2 secondary outcomes
Cylinder prediction error
Time frame: At index examination
Axis prediction error
Time frame: At index examination
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 19 Sep 2025.
- 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.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Cataract, run by Jin Yang at 1 site in China.
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 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: looking for participants now, as of the sponsor's last update on 19 Sep 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
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 Xuanqiao Lin (+8615088920668, 1532483480@qq.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Refractive prediction error for sphere (time frame: At index examination). The study lists 2 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 19 Sep 2025; AzTrials last refreshed its copy on 29 Oct 2026.