CataractNCT03623971
Validation of a Universal Cataract Intelligence Platform
A study testing Cataract AI agent in people with Cataract and Artificial Intelligence, run by Sun Yat-sen University.
Trial summary
This study established and validated a universal artificial intelligence (AI) platform for collaborative management of cataracts involving multi-level clinical scenarios and explored an AI-based medical referral pattern to improve collaborative efficiency and resource coverage.The datasets were labeled using a… Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Cataract, Artificial Intelligence
- What is being tested?
- Cataract AI agent
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 500 participants
- Where
- No sites listed yet
- Study dates
- 1 Jan 2013to 1 Jun 2017
- Phase
- Interventional
- Are there results?
- None posted in the registry
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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Who may be able to join
- Patients who underwent ophthalmic examination of the eye and recorded their ocular information in the primary healthcare center.
Who cannot join
- The patients who cannot cooperate with the examinations.
About this study
Official title: Validation of the Utility of a Universal Cataract Intelligence Platform
This study established and validated a universal artificial intelligence (AI) platform for collaborative management of cataracts involving multi-level clinical scenarios and explored an AI-based medical referral pattern to improve collaborative efficiency and resource coverage.The datasets were labeled using a three-step strategy: (1) categorize slit lamp photographs into four separate capture modes; (2) diagnose each photograph as a normal lens, cataract or a postoperative eye; and (3) based on etiology and severity, further classify each diagnosed photograph for a management strategy of referral or follow-up. A deep residual convolutional neural network (CS-ResCNN) was used for the image classification task. Moreover, we integrated the cataract AI agent with a real-world multi-level referral pattern involving self-monitoring at home, primary healthcare, and specialized hospital services.
- Conditions
- Cataract, Artificial Intelligence
- Being tested
- Cataract AI agent
- Keywords
- Cataract, Artificial Intelligence, Medical Referral Pattern
- Medical subject headings
- Cataract
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Cataract AI agentDevice
An artificial intelligence to make comprehensive evaluation and treatment decision of different types of cataracts.
| Study group | Role | What they receive |
|---|---|---|
| Artificial Intelligence | Experimental | A universal diagnostic system. An artificial intelligence to make comprehensive evaluation and treatment decision of cataract. Receives: Device: Cataract AI agent |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic accuracy of the cataract AI agent
AUC: area under the receiver operating curve; accuracy (ACC) = (TP + TN) / (TP + TN + FP + FN); sensitivity (SEN) = TP / (TP + FN); specificity (SPE) = TN / (TN + FP); TP = true positive; TN = true negative; FP = false positive; FN = false negative.
Measured: 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Aug 2018
- Study start1 Jan 2013
- Primary completion1 Jun 2017
- Study completion1 Jun 2017
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.
- More trials run by Sun Yat-sen UniversityThe sponsor's other registered studies.
- 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
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 500 participants (actual)
Sponsor and investigators
- Lead sponsor
- Sun Yat-sen University
- Sponsor type
- University, hospital or other
- Collaborators
- Xidian University
- Responsible party
- Principal investigator, Haotian Lin, Sun Yat-sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03623971
- Sponsor's protocol ID
- CCPMOH2018- China7
- Status last verified
- Aug 2018
- First submitted
- 7 Aug 2018
- Last updated
- 9 Aug 2018
- 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 9 Aug 2018. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Validation of the Utility of a Universal Cataract Intelligence Platform
- ClinicalTrials.gov ID
- NCT03623971
- Acronym / sponsor ID
- CCPMOH2018- China7
- Conditions
- Cataract, Artificial Intelligence
- Keywords
- Cataract, Artificial Intelligence, Medical Referral Pattern
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 500 participants (actual)
- Lead sponsor
- Sun Yat-sen UniversityUniversity, hospital or other
- Collaborators
- Xidian University
- Study start
- 1 Jan 2013 (actual)
- Primary completion
- 1 Jun 2017 (actual)
- Study completion
- 1 Jun 2017 (actual)
- First posted
- 9 Aug 2018 (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.
This study established and validated a universal artificial intelligence (AI) platform for collaborative management of cataracts involving multi-level clinical scenarios and explored an AI-based medical referral pattern to improve collaborative efficiency and resource coverage.The datasets were labeled using a three-step strategy: (1) categorize slit lamp photographs into four separate capture modes; (2) diagnose each photograph as a normal lens, cataract or a postoperative eye; and (3) based on etiology and severity, further classify each diagnosed photograph for a management strategy of referral or follow-up. A deep residual convolutional neural network (CS-ResCNN) was used for the image classification task. Moreover, we integrated the cataract AI agent with a real-world multi-level referral pattern involving self-monitoring at home, primary healthcare, and specialized hospital services.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Artificial Intelligence | Experimental | Device: Cataract AI agentA universal diagnostic system. An artificial intelligence to make comprehensive evaluation and treatment decision of cataract. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (1 listed)
- Patients who underwent ophthalmic examination of the eye and recorded their ocular information in the primary healthcare center.
Key exclusion criteria (1 listed)
- The patients who cannot cooperate with the examinations.
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 |
|---|---|---|
| Cataract AI agentAn artificial intelligence to make comprehensive evaluation and treatment decision of different types of cataracts. | Device | Artificial Intelligence |
Primary outcome
Diagnostic accuracy of the cataract AI agent
Time frame: 6 months
AUC: area under the receiver operating curve; accuracy (ACC) = (TP + TN) / (TP + TN + FP + FN); sensitivity (SEN) = TP / (TP + FN); specificity (SPE) = TN / (TN + FP); TP = true positive; TN = true negative; FP = false positive; FN = false negative.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 9 Aug 2018.
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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. This study is not recruiting at the moment.
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 Cataract AI agent in people with Cataract and Artificial Intelligence, run by 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 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: finished, as of the sponsor's last update on 9 Aug 2018. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
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?
Diagnostic accuracy of the cataract AI agent (time frame: 6 months). 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 9 Aug 2018.