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NCT04314180
Artificial Intelligence System for Assessing Image Quality of Slit-Lamp Images and Its Effects on Diagnosis
trial testing Taking slit-lamp images in Anterior Segment Disorders in 300 participants. Status unknown.
1 July 2020
Quick facts
| Lead sponsor | Sun Yat-sen University |
|---|---|
| Status | Status unknown |
| Study type | OBSERVATIONAL |
| Enrollment | 300 |
| Start date | 1 February 2020 |
| Primary completion | 1 July 2020 |
| Estimated completion | 1 July 2020 |
| Sites | 1 location across China |
Drugs / interventions tested
- Taking slit-lamp images
Conditions studied
- Anterior Segment Disorders — all drugs for Anterior Segment Disorders →
- Artificial Intelligence — all drugs for Artificial Intelligence →
Sponsor
Sun Yat-sen University
Who can join
Eligibility, any sex, with Anterior Segment Disorders or Artificial Intelligence. Patients with the condition only — healthy volunteers not accepted.
Sponsor's own description
Slit-lamp images are widely used in ophthalmology for the detection of cataract, keratopathy and other anterior segment disorders. In real-world practice, the quality of slit-lamp images can be unacceptable, which can undermine diagnostic accuracy and efficiency. Here, the researchers established and validated an artificial intelligence system to achieve automatic quality assessment of slit-lamp images upon capture. This system can also provide guidance to photographers according to the reasons for low quality.
Publications & conference data
No peer-reviewed publications indexed yet for this trial.
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Verify against primary sources
- ClinicalTrials.gov — authoritative US registry record
- WHO ICTRP — international registry index
- EU Clinical Trials Register
- Sponsor press releases (Google)
- Trial protocol + status: ClinicalTrials.gov NCT04314180 (US National Library of Medicine, public domain)
- Drug + disease cross-links: matched in real time against Drug Landscape's normalised drug + company + condition tables
- Sponsor: as reported to ClinicalTrials.gov by Sun Yat-sen University
- Last refreshed: 19 March 2020
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