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NCT05342298: OCID
Assessment of Ovarian Cysts Using Machine Learning
trial testing prediction model in Ovarian Cyst in 1,000 participants. Status unknown.
1 June 2023
Quick facts
| Lead sponsor | Assiut University |
|---|---|
| Status | Status unknown |
| Study type | OBSERVATIONAL |
| Enrollment | 1,000 |
| Start date | 1 October 2022 |
| Primary completion | 1 June 2023 |
| Estimated completion | 1 September 2023 |
| Sites | 2 locations across Egypt |
Drugs / interventions tested
- prediction model
Conditions studied
- Ovarian Cyst — all drugs for Ovarian Cyst →
Sponsor
Assiut University
Who can join
Adults 15 to 80, female only, with Ovarian Cyst. Patients with the condition only — healthy volunteers not accepted.
Sponsor's own description
The study aims at creating a prediction model using machine learning algorithms that is capable of predicting malignant potential of ovarian cysts/masses based on patient characteristics, sonographic findings, and biochemical markers
Publications & conference data
No peer-reviewed publications indexed yet for this trial.
Verify or expand the search:
- PubMed search for NCT05342298
- Europe PMC full search
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- ESMO Meeting Library
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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 NCT05342298 (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 Assiut University
- Last refreshed: 23 August 2022
Drug Landscape aggregates and links these public records for informational use only. Always verify against the primary source before clinical or regulatory decisions. Canonical URL: https://druglandscape.com/trial/NCT05342298.
Primary sources · FDA · ClinicalTrials.gov · EMA · SEC EDGAR · ChEMBL · Wikidata · full sourcing