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Application of a Multimodal Deep Learning Prognostic Prediction Model Based on Whole-Slide Pathological Images and CBCT Images in Odontogenic Keratocyst and Ameloblastoma

Application of a Multimodal Deep Learning Prognostic Prediction Model Based on Whole-Slide Pathological Images and CBCT Images in Odontogenic Keratocyst and Ameloblastoma

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2600130706
Enrollment
Unknown
Registered
2026-08-24
Start date
2026-08-25
Completion date
Unknown
Last updated
2026-08-31

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Ameloblastoma

Interventions

Gold Standard:definitive postoperative histopathological diagnosis
Index test:None

Sponsors

Beijing Stomatological Hospital , Capital Medical University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
10 Years to 75 Years

Inclusion criteria

Inclusion criteria: 1.Age at presentation between 10 and 75 years, regardless of sex; 2.Complete clinical medical records; 3.Availability of preoperative jaw CBCT scan data with clear imaging and absence of artifacts; 4.Definitive postoperative histopathological diagnosis of odontogenic keratocyst or ameloblastoma; 5.Availability of follow-up imaging data for at least 6 months postoperatively;

Exclusion criteria

Exclusion criteria: 1.Nevoid basal cell carcinoma syndrome; 2.Extraosseous or peripheral ameloblastoma, metastatic ameloblastoma;

Design outcomes

Primary

MeasureTime frame
Accuracy of the multimodal deep learning prognosis prediction model;—

Countries

China

Contacts

Public ContactQin Lizheng

Beijing Stomatological Hospital , Capital Medical University

qinlizheng@aliyun.com+86 10 57099157

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Sep 19, 2026