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Establishment of diagnosis and treatment system of pulpal and periapical diseases based on deep learning oral CBCT

Establishment of diagnosis and treatment system of pulpal and periapical diseases based on deep learning oral CBCT

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2300077078
Enrollment
Unknown
Registered
2023-10-27
Start date
2023-12-01
Completion date
Unknown
Last updated
2023-10-30

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

Conditions

pulp periapical disease

Interventions

Gold Standard:Three endodontic clinicians with 10 years of clinical experience observed CBCT images and gave the diagnosis.
Index test: automatic diagnosis of pulpal periapical lesions by deep learning neural network

Sponsors

Affiliated Stomatological Hospital of Sun Yat-sen University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
10 Years to 75 Years

Inclusion criteria

Inclusion criteria: Patients aged from 10 to 75 years old, which were admitted to the Department of Conservative Dentistry and Endodontics, Hospital of Stomatology, Sun Yat-sen University between January 1, 2019 and December 31, 2022 and whose CBCT images were clear without severe metal artifacts and diagnosed as pulp periapical lesions, are included in this study.

Exclusion criteria

Exclusion criteria: Patient whose CBCT images shows poor image quality of the root canal system and periapical region, and repeated scans of the same patient.

Design outcomes

Primary

MeasureTime frame
accuracy;sensitivity;specificity;AUC;—

Countries

China

Contacts

Public ContactDu Yu

Affiliated Stomatological Hospital of Sun Yat-sen University

duyu3@mail.sysu.edu.cn+86 137 9804 0097

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 6, 2026