Deep Learning to Summarize Findings in Dental Panoramic Radiographs

Dental Diseases Clinical Trial

Official title:

Deep Learning to Summarize Findings in Dental Panoramic Radiographs

Clinical Trial Details — Status: Enrolling by invitation

Administrative data

• NCT number: NCT04894201
• Other study ID #: 202102018RINB
• Status: Enrolling by invitation
• Start date: May 7, 2021
• Est. completion date: May 2023

Study information

• Verified date: May 2021
• Source: National Taiwan University Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

In this work, the investigators study the application of artificial intelligence systems on dental panoramic images for dental findings. An artificial intelligence system will be learned on an publicly available panoramic image dataset, and test against the investigators' local patient cohort as external test data. The investigators hypothesize the performance would be similar, if not identical to on the public data, and that the investigators' AI system is generalizable.

Recruitment information / eligibility

• Status: Enrolling by invitation
• Enrollment: 1000
• Est. completion date: May 2023
• Est. primary completion date: December 2021
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 20 Years to 100 Years

Eligibility

Exclusion Criteria:

• patients under 20 or with primary teeth
• patients with non-removable metal accessory above neck, such as tongue ring, nose ring etc.
• patients with mandible or maxilla deformation

Related Conditions & MeSH terms

• Dental Diseases
• Dentist-Patient Relations
• Digital
• Radiography
• Stomatognathic Diseases

Intervention

Radiation:
• Dental Panoramic Radiographs
Exposure to dental panoramic radiographs

Locations

Country	Name	City	State
Taiwan	National Taiwan University Hospital	Taipei

Sponsors (1)

Lead Sponsor	Collaborator
National Taiwan University Hospital

Country where clinical trial is conducted

Taiwan, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Area Under Curve for Receiver Operating Characteristics of Clinical Findings	The investigators use the AI model to infer whether a clinical finding (out of the six type of findings the investigators are interested in) is present in an imaging study from the test set. The result is compared against expert annotation and evaluated for receiver operating characteristics over the whole set. The area under curve will then be calculated and averaged across six type of findings to represent the overall efficacy of the model on detecting findings from panoramic images. From a scale of zero to one, zero is the worst this metric can be and one is the best.	1 day