Application and Validation of a Smartphone-based Deep Learning System for Oral Potentially Malignant Disorders and Oral Cancer Screening

March 2, 2025 updated by: National Taiwan University Hospital

Application and Validation of a Smartphone-based Deep Learning System for Oral Potentially Malignant Disorders (OPMD) and Oral Cancer Screening

The goal of this clinical trial is to learn if smartphone-based deep learning system works to accurately detect oral potentially malignant disorders and oral cancer in adults. It will also learn about if it is as effective as assessments conducted by dentists and non-certified health provider.

We expect that the deep learning system will have higher sensitivity in detecting oral potentially malignant disorders and oral cancer, where as the dentists and non-certified health providers will exhibit higher specificity in screening.

Participants will be grouped into three arms: deep learning system (arm A) or board-certified dentist with deep learning system (arm B) or non-certified health providers (general practitioners) with deep learning system (arm C).

Oral cancer risk factors, such as habits of smoking or having chewed betel nut or alcohol drinking, would be recorded by anonymous questionnaires.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Detailed Description

Background:

Oral cancer remains one of the leading causes of cancer-related deaths in Taiwan and worldwide. Artificial intelligence has the potential to improve oral cancer screening, enabling early detection by addressing healthcare access issues with high-quality solutions.

Objective:

To validate the smartphone-based deep learning system's accuracy in detecting oral potentially malignant disorders (OPMD) and oral cancer, while also demonstrating it is as effective as assessments conducted by dentists and non-certified health providers.

Methods:

Design, Setting and Participants: An open, three-arm, randomized controlled trial will be done in a medical center in Northern Taiwan between Jan 2025 to Dec 2025. The trial will include subjects aged 18 years or older who visit the cancer screening center for all kinds of screening. Oral cancer risk factors, such as habits of smoking or having chewed betel nut or alcohol drinking, would be recorded by anonymous questionnaires.

Interventions: Eligible subjects would be randomized in a 1:1:1 ratio using a computer-generated randomization algorithm to deep learning system (arm A) or board-certified dentist with deep learning system (arm B) or non-certified health providers (general practitioners) with deep learning system (arm C). The deep learning system in arm B and C would only be used for subsequent comparison and would not assist manual interpretation.

Main Outcomes and Measures: The primary outcome is the sensitivity and specificity for the three referral grades (benign (green), potentially malignant (yellow), and malignant (red)) by the deep learning system, dentists and non-certified health providers. The area under the curve (AUC) for each receiver operating characteristic (ROC) curve will also be calculated. The secondary outcome is subjects' feedback of comfortability during exam and the time needed for assessment.

Anticipated Results:

We hypothesize that deep learning systems will have higher sensitivity in detecting OPMD and oral cancer, whereas dentists and general practitioners will exhibit higher specificity in screening. The results could assist us in enhancing the oral cancer screening promotion process.

Study Type

Interventional

Enrollment (Estimated)

954

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

  • Name: Shao-Yi Cheng, MD, MSc, DrPH
  • Phone Number: 266823 +886-2312-3456
  • Email: scheng2140@gmail.com

Study Locations

  • Taiwan
      • Taipei, Taiwan, 100229
        • Department of Family Medicine, National Taiwan University Hospital
        • Contact:
        • Contact:
        • Contact:
          • Yi-Hsuan Lee, MD, MPH

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Description

Inclusion Criteria:

  • Adult patients (age ≥18) visiting cancer screening center

Exclusion Criteria:

  • Unable to cooperate to fully open mouth/ navigate tongue
  • Unable to cooperate for the assessment

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Screening
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: A
Deep learning system
Device: Smartphone-based deep learning system
The smartphone-based deep learning system was trained using a dataset of over 50,000 white-light macroscopic images collected between 2006 and 2013 to develop the YOLOv7 model. Lesions were categorized into three referral grades: benign (green), potentially malignant (yellow), and malignant (red).
Active Comparator: B
Board-certified dentist with deep learning system
Device: Smartphone-based deep learning system
The smartphone-based deep learning system was trained using a dataset of over 50,000 white-light macroscopic images collected between 2006 and 2013 to develop the YOLOv7 model. Lesions were categorized into three referral grades: benign (green), potentially malignant (yellow), and malignant (red).
Active Comparator: C
non-certified health providers (general practitioners) with deep learning system
Device: Smartphone-based deep learning system
The smartphone-based deep learning system was trained using a dataset of over 50,000 white-light macroscopic images collected between 2006 and 2013 to develop the YOLOv7 model. Lesions were categorized into three referral grades: benign (green), potentially malignant (yellow), and malignant (red).

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Effectiveness and accuracy
Time Frame: Within 6 months
The primary outcome is the sensitivity and specificity for the three referral grades (green, yellow and red) by the deep learning system, dentists and non-certified health providers. The area under the curve (AUC) for each receiver operating characteristic (ROC) curve will also be calculated.
Within 6 months

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Questionnaire
Time Frame: Within 6 months
The secondary outcome is subjects' feedback of comfortability during exam evaluated by the visual analog scale (VAS) (a score out of 10). The time needed for screening will also be recorded for the assessment of efficiency.
Within 6 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

National Taiwan University Hospital

Investigators

  • Study Chair: Shao-Yi Cheng, MD, MSc, DrPH, Department of Family Medicine, College of Medicine and Hospital, National Taiwan University

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

March 1, 2025

Primary Completion (Estimated)

October 1, 2025

Study Completion (Estimated)

December 1, 2025

Study Registration Dates

First Submitted

March 2, 2025

First Submitted That Met QC Criteria

March 2, 2025

First Posted (Actual)

March 25, 2025

Study Record Updates

Last Update Posted (Actual)

March 25, 2025

Last Update Submitted That Met QC Criteria

March 2, 2025

Last Verified

February 1, 2025

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 202204032RIND

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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