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

NCT06862414 | Not Yet Recruiting

• 1 other identifier: 202204032RIND
• Study Type: interventional
• Target: 954
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Cancer Screening; Oral Cancer; Oral Potentially Malignant Disorders

Keywords

Deep learning systems; Artificial Intelligence

Outcome Measures

Primary Outcomes (1)

• Effectiveness and accuracy

  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 Outcomes (1)

• Questionnaire

  Within 6 months

Study Arms (3)

A

EXPERIMENTAL

Deep learning system

Device: Smartphone-based deep learning system

B

ACTIVE COMPARATOR

Board-certified dentist with deep learning system

Device: Smartphone-based deep learning system

C

ACTIVE COMPARATOR

non-certified health providers (general practitioners) with deep learning system

Device: Smartphone-based deep learning system

Interventions

Smartphone-based deep learning system DEVICE

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).

A; B; C

Eligibility Criteria

• Age: 19 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

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

You may not qualify if:

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

Sponsors & Collaborators

• National Taiwan University Hospital: lead

Study Sites (1)

Department of Family Medicine, National Taiwan University Hospital

Taipei, 100229, Taiwan

Location

Related Publications (11)

• Hsu Y, Chou CY, Huang YC, Liu YC, Lin YL, Zhong ZP, Liao JK, Lee JC, Chen HY, Lee JJ, Chen SJ. Oral mucosal lesions triage via YOLOv7 models. J Formos Med Assoc. 2025 Jul;124(7):621-627. doi: 10.1016/j.jfma.2024.07.010. Epub 2024 Jul 12.

  PMID: 39003230 BACKGROUND

• Tanriver G, Soluk Tekkesin M, Ergen O. Automated Detection and Classification of Oral Lesions Using Deep Learning to Detect Oral Potentially Malignant Disorders. Cancers (Basel). 2021 Jun 2;13(11):2766. doi: 10.3390/cancers13112766.

  PMID: 34199471 BACKGROUND

• Hegde S, Ajila V, Zhu W, Zeng C. Artificial intelligence in early diagnosis and prevention of oral cancer. Asia Pac J Oncol Nurs. 2022 Aug 24;9(12):100133. doi: 10.1016/j.apjon.2022.100133. eCollection 2022 Dec.

  PMID: 36389623 BACKGROUND

• Ng SW, Syamim Syed Mohd Sobri SN, Zain RB, Kallarakkal TG, Amtha R, Wiranata Wong FA, Rimal J, Durward C, Chea C, Jayasinghe RD, Vatanasapt P, Saleha Binti Ibrahim Tamin N, Cheng LC, Mazlipah Binti Ismail S, Tepirou C, Ariff Bin Abdul Rahman Z, Rajendran S, Kanapathy J, Liew CS, Cheong SC. Barriers to early detection and management of oral cancer in the Asia Pacific region. J Health Serv Res Policy. 2022 Apr;27(2):133-140. doi: 10.1177/13558196211053110. Epub 2022 Jan 22.

  PMID: 35068209 BACKGROUND

• Khanagar SB, Naik S, Al Kheraif AA, Vishwanathaiah S, Maganur PC, Alhazmi Y, Mushtaq S, Sarode SC, Sarode GS, Zanza A, Testarelli L, Patil S. Application and Performance of Artificial Intelligence Technology in Oral Cancer Diagnosis and Prediction of Prognosis: A Systematic Review. Diagnostics (Basel). 2021 May 31;11(6):1004. doi: 10.3390/diagnostics11061004.

  PMID: 34072804 BACKGROUND

• Gigliotti J, Madathil S, Makhoul N. Delays in oral cavity cancer. Int J Oral Maxillofac Surg. 2019 Sep;48(9):1131-1137. doi: 10.1016/j.ijom.2019.02.015. Epub 2019 Mar 13.

  PMID: 30878273 BACKGROUND

• Peacock ZS, Pogrel MA, Schmidt BL. Exploring the reasons for delay in treatment of oral cancer. J Am Dent Assoc. 2008 Oct;139(10):1346-52. doi: 10.14219/jada.archive.2008.0046.

  PMID: 18832270 BACKGROUND

• R VC, C R, Sridhar P, Ramachandra C, Kumar M. Barriers related to Oral Cancer Screening, Diagnosis and Treatment in Karnataka, India. Gulf J Oncolog. 2023 Sep;1(43):19-24.

  PMID: 37732523 BACKGROUND

• Gonzalez-Moles MA, Aguilar-Ruiz M, Ramos-Garcia P. Challenges in the Early Diagnosis of Oral Cancer, Evidence Gaps and Strategies for Improvement: A Scoping Review of Systematic Reviews. Cancers (Basel). 2022 Oct 10;14(19):4967. doi: 10.3390/cancers14194967.

  PMID: 36230890 BACKGROUND

• Warnakulasuriya S, Kujan O, Aguirre-Urizar JM, Bagan JV, Gonzalez-Moles MA, Kerr AR, Lodi G, Mello FW, Monteiro L, Ogden GR, Sloan P, Johnson NW. Oral potentially malignant disorders: A consensus report from an international seminar on nomenclature and classification, convened by the WHO Collaborating Centre for Oral Cancer. Oral Dis. 2021 Nov;27(8):1862-1880. doi: 10.1111/odi.13704. Epub 2020 Nov 26.

  PMID: 33128420 BACKGROUND

• Stathopoulos P, Smith WP. Analysis of Survival Rates Following Primary Surgery of 178 Consecutive Patients with Oral Cancer in a Large District General Hospital. J Maxillofac Oral Surg. 2017 Jun;16(2):158-163. doi: 10.1007/s12663-016-0937-z. Epub 2016 Jul 8.

  PMID: 28439154 BACKGROUND

MeSH Terms

Conditions

Mouth Neoplasms

Condition Hierarchy (Ancestors)

Head and Neck Neoplasms; Neoplasms by Site; Neoplasms; Mouth Diseases; Stomatognathic Diseases

Study Officials

• Shao-Yi Cheng, MD, MSc, DrPH

  Department of Family Medicine, College of Medicine and Hospital, National Taiwan University

  STUDY CHAIR

Central Study Contacts

Shao-Yi Cheng, MD, MSc, DrPH

CONTACT

+886-2312-3456; scheng2140@gmail.com

I Ann Hsiao, MD

CONTACT

+886-2312-3456; iamiannhsiao@gmail.com

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: SCREENING
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Model Details: An open, three-arm, randomized controlled trial will be done in a medical center in Northern Taiwan between Jan 2025 to Dec 2025

Study Record Dates

• First Submitted: March 2, 2025
• First Posted: March 6, 2025
• Study Start: March 1, 2025
• Primary Completion: October 1, 2025
• Study Completion: December 1, 2025
• Last Updated: March 6, 2025

Record last verified: 2025-02

Locations

TW (1)