Application and Validation of a Smartphone-based Deep Learning System for Oral Potentially Malignant Disorders and Oral Cancer Screening
NCT ID: NCT06862414
Last Updated: 2025-03-06
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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NOT_YET_RECRUITING
NA
954 participants
INTERVENTIONAL
2025-03-31
2025-12-31
Brief Summary
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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.
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Detailed Description
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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.
Conditions
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Study Design
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RANDOMIZED
PARALLEL
SCREENING
NONE
Study Groups
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A
Deep learning system
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).
B
Board-certified dentist with deep learning system
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).
C
non-certified health providers (general practitioners) with deep learning system
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).
Interventions
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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).
Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
* Unable to cooperate for the assessment
19 Years
ALL
Yes
Sponsors
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National Taiwan University Hospital
OTHER
Responsible Party
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Principal Investigators
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Shao-Yi Cheng, MD, MSc, DrPH
Role: STUDY_CHAIR
Department of Family Medicine, College of Medicine and Hospital, National Taiwan University
Locations
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Department of Family Medicine, National Taiwan University Hospital
Taipei, , Taiwan
Countries
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Central Contacts
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Facility Contacts
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References
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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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Other Identifiers
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202204032RIND
Identifier Type: -
Identifier Source: org_study_id
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