Artificial Intelligence Versus Clinical Examination in White Spot Lesions Detection, Identification, And Scoring

NCT07639749 | Not Yet Recruiting

• 1 other identifier: AI in detecting dental caries
• Study Type: observational
• Target: 329
• Locations: 0 countries
• Sites: N/A

Brief Summary

The goal of this observational study is to compare the diagnostic accuracy of Clinical examination as a standard for detection, identification and scoring of White Spot Lesions Versus Artificial intelligence analysis of intraoral photographs. The photographs are examined by experienced dental professionals to maintain diagnostic accuracy. Machine learning models YOLO and Mask-RCNN will analyze these images in three phases: pre-analytical, analytical and post-analytical. A dataset of 329 labelled photographs, annotated by experts, is used to train these models. Data augmentation methods enhance model performance, and accuracy is assessed against clinical examination results to confirm reliability. The main question it aims to answer is: \- Is artificial intelligence analysis of intraoral photographs as accurate as clinical assessment in the detection, identification, and scoring of white spot lesions among adult Egyptian patients attending Cairo University Dental Hospital?

Conditions

White Spot Lesion of Tooth

Keywords

white spot lesions; dental; caries; intraoral; photography; diagnostic accuracy; artificial intelligence

Outcome Measures

Primary Outcomes (1)

• Artificial Intelligence diagnostic accuracy in White Spot Lesions Detection

  Baseline

Interventions

Artificial Intelligence models (YOLO and MASK-RCNN) OTHER

Machine learning model well be used for assessment of intraoral photographs for the detection, identification, and scoring of white spot lesions in teeth

Eligibility Criteria

• Age: 20 Years - 60 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64)
• Sampling Method: Non-Probability Sample

Study Population

Patients attending the Conservative Department of Cairo University Dental Clinic, aged from 20 to 60 years, presenting with white spot lesions of teeth, showing no signs or symptoms, demonstrating co-operation, and expressing interest in participating in the study will be considered eligible. Patients with orthodontic appliances or bridgework that could impact the clinical assessment process will be excluded.

You may qualify if:

• Adult patients aged 20 - 60 years
• Males or Females
• Patients with white spot lesions of teeth 4 - Co-operative patients with interest in participation in the study

You may not qualify if:

• Patients with orthodontic appliances or bridgework that might interfere with evaluation and assessment
• Patients with no white spot lesions
• Patients with systematic diseases that might affect participation
• Patients refusing to sign the informed consent or not willing to be part of the study

Sponsors & Collaborators

• Cairo University: lead

Related Publications (5)

• Albuhayri FS, Albshaier SJ, Dashti AI, Alrajhi JF, Alhamidy FK, Busuhail MA, Bujbarah FN, Rizq MK, Thubab NA, Takronni SA, Alharbi JI, Hakami AH, Aloufi HS, Mathar MI. The Expanding Role of Artificial Intelligence in Dentistry: A Cross-Specialty Chairside Perspective. Cureus. 2025 Dec 4;17(12):e98449. doi: 10.7759/cureus.98449. eCollection 2025 Dec.

  PMID: 41487724 BACKGROUND

• Caldwell J, Parekh K, Crowther B, Gohel C, Pileggi R, Garcia AI, Ghorbanifarajzadeh M, Dolan TA, Gohel A. Performance evaluation of AI-based caries detection technology and its educational training module: a dual-phase investigation. Front Dent Med. 2026 Jan 29;6:1741855. doi: 10.3389/fdmed.2025.1741855. eCollection 2025.

  PMID: 41696690 BACKGROUND

• Abbott LP, Saikia A, Anthonappa RP. ARTIFICIAL INTELLIGENCE PLATFORMS IN DENTAL CARIES DETECTION: A SYSTEMATIC REVIEW AND META-ANALYSIS. J Evid Based Dent Pract. 2025 Mar;25(1):102077. doi: 10.1016/j.jebdp.2024.102077. Epub 2024 Dec 12.

  PMID: 39947783 BACKGROUND

• Noro LRA, Manzanares Cespedes MC. Artificial intelligence and oral photography: an approach to the epidemiology of dental caries. Rev Saude Publica. 2026 Jan 12;59:e53. doi: 10.11606/s1518-8787.2025059006910. eCollection 2026.

  PMID: 41538677 BACKGROUND

• Chung HM, Ke J, Zhang M, Kong L, Zheng J, Xiang L. Tooth-to-white spot lesion YOLO: a novel model for white spot lesion detection. BMC Oral Health. 2025 Oct 9;25(1):1577. doi: 10.1186/s12903-025-06936-w.

  PMID: 41068748 RESULT

Study Officials

• Asmaa A. Mohamed Yassen

  Professor of Conservative Dentistry Department, Faculty of Dentistry, Cairo University

  STUDY DIRECTOR

• Rawda Hesham Abdelaziz

  Associate Professor of Conservative Dentistry Department, Faculty of Dentistry, Cairo University

  STUDY DIRECTOR

• Asmaa A. Elsayed Osman

  Lecturer of Information Technology, Faculty of Computers and Artificial Intelligence, Cairo University

  STUDY DIRECTOR

Central Study Contacts

Mohamed Hisham A.ELFattah Gabr, PhD

CONTACT

+201005660842; mohamed_gabr@dentistry.cu.edu.eg

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: PROSPECTIVE
• Target Duration: 1 Year
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: June 2, 2026
• First Posted: June 10, 2026
• Study Start (Estimated): July 1, 2026
• Primary Completion (Estimated): July 1, 2027
• Study Completion (Estimated): November 1, 2027
• Last Updated: June 10, 2026

Record last verified: 2026-06