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

June 8, 2026 updated by: Mohamed Hisham Abd ElFattah Gabr Ali, Cairo University

Diagnostic Accuracy of Artificial Intelligence Analysis Using Intraoral Photographs Versus Clinical Examination in White Spot Lesions Detection, Identification, And Scoring.

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?

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Detailed Description

Dentists frequently encounter various dental hard tissue anomalies that present both diagnostic challenges and require careful treatment planning. A common example is white spot lesions or whitish discolorations of the teeth that can arise from multiple etiologies. These may be carious in nature, such as post-orthodontic incipient caries, or represent developmental defects like dental fluorosis or molar-incisor hypo-mineralization (MIH). Distinguishing between these conditions is essential for appropriate clinical management. The diagnosis of WSLs primarily relies on visual and photographic examination, which evaluates the morphology, size, color, and location of the lesions. Depth assessment is particularly critical, as it serves as a key determinant in selecting the most appropriate treatment approach. Detection and scoring of WSLs can be accomplished through clinical visual inspection alone or enhanced by adjunctive diagnostic technologies, including laser fluorescence, quantitative light-induced fluorescence (QLF), and electrical impedance spectroscopy.

The clinical characteristics of WSLs can vary considerably, making differential diagnosis challenging. While specialist clinicians demonstrate high validity and reliability in distinguishing between various white spot lesions, general dental practitioners exhibit lower diagnostic accuracy. Conventional diagnostic approaches for white spot lesions often lack precision and consistency. Visual assessment presents several inherent limitations, including the potential for misclassification due to overlapping clinical presentations among different etiologies. A systematic review and meta-analysis examining the efficacy of detection methods for incipient caries reported that photographic visual inspection achieved a sensitivity of only 67% and specificity of 79%, highlighting the need for more reliable diagnostic approaches.

Recent advances in computing power, data accessibility, and processing capabilities have accelerated the development of artificial intelligence (AI) applications, transforming contemporary healthcare research. Dentistry has similarly benefited from this technological evolution, with AI demonstrating considerable potential across various clinical applications. Machine learning (ML), a subfield of AI, represents a powerful approach for computer-aided diagnostic support, with algorithms that identify patterns within datasets during training and apply this knowledge to make predictions on new data. Emerging evidence indicates that these AI advances can improve diagnostic accuracy in caries detection, thereby supporting clinicians in making more precise and reliable assessments. Dental photography, captured using devices such as DSLR cameras or intraoral cameras, serves as a valuable tool for diagnosis and treatment planning. When combined with intelligent image analysis methods, AI can automate the identification and assessment of diagnostic data from photographs, facilitating standalone diagnostic procedures that reduce subjectivity and enhance clinical decision-making. Deep CNNs can detect and distinguish entities of similar but not identical appearance when trained on sufficiently large image datasets. Employing such models as part of an integrated image-analysis software solution would enable rapid classification of existing photographic library data and improve the accuracy and reliability of clinicians' decision-making in treatment planning or referral. Emerging evidence from studies using intraoral photographs and AI algorithms demonstrates promising detection rates, with AI exhibiting high sensitivity, specificity, precision, accuracy, and reliability in diagnostic performance. AI-driven tools are designed to serve as supportive aids for clinicians, strengthening diagnostic accuracy, streamlining workflows, improving cost-efficiency, and enhancing patient care rather than replacing clinical expertise. Simplifying the diagnostic process for white spot lesions is essential to enable their timely detection at an early stage, facilitating prompt intervention and improving preventive outcomes. AI models must be validated using local data from diverse clinical settings, particularly in lower-middle-income countries, with their performance assessed through sensitivity, specificity, and accuracy measures and benchmarked against conventional diagnostic approaches. Therefore, it is crucial to develop and validate white spot lesion detection and classification models using data from Egyptian patients attending Cairo University Hospital to ensure accurate and clinically relevant results, emphasizing the potential improvements in accuracy and reliability that AI can bring to dental diagnostics.

Study Type

Observational

Enrollment (Estimated)

329

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

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

Accepts Healthy Volunteers

No

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.

Description

Inclusion Criteria:

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

Exclusion Criteria:

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

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

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Time Frame
Artificial Intelligence diagnostic accuracy in White Spot Lesions Detection
Time Frame: Baseline
Baseline

Collaborators and Investigators

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

Sponsor

Cairo University

Investigators

  • Study Director: 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

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)

July 1, 2026

Primary Completion (Estimated)

July 1, 2027

Study Completion (Estimated)

November 1, 2027

Study Registration Dates

First Submitted

June 2, 2026

First Submitted That Met QC Criteria

June 8, 2026

First Posted (Actual)

June 10, 2026

Study Record Updates

Last Update Posted (Actual)

June 10, 2026

Last Update Submitted That Met QC Criteria

June 8, 2026

Last Verified

June 1, 2026

More Information

Terms related to this study

Keywords

Other Study ID Numbers

  • AI in detecting dental caries

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

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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