The Impact of Artificial Intelligence on Dentists' Decision-Making Process During Caries Detection

NCT07027189 | Not Yet Recruiting DMC

• 1 other identifier: 2025-18123
• Study Type: interventional
• Target: 25
• Locations: 1 country
• Sites: 1

Brief Summary

This study aims to evaluate the influence of artificial intelligence (AI) on the decision-making process for intervention after caries lesion detection. Participants will be dentists working in the Netherlands randomly divided into two groups. Dentists will be divided into two groups and receive a set of bitewing radiographs, which first will be evaluated with or without AI support according to their group. Participants will examine caries lesions on the radiographs and formulate treatment plans accordingly. Then, after a wash-out period of one month, the same radiographs, but in the opposite condition of AI support and again formulate treatment suggestions according to the present caries lesions.

Conditions

Artificial Intelligence (AI) in Diagnosis; Artificial Intelligence Supported Image Reviewing

Keywords

Artificial Intelligence; Caries Detection; Decision-making; Dental Imaging; Treatment Planning

Outcome Measures

Primary Outcomes (1)

• Treatment decisions: Compare the treatment recommendations of dentists for caries lesions detected with and without AI support.

  The given options will be "no treatment", "non-invasive treatment" (fluoride varnish, polishing, sealing), and "restoration". Participants' answers will be compared to a reference standard.

  Each participant will be assessed over a period of up to 2 months (includes both evaluation phases and washout period)

Secondary Outcomes (1)

• Diagnostic Accuracy in Caries Detection

  Each participant will be assessed over a period of up to 2 months (includes both evaluation phases and washout period)

Study Arms (2)

Phase 1: Caries detection without AI, Phase 2: Caries detection with AI

ACTIVE COMPARATOR

In this group participants will examine caries lesions on the radiographs without AI support first. Then, after a wash-out period of one month, all participants will re-examine the same radiographs with AI.

Diagnostic Test: Artificial intelligence in diagnosis

Phase 1: Caries detection with AI, Phase 2: Caries detection without AI

ACTIVE COMPARATOR

In this group participants will examine caries lesions on the radiographs with AI support first. Then, after a wash-out period of one month, all participants will re-examine the same radiographs without AI.

Diagnostic Test: Artificial intelligence in diagnosis

Interventions

Artificial intelligence in diagnosis DIAGNOSTIC_TEST

AI-based diagnostic programs have proved to enhance diagnostic performance, however research on its effects on treatment decisions is scarce. In contrast to other studies focusing on AI's accuracy or the resulting increase in dentists' accuracy, this study aims to investigate the differences in dentists' treatment recommendations when supported by AI versus when they are not during caries detection.

Phase 1: Caries detection with AI, Phase 2: Caries detection without AI; Phase 1: Caries detection without AI, Phase 2: Caries detection with AI

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• Graduated, practising dentists.
• At least three years of experience

You may not qualify if:

• Retired dentists.
• Specialized practitioners (e.g., orthodontists and oral surgeons) if their typical practice does not involve routine caries diagnostics and treatment planning.

Sponsors & Collaborators

• Radboud University Medical Center: lead
• Prime Dental Alliance Eindhoven: collaborator

Study Sites (1)

Department of Dentistry Radboud Uniersity Medical Center

Nijmegen, Gelderland, 6525 EX, Netherlands

Location

Related Publications (6)

• Panyarak W, Wantanajittikul K, Suttapak W, Charuakkra A, Prapayasatok S. Feasibility of deep learning for dental caries classification in bitewing radiographs based on the ICCMS radiographic scoring system. Oral Surg Oral Med Oral Pathol Oral Radiol. 2023 Feb;135(2):272-281. doi: 10.1016/j.oooo.2022.06.012. Epub 2022 Jul 2.

  PMID: 36513589 BACKGROUND

• Ayan E, Bayraktar Y, Celik C, Ayhan B. Dental student application of artificial intelligence technology in detecting proximal caries lesions. J Dent Educ. 2024 Apr;88(4):490-500. doi: 10.1002/jdd.13437. Epub 2024 Jan 10.

  PMID: 38200405 BACKGROUND

• Mertens S, Krois J, Cantu AG, Arsiwala LT, Schwendicke F. Artificial intelligence for caries detection: Randomized trial. J Dent. 2021 Dec;115:103849. doi: 10.1016/j.jdent.2021.103849. Epub 2021 Oct 14.

  PMID: 34656656 BACKGROUND

• Laske M, Opdam NJM, Bronkhorst EM, Braspenning JCC, van der Sanden WJM, Huysmans MCDNJM, Bruers JJ. Minimally Invasive Intervention for Primary Caries Lesions: Are Dentists Implementing This Concept? Caries Res. 2019;53(2):204-216. doi: 10.1159/000490626. Epub 2018 Aug 14.

  PMID: 30107377 BACKGROUND

• Ammar N, Kuhnisch J. Diagnostic performance of artificial intelligence-aided caries detection on bitewing radiographs: a systematic review and meta-analysis. Jpn Dent Sci Rev. 2024 Dec;60:128-136. doi: 10.1016/j.jdsr.2024.02.001. Epub 2024 Feb 29.

  PMID: 38450159 BACKGROUND

• Khanagar SB, Al-Ehaideb A, Maganur PC, Vishwanathaiah S, Patil S, Baeshen HA, Sarode SC, Bhandi S. Developments, application, and performance of artificial intelligence in dentistry - A systematic review. J Dent Sci. 2021 Jan;16(1):508-522. doi: 10.1016/j.jds.2020.06.019. Epub 2020 Jun 30.

  PMID: 33384840 BACKGROUND

Related Links

• Dutch professional profile description for dentists.

MeSH Terms

Interventions

Artificial Intelligence; Diagnosis

Intervention Hierarchy (Ancestors)

Algorithms; Mathematical Concepts

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: PARTICIPANT
• Purpose: DIAGNOSTIC
• Intervention Model: CROSSOVER
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: May 28, 2025
• First Posted: June 18, 2025
• Study Start: October 2, 2025
• Primary Completion (Estimated): June 2, 2026
• Study Completion (Estimated): June 2, 2026
• Last Updated: June 18, 2025

Record last verified: 2025-05

Locations

NL (1)