Clinical trial NCT07027189: The Impact of Artificial Intelligence on Dentists' Decision-Making Process During Caries Detection
Official title: DECIDE-AI:The Impact of Artificial Intelligence on Dentists' Decision-Making Process During Caries Detection: A Randomized Controlled Study
Study type: INTERVENTIONAL
Overall status: NOT_YET_RECRUITING
Phase: NA
Conditions: Artificial Intelligence (AI) in Diagnosis
Artificial Intelligence Supported Image Reviewing
Keywords: Artificial Intelligence
Caries Detection
Decision-making
Dental Imaging
Treatment Planning
Lead sponsor: Radboud University Medical Center
Sponsor class: OTHER
Collaborators: name: Prime Dental Alliance Eindhoven
First posted: 2025-06-18
Enrollment: 25
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: CROSSOVER
Primary purpose: DIAGNOSTIC

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.

Detailed description: This crossover randomized controlled trial evaluates the effect of artificial intelligence (AI) decision support on dentists' treatment planning following caries detection bitewing radiographs. The study targets clinical decision-making processes by assessing how AI influences diagnostic interpretation and subsequent treatment suggestions. Dentists will be randomly assigned into two study arms. Each participant will evaluate a standardized set of digital bitewing radiographs under two conditions: once with AI assistance and once without, separated by a one-month wash-out period to minimize recall bias. The AI tool provides caries detection prompts based on radiographic analysis but does not suggest treatment. The crossover design enables within-subject comparison, controlling for individual diagnostic thresholds. The radiographs remain constant across both phases to isolate the influence of AI support. The study focuses on diagnostic performance and clinical decision outcomes, both with and without AI support. Treatment decisions are categorized into three predefined levels: no treatment, non-invasive treatment (e.g., fluoride application, polishing, sealing), and invasive intervention (i.e., restorative treatment). Diagnostic accuracy is measured against a reference standard and reported in terms of sensitivity and specificity. Caries detection will be classified using a modified International Caries Classification and Management System (ICCMS). This study design allows to quantify AI's impact on diagnostic performance, as well as on potential shifts in treatment approach. The study aims to contribute to evidence-based guidance on the integration of AI tools into clinical dental practice.

Eligibility criteria: Inclusion Criteria:

1. Graduated, practising dentists.
2. At least three years of experience

Exclusion Criteria:

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

Primary outcomes:
measure: Treatment decisions: Compare the treatment recommendations of dentists for caries lesions detected with and without AI support.
timeFrame: Each participant will be assessed over a period of up to 2 months (includes both evaluation phases and washout period)
Secondary outcomes:
measure: Diagnostic Accuracy in Caries Detection
timeFrame: Each participant will be assessed over a period of up to 2 months (includes both evaluation phases and washout period)
Other outcomes:
Locations:
city: Nijmegen
country: Netherlands
facility: Department of Dentistry Radboud Uniersity Medical Center
state: Gelderland
Study officials:
References:
citation: 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.
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citation: 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.
citation: 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.
citation: 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.