The Impact of Artificial Intelligence on Dentists' Decision-Making Process During Caries Detection (DECIDE-AI)
June 10, 2025 updated by: Radboud University Medical Center
DECIDE-AI:The Impact of Artificial Intelligence on Dentists' Decision-Making Process During Caries Detection: A Randomized Controlled Study
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.
Study Overview
Status
Not yet recruiting
Conditions
Intervention / Treatment
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.
Study Type
Interventional
Enrollment (Estimated)
25
Phase
- Not Applicable
Contacts and Locations
This section provides the contact details for those conducting the study, and information on where this study is being conducted.
Study Locations
-
-
Gelderland
-
Nijmegen, Gelderland, Netherlands, 6525 EX
- Department of Dentistry Radboud Uniersity Medical Center
-
Contact:
- Dilek Sezen-Hulsmans, Dentist
- Phone Number: 00491708975313
- Email: dilek.sezen-hulsmans@radboudumc.nl
-
Contact:
- Dr. Cenci
- Email: Max.Cenci@radboudumc.nl
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Principal Investigator:
- Dilek Sezen-Hulsmans, Dental Medical Doctor
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Principal Investigator:
- Dr. Cenci
-
-
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
- Child
- Adult
- Older Adult
Accepts Healthy Volunteers
Yes
Description
Inclusion Criteria:
- Graduated, practising dentists.
- At least three years of experience
Exclusion Criteria:
- Retired dentists.
- Specialized practitioners (e.g., orthodontists and oral surgeons) if their typical practice does not involve routine caries diagnostics and treatment planning.
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
- Primary Purpose: Diagnostic
- Allocation: Randomized
- Interventional Model: Crossover Assignment
- Masking: Single
Arms and Interventions
Participant Group / Arm |
Intervention / Treatment |
|---|---|
|
Active Comparator: Phase 1: Caries detection without AI, Phase 2: Caries detection with AI
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.
|
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.
|
|
Active Comparator: Phase 1: Caries detection with AI, Phase 2: Caries detection without AI
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.
|
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.
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Treatment decisions: Compare the treatment recommendations of dentists for caries lesions detected with and without AI support.
Time Frame: Each participant will be assessed over a period of up to 2 months (includes both evaluation phases and washout period)
|
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 Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Diagnostic Accuracy in Caries Detection
Time Frame: Each participant will be assessed over a period of up to 2 months (includes both evaluation phases and washout period)
|
RA0: No radiolucency - No visible caries. RA1: Radiolucency confined to the outer half of enamel - Enamel caries. RA2: Radiolucency extending to the inner half of enamel but not reaching dentin - Moderate enamel caries. RA3: Radiolucency extending into the outer third of dentin - Dentin caries. RA4: Radiolucency extending into the middle of dentin - Advanced dentin caries. RA5: Radiolucency extending into the inner third of dentin - Severe dentin caries. 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)
|
Collaborators and Investigators
This is where you will find people and organizations involved with this study.
Collaborators
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
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
Helpful Links
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)
October 2, 2025
Primary Completion (Estimated)
June 2, 2026
Study Completion (Estimated)
June 2, 2026
Study Registration Dates
First Submitted
May 28, 2025
First Submitted That Met QC Criteria
June 10, 2025
First Posted (Actual)
June 18, 2025
Study Record Updates
Last Update Posted (Actual)
June 18, 2025
Last Update Submitted That Met QC Criteria
June 10, 2025
Last Verified
May 1, 2025
More Information
Terms related to this study
Other Study ID Numbers
- 2025-18123
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
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