DECIDE-AI:The Impact of Artificial Intelligence on Dentists' Decision-Making Process During Caries Detection: A Randomized Controlled Study
Phase: NA
Conditions: Artificial Intelligence (AI) in Diagnosis, Artificial Intelligence Supported Image Reviewing
Sponsor: Radboud University Medical Center

The Impact of Artificial Intelligence During Caries Detection 
 
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RESEARCH PROTOCOL 
 
DECIDE-AI 
The Impact of Artificial Intelligence on Dentists’ Decision-Making 
Process During Caries Detection: A Randomized Controlled Study 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

The Impact of Artificial Intelligence During Caries Detection 
 
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PROTOCOL TITLE The Impact of Artificial Intelligence on the Decision-Making Process of 
Dental Professionals During Caries Detection: A Randomized Controlled Study 
 
Short title 
The Impact of Artificial Intelligence on Caries 
Detection 
Version 
3 
Date 
26.05.2025 
Registration 
ClinicalTrials.gov 
Secondary identifying numbers 
2025-18123 
Project leader 
Dr. Maximiliano S. Cenci  
Radboudumc, Department of Dentistry 
Philips van Leydenlaan 25, 6525 EX Nijmegen, the 
Netherlands 
Max.Cenci@radboudumc.nl 
Coordinating Investigator 
Dilek Sezen-Hulsmans 
Radboudumc, Department of Dentistry 
Philips van Leydenlaan 25, 6525 EX Nijmegen, the 
Netherlands 
+491708975313  
Dilek.Sezen-Hulsmans@radboudumc.nl 
Principal Investigators 
Dr. Maximiliano S. Cenci  
Radboudumc, Department of Dentistry 
Philips van Leydenlaan 25, 6525 EX Nijmegen, the 
Netherlands 
Max.Cenci@radboudumc.nl 
 
Dr. Ewald Bronkhorst 
Radboudumc, Department of Dentistry 
Philips van Leydenlaan 25, 6525 EX Nijmegen, the 
Netherlands 
Ewald.Bronkhorst@radboudumc.nl 
 
Prof. Dr. Bas Loomans  
Radboudumc, Department of Dentistry 

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Philips van Leydenlaan 25, 6525 EX Nijmegen, the 
Netherlands 
Bas.Loomans@radboudumc.nl 
 
Dr. Rutger Sonneveld 
Prime Dental Alliance 
Don Boscostraat 4, 5611 KW Eindhoven 
Rutger.Sonneveld@pda.nl 
Author’s contribution 
DSH, MSC, RS, and BL initiated the study design, 
and EB provided statistical expertise and is 
conducting the primary statistical analysis. All 
authors contributed to refining the study protocol and 
approved the final manuscript. 
Sponsor 
Radboudumc, Nijmegen 
Prime Dental Alliance, Eindhoven 
Subsidising party 
Prime Dental Alliance, Eindhoven 
Counrty 
of 
Participant 
recruitment 
The Netherlands 
Study type 
Randomized controlled study 
Primary outcome 
Differences in treatment decisions with and without 
Artificial Intelligence support during caries detection 
using bitewings (dental radiographs). 
Secondary outcome 
Differences in diagnostic performance with and 
without Artificial Intelligence support during caries 
detection using bitewings. 
 
 
 
 
 
 
 
 
 
 
 
 
 

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TABLE OF CONTENTS 
 
1.INTRODUCTION AND RATIONALE................................................................................... 5 
2.OBJECTIVES ..................................................................................................................... 6 
3.STUDY DESIGN ................................................................................................................ 6 
4.STUDY POPULATION ....................................................................................................... 7 
5.METHODS ......................................................................................................................... 8 
6.STATISTICAL ANALYSIS .................................................................................................10 
7.ETHICAL CONSIDERATIONS ..........................................................................................10 
8.ADMINISTRATIVE ASPECTS AND PUBLICATION ..........................................................11 
9.REFERENCES ..................................................................................................................12 
 
 
 
 
 
 

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ABSTRACT 
Rationale: Artificial Intelligence (AI) based tools can support dental care providers' diagnosis 
of dental X-rays, promising increased accuracy. However, the influence of AI on dental 
professionals’ decision-making process must be further investigated to ensure it complements 
the practitioner’s abilities without diminishing their clinical judgment. Moreover, the potential  
Objective: The aim of this study is to evaluate the impact of AI-based diagnostic tools on the 
decision-making process of dental care providers compared to non-AI-supported decisions 
during caries detection. Specifically, the aim is to determine whether there are differences in 
non-invasive or invasive treatment recommendations, and diagnostic accuracy, when AI is 
utilized versus when it is not. Additionally, it will be explored how AI affects participants’ 
confidence levels. 
Study design: This study is a randomized controlled study with a crossover design examining 
the impact of AI on the decision-making process with caries lesions detection on dental 
radiographs (bitewings).  
Study population: Dentists from the Department of Dentistry at Radboud university medical 
center or private dental practices some affiliated with Prime Dental Alliance.  
Study parameters:  
1. Treatment Decisions: Assessment of treatment recommendations (e.g., no therapy, 
non-invasive interventions, or invasive treatments) with and without AI assistance. 
2. Diagnostic Accuracy: Measured by sensitivity and specificity in detecting caries lesions 
with and without AI assistance. 
 
Nature and extent of the burden and risks associated with participation, benefit, and 
group relatedness: There are no risks associated with participating in this study, since it 
includes standard diagnostic evaluations routinely performed by dentists, with the addition of 
using AI-based tools for some assessments. All evaluations are done in a controlled, non-
clinical setting with no changes in patient care. The study is designed collect information from 
dentists, simulating their clinical performance without patient data. Diagnostic performance and 
decision-making will be explored, posing no risk to patients or participants' professional 
standing. 
 
1. INTRODUCTION AND RATIONALE 
 
AI-based tools are becoming more present in dentistry by supporting diagnostic processes, 
such as caries detection, while promising enhanced diagnostic accuracy [1]. However, even 
with the increased performance of AI technologies, there still appears to be a lack of knowledge 
about its influence on practitioners’ decision-making, which is critical if the goal is to integrate 

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it further into dental practices [2]. Research shows that dentists tend to be more invasive with 
conditions that may not progress if only observed or treated non-invasively [3]. AI, being highly 
sensitive to flagging initial lesions, may contribute to these decisions with the potential result 
of overdiagnosis and overtreatment.  
A study conducted in 2021 recruited 20 dentists to analyse a set of 20 bitewings (dental 
x-rays used to diagnose caries), 10 with the support of AI and ten without. The results showed 
that using AI led to higher detection rates as well as more non-invasive and invasive treatment 
suggestions for enamel caries, shifting from 32% to 36% for non-invasive and 17% to 24% for 
invasive treatment (p<0.05) [4]. On the other hand, studies have also emphasized the 
educative factor of using AI. In a different study the caries diagnosis performance of students 
was investigated after training with AI, showing that accuracy, sensitivity, and specificity were 
significantly higher for the trained group compared to the non-trained group (p < 0.05) [5].  
Incorporating a support medium, such as AI-based diagnostic programs, may enhance 
accuracy in detecting caries lesions and introduce potential risks, such as overtreatment. 
Therefore, in contrast to other studies focusing on AI’s accuracy or the resulting increase in 
dentists’ accuracy, this study will focus on differences in dentists’ treatment recommendations 
when supported by AI versus when they are not during caries detection. Researching these 
aspects of AI in dental diagnostics is crucial to enhance the responsible integration of Artificial 
Intelligence into dental practice. 
  
2. OBJECTIVES 
      2.1 Primary Objectives:  
To evaluate the impact of AI-based diagnostic tools on the decision-making process of dental 
care providers using dental radiographs (bitewings). 
1. Treatment decisions: Compare the treatment recommendations of dentists (e.g., no therapy, 
non-invasive interventions, or invasive treatments) for caries lesions detected with and without 
AI support. 
 
     2.2 Secondary Objectives:  
1. Diagnostic performance: Measured by sensitivity and specificity in detecting caries lesions 
with and without AI assistance. 
 
     2.3 Hypotheses 
1. 
The use of an AI-based diagnostic tool increases non-invasive and invasive treatment 
decisions of dentists during caries detection on bitewing radiographs.  
2. 
The diagnostic sensitivity of dentist in detecting caries lesions increases when using AI 
support, while the diagnostic specificity does not differ with or without AI support. 

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3. STUDY DESIGN 
This study is a crossover randomized controlled study investigating the influence of artificial 
intelligence on the decision-making process for intervention after caries lesion detection. 
Participants will be dentists randomly divided into two groups. Each group will be randomized 
to receive a set of bitewing radiographs, which should be evaluated with or without AI 
assistance. The participants will examine caries lesions on the radiographs according to their 
group and formulate treatment plans accordingly. Then, after a wash-out period of one month, 
all participants will re-examine the same radiographs, but in the opposite condition of AI 
assistance (Figure 1). The primary outcome will be classified into no treatment, non-invasive 
treatment, and invasive approaches. The secondary outcomes include diagnostic 
performance, assessed through sensitivity and specificity of caries detection. 
The impact of AI assistance will be evaluated by comparing the frequency and type of 
treatment recommendations, as well as diagnostic accuracy (sensitivity and specificity), 
between conditions with and without AI support. Additionally, these decisions will also be 
evaluated against a reference standard. 
 
4. STUDY POPULATION 
     4.1 Inclusion criteria 
The study’s target population are dental professionals who are authorized to detect caries on 
bitewing radiographs and indicate treatment in clinical practice, according to the Dutch 
professional profiles [6]. Dentists who meet the eligibility criteria will be recruited from the 
dentistry department and dental practices. They will be invited to perform two rounds of 
bitewing evaluation. Participants will be asked to sign an informed consent form within 
CastorEDC before the start of the survey. 
1. Graduated, practising dentists. 
2. At least three years of experience 
 
4.2 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.  
 
4.3 Sample size calculation 
By means of simulations, a sample size was determined that allows us to find a difference in 
treatment decisions when using AI assistance during caries detection. Simulations were based 
on data from a pilot study conducted on last-year dental students. Sensitivity for detecting all 
stage lesions without AI was 85.2%, and sensitivity with AI was 90.4%, so a difference of 5.2%. 

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As a result of the study’s crossover design, where each dentist evaluates both with and without 
AI, the study controls for individual differences and increases statistical power. The power 
analysis assumes statistical testing at a 5% significance level, and we aim to be able to detect 
a difference of 5% in sensitivity with AI, assuming a baseline sensitivity of 85% without AI. 
Therefore, to achieve 80% power, the study requires 25 dentists, each evaluating 25 surfaces 
(total of 625 surfaces), to enable detecting a 5% difference in sensitivity. Therefore, assuming 
one bitewing includes 2-3 surfaces that require treatment, 25 dentists would each have to 
analyse 8-10 bitewings. 
 
5. METHODS 
The SPIRIT reporting guideline was followed to report this protocol. This protocol will be 
submitted to the appraisal through the niet-WMO (Wet Medisch-wetenschappelijk Onderzoek)-
plichtig route and registered at PaNaMa. The intention is to register further at ClinicalTrials.gov. 
Additionally, it will be accessible through the Open Science Framework. 
 
      5.1 Study parameters 
5.1.1 Main study parameter  
1. Treatment Recommendations: The main study parameter is the differences in treatment 
recommendations for caries lesions with and without AI assistance. The given options will be 
“no treatment”, “non-invasive treatment” (fluoride varnish, polishing, sealing), and “restoration”.  
 
5.1.2 Secondary study parameters/endpoints 
1. Diagnostic Accuracy: The secondary parameters include diagnostic accuracy of caries 
lesions when assessed using AI-based diagnostic tools compared to without AI support. This 
will be measured by evaluating the sensitivity and specificity of AI-assisted and non-AI-assisted 
diagnostics. Participants will report on whether a caries lesion is presented and if yes, the 
extend of it as following:  
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. 
This classification is conformed to the International Caries Classification and Management 
System (ICCMS) [7]. However, codes 4 and 5 will be summarized for the study’s purpose. 
 

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5.2 Randomisation 
Dentists will be randomly allocated into two parallel groups in a 1:1 ratio: 
1. 
Group 1: Starting with AI support. 
2. 
Group 2: Starting without AI support. 
The allocation sequence will be computer-generated using the randomization module within 
CastorEDC. Participants will receive a unique identifier and be assigned to a group upon 
enrolment, ensuring allocation concealment while allowing identification for the second round. 
The randomization will follow a simple randomization method without stratification. Study 
personnel involved in enrolment or data collection will have no access to the randomization 
sequence, ensuring allocation concealment by default. An independent researcher, who is not 
involved in recruitment, will enrol participants, and start the randomization process within 
CastorEDC.  
Additionally, to reduce the potential for recognition bias, the sequence in which each bitewing 
radiograph is evaluated will be randomized individually for each dentist and for both study 
phases. This randomization will also be performed using functions in CastorEDC. 
 
5.3 Study procedures 
Participants will assess bitewings with or without AI assistance according to the assigned 
group. When caries is detected by the participant, they must select a possible treatment 
approach: no treatment (includes monitoring), non-invasive interventions (dietary advice, oral 
hygiene instructions, fluoride varnish, polishing, or sealing), or restoration placement. In 
addition, participants will be asked to evaluate if caries lesions are present to indicate the level 
of progression. The example in figure 2 will be provided to calibrate participants answers. After 
a wash-out period of four weeks, participants will proceed to the second round of assessments. 
At this point, the groups will assess the bitewings in the opposite AI condition. The same 

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bitewings will be assessed; however, the sequence will be randomized again.  
 
 
5.4 Data collection  
In this study, bitewings will be used, and Pearl’s Second Opinion AI software will detect caries 
lesions on the radiographs. The bitewings will be collected from open datasets of previous 
studies and included in a survey using Castor EDC. A link will be provided to each participant, 
who will complete the survey online. For participants in the AI-group each image will be 
provided twice. The original image and one with AI flagging. In the non-AI group, each image 
will be provided without AI flagging. Finally, all the data will be converted into an Excel file, and 
a. csv file, which will then be reviewed.  

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5.5 Reference standard 
Five experts will establish the reference test for caries lesions. The experts will independently 
evaluate each case regarding the presence and depth of carious lesions (enamel lesion, early 
dentin lesion, middle dentin lesion, outer dentin lesion), and indicate a therapeutic approach. 
A discussion round will be held to seek full agreement among the experts.  
 
6. STATISTICAL ANALYSIS 
The analysis will focus on treatment decision-making and diagnostic accuracy between 
conditions with and without AI support. Primary analysis will focus on treatment 
recommendations, classified into three categories: no treatment (NT), non-invasive treatment 
(NIT), and invasive treatment (IT). Treatment decisions will be compared across the two 

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conditions with vs. without AI support. A confusion matrix will be constructed to visualize the 
direction and quantity of changes between these categories (e.g., NT to NIT, NT to IT, NIT to 
NT). Differences in proportions will be examined using McNemar’s test or paired statistical 
tests, depending on data distribution and assumptions. 
Secondary outcomes will focus on diagnostic performance, specifically the ability to correctly 
identify the presence or absence of caries and differences between conditions with and without 
AI support. Again, confusion matrices will be used to visualize the direction and quantity of 
changes in diagnostic outcomes. Proportions of correct and incorrect classifications will be 
compared using paired statistical tests. Key metrics, including sensitivity, specificity, precision, 
and F1 score, will be calculated to quantify diagnostic accuracy.  
Descriptive statistics will summarize participant demographics (age, gender, years of 
experience, training background).    In addition, we will conduct exploratory regression 
analyses to assess whether participant years of experience and training background are 
associated with treatment decisions and diagnostic accuracy. For treatment decision outcomes 
(NT, NIT, IT), a multinomial logistic regression will be performed. For diagnostic performance 
(correct/incorrect classification), a binary logistic regression will be used. These analyses will 
help identify whether clinician background moderates the impact of AI support. Given the 
sample size (n=25), these analyses will be considered exploratory and interpreted with caution. 
All statistical analyses will be conducted using SPSS or R. 
 
7. ETHICAL CONSIDERATIONS 
     7.1 Regulations 
The Medical Research Involving Human Subjects Act (WMO) is not applicable to this study. 
Before the start of the study, the study protocol and other related documents were submitted 
to the Committee on Research Involving Human Subjects (CMO) and declared as “niet-WMO-
verplicht” research. Any subsequent protocol amendments will be submitted to the CMO to 
review. No substantial amendment that requires review by the CMO will be implemented until 
the CMO grants a favourable opinion for the study. 
 
     7.2 Recruitment and privacy 
The participants will be dentists working at Radboudumc and practices affiliated with Prime 
Dental Alliance. Participants who meet the eligibility criteria will be invited via an introductory 
email and if they wish to participate, an informed consent form will be required. A key file will 
be kept with participant information so that the invitations for the second round of evaluation 
can be send. After data collection that file will be deleted to ensure anonymity. 
 
 

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7.3 Dataset 
In this study bitewings, will be used for caries detection. The bitewings were collected from 
several different studies and considered anonymous data because they only provide partial 
information about a patient, and do not reveal the individual's identity. No additional patient 
data will be needed for this study.  
The participants identity will be kept in a separate key file until after the second round 
of questionnaires, then all contact information will be deleted, and participant anonymity will 
be fully secured. 
 
8. ADMINISTRATIVE ASPECTS AND PUBLICATION 
    
8.1 Publication 
The arrangements between the sponsor (PDA) and the primary researcher (DSH) regarding 
the publication of the research data ensure that both parties' interests are respected. The 
researcher retains the right to publish the research data in scientific journals and present the 
findings at conferences. 
 
     
8.2 Handling and storage of data and documents 
All data collected during the study, will be securely stored in compliance with data protection 
regulations, and will be backed up regularly and protected by password authentication to 
prevent unauthorized access. Access logs will be maintained to track any data access or 
modifications, by the leading researcher. Data will be stored after the study's completion for 
verification and research purposes at the Radboud Data Repository. Data, bitewings, and 
participants’ responses will be anonymized to ensure participant confidentiality, with no 
personal identifiers linked to the radiographs or survey responses. The anonymized data will 
be accessible alongside the publication. 
 
 
 
 
 
 
 
 
 
 
 
 

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9. REFERENCES 
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dentistry: a systematic review, J. Dent. Sci. 16 (2021) 508–522. 
[2] N. Ammar, Diagnostic performance of artificial intelligence-aided caries detection on 
bitewing radiographs: a systematic review and meta-analysis, Jpn. Dent. Sci. Rev. 60 (2024) 
128–136. 
[3] M. Laske, N. J. M. Opdam, E. M. Bronkhorst, J. C. C. Braspenning, W. J. M. van der 
Sanden, M. C. D. N. J. M. Huysmans, J. J. Bruers, Minimally invasive intervention for primary 
caries lesions: are dentists implementing this concept?, Caries Res. 53 (2019) 204–216. 
[4] S. Mertens, J. Krois, A. G. Cantu 2, L. T. Arsiwala, F. Schwendicke, Artificial intelligence 
for caries detection: randomized trial, J. Dent. 115 (2021) 103849. 
[5] E. Ayan, Y.Bayraktar, Ç. Çelik, B. Ayhan, Dental student application of artificial intelli-
gence technology in detecting proximal caries lesions, J. Dent. Educ. 88 (2024) 490–500. 
[6] Koninklijke Nederlandse Maatschappij tot bevordering der Tandheelkunde (KNMT), Den-
tist job profile [Functieprofiel tandarts], (2021). https://knmt.nl/sites/default/files/plat-
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[7] W. Panyarak, K. Wantanajittikul, W. Suttapak, A. Charuakkra, S. Prapayasato, Feasibility 
of deep learning for dental caries classification in bitewing radiographs based on the IC-
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(2023) 272–278.