Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Feb 26;14(5):1566.
doi: 10.3390/jcm14051566.

Comparison of the Diagnostic Accuracy of an AI-Based System for Dental Caries Detection and Clinical Evaluation Conducted by Dentists

Jakub Kwiatek  1 , Marta Leśna  1 , Wiktor Piskórz  1 , Justyna Kaczewiak  1
Affiliations

Comparison of the Diagnostic Accuracy of an AI-Based System for Dental Caries Detection and Clinical Evaluation Conducted by Dentists

Jakub Kwiatek et al. J Clin Med. .

Abstract

Background/Objectives: Artificial intelligence (AI)-based software is increasingly used for radiographic analysis in dentistry. This study aimed to evaluate the diagnostic accuracy of an AI-powered radiographic analysis system, using Diagnocat (DGNCT LLC, Miami, FL, USA) as an example, compared with clinical evaluations performed by three experienced dentists. The assessment focused on primary caries detection and the total number of primary and secondary caries based on panoramic radiographs (OPGs). Methods: Three dentists with similar expertise independently classified teeth for treatment using only panoramic radiographs and their clinical knowledge. The study was conducted under single-blind conditions, where clinicians were unaware that their diagnoses would be compared to the AI system's analysis. Results: The AI system's agreement with human evaluations varied depending on tooth location, patient age, and gender. The lowest agreement was observed for premolars, likely due to limitations of 2D imaging, while higher accuracy was found for molars and incisors, particularly in younger patients. The system showed limitations in detecting occlusal, labial, and lingual caries. Conclusions: AI-assisted radiographic analysis has the potential to enhance diagnostic efficiency and automation in dentistry. However, its accuracy is influenced by tooth location and imaging modality. Further research is needed to explore the benefits of integrating AI with 3D imaging techniques to improve diagnostic reliability.

Keywords: Diagnocat; artificial intelligence (AI); blind study; dental caries; dental diagnostics; machine learning; panoramic radiographs.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Screenshot of the AI Program “Diagnocat” (DGNCT LLC, Miami, FL, USA)—Report from Panoramic Radiograph Analysis.
Figure 2
Figure 2
Characteristics of the Study Sample.
Figure 3
Figure 3
Agreement Between Diagnocat (DGNCT LLC, Miami, FL, USA) Analysis and Clinical Evaluation—Primary and Secondary Caries.
Figure 4
Figure 4
Agreement Between Diagnocat (DGNCT LLC, Miami, FL, USA) Analysis and Clinical Evaluation—Primary and Secondary Caries in Women and Men.
Figure 5
Figure 5
Agreement Between Diagnocat (DGNCT LLC, Miami, FL, USA) Analysis and Clinical Evaluation—Healthy Teeth in Women and Men.
Figure 6
Figure 6
Agreement Between Diagnocat (DGNCT LLC, Miami, FL, USA) Analysis and Clinical Evaluation in Detecting Primary and Secondary Caries by Age Group. (*—Statistically significant difference detected for this tooth type).
Figure 7
Figure 7
Agreement Between Diagnocat (DGNCT LLC, Miami, FL, USA) Analysis and Clinical Evaluation by Dentist—Primary and Secondary Caries. (*—Statistically significant difference detected for this tooth type).
Figure 8
Figure 8
Agreement Between Diagnocat (DGNCT LLC, Miami, FL, USA) Analysis and Clinical Evaluation—Primary Carie.
Figure 9
Figure 9
Agreement Between Diagnocat (DGNCT LLC, Miami, FL, USA) Analysis and Clinical Evaluation—Primary Caries in Women and Men.
Figure 10
Figure 10
Agreement Between Diagnocat (DGNCT LLC, Miami, FL, USA) Analysis and Clinical Evaluation in Detecting Primary Caries by Age Group.
Figure 11
Figure 11
Agreement Between Diagnocat (DGNCT LLC, Miami, FL, USA) Analysis and Clinical Evaluation by Dentist—Primary Caries. (*—Statistically significant difference detected for this tooth type).
Figure 12
Figure 12
Agreement Between Diagnocat (DGNCT LLC, Miami, FL, USA) Analysis and Clinical Evaluation—Primary and Secondary Caries (Total) and Primary Caries.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Issa J., Jaber M., Rifai I., Mozdziak P., Kempisty B., Dyszkiewicz-Konwińska M. Diagnostic Test Accuracy of Artificial Intelligence in Detecting Periapical Periodontitis on Two-Dimensional Radiographs: A Retrospective Study and Literature Review. Medicina. 2023;59:768. doi: 10.3390/medicina59040768. - DOI - PMC - PubMed
    1. Hołyst J.A., Mayr P., Thelwall M., Frommholz I., Havlin S., Sela A., Kenett Y.N., Helic D., Rehar A., Maček S.R. Protect our environment from information overload. Nat. Hum. Behav. 2024;8:402–403. doi: 10.1038/s41562-024-01833-8. - DOI - PubMed
    1. Chen Y.W., Stanley K., Att W. Artificial intelligence in dentistry: Current applications and future perspectives. Quintessence Int. 2020;51:248–257. - PubMed
    1. Schwendicke F.A., Samek W., Krois J. Artificial intelligence in dentistry: Chances and challenges. J. Dent. Res. 2020;99:769–774. doi: 10.1177/0022034520915714. - DOI - PMC - PubMed
    1. Zadrożny Ł., Regulski P., Brus-Sawczuk K., Czajkowska M., Parkanyi L., Ganz S., Mijiritsky E. Artificial intelligence application in assessment of panoramic radiographs. Diagnostics. 2022;12:224. doi: 10.3390/diagnostics12010224. - DOI - PMC - PubMed

LinkOut - more resources