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Detecting the Size/Depth of Carious Lesions through Artificial Intelligence

384 - Detecting Dental Caries and Furcation Involvement through Artificial Intelligence

Saturday, May 24, 2025
2:15 PM - 2:30 PM MDT
Has Audio

    Presenting Author(s)

  • Tiffanie Chiu, DMD

    Pediatric Dentistry Resident
    Rutgers School of Dental Medicine, Newark, NJ
    Rutgers School of Dental Medicine
    East Orange, New Jersey, United States

    • Program Director(s)

  • MM

    Madhu Mohan, DMD

    Rutgers Dental School
    Newark, New Jersey, United States

Purpose: The purpose of this research project is to test the accuracy of artificial intelligence (AI) detected dental caries and furcation involvement lesions compared to clinical findings by board certified pediatric dentists.

Methods: A total of 50 pediatric (ages 13 and below) bitewing radiographs will be examined and assessed by Diagnocat, an artificial intelligence dental platform. Some bitewings will contain carious decay and other ones will also present with furcation involvement. The bitewings will be submitted for evaluation and detection of dental caries and furcation involvement lesions by Diagnocat. The results from Diagnocat will be compared to the dental caries and furcation lesions found clinically by licensed pediatric dentists. The percentage accuracy of the AI detection will be calculated.

Results: Final data pending

Conclusion: Final data pending. The results will give insight on the current expectations of AI in pediatric dentistry as well as what improvements can be made for the future.