Real-Time Caries Detection of Bitewing Radiographs Using a Mobile Phone and an Artificial Neural Network: A Pilot Study

Abstract

This pilot study aimed to implement and assess the performance of an experimental artificial intelligence (AI) mobile phone app in the real-time detection of caries lesions on bitewing radiographs (BWRs) with the use of a back-facing mobile phone video camera. The author trained an EfficientDet-Lite1 artificial neural network using 190 radiographic images from the Internet. The trained model was deployed on a Google Pixel 6 mobile phone and used to detect caries on ten additional Internet BWRs. The sensitivity/precision/F1 scores ranged from 0.675/0.692/0.684 to 0.575/0.719/0.639 for the aggregate handheld detection of caries in static BWRs versus the stationary scanning of caries in a moving video of BWRs, respectively. Averaging the aggregate results, the AI app detected—in real time—62.5% of caries lesions on ten BWRs with a precision of 70.6% using the back-facing mobile phone video camera. When combined with the AI app’s relative ease of use and speed and the potential for global accessibility, this proof-of-concept study could quite literally place AI’s vast potential for improving patient care in dentists’ hands.