Success of Caries and Periapical Infection Diagnoses on Panoramic images: Artificial Intelligence vsJunior and Specialist Dentists

Abstract Purpose: The aim of this study is to evaluate the success rate of radiological diagnoses regarding caries and periapical infection, comparing an artificial intelligence application against junior dentists, based on the valid determinations by specialist dentists. Methods: In the initial stage of the study, 2 specialist dentists evaluated the presence of caries and periapical lesions on 500 digital panoramic radiographs, and the detection time was recorded in seconds. In the second stage, 3 junior dentists and an artificial intelligence application performed diagnoses on the same panoramic radiographs, and the diagnostic results and durations were recorded in seconds. Results: The artificial intelligence and the three junior dentists, respectively, detected dental caries at an SEN of 0.907,0.889,0.491,0.907; a SPEC of 0.760,0.740,0.454,0.769660; a PPV of 0.693,0.470,0.155,0.666; an NPV of 0.505,0.415,0.275,0.367 and an F1-score of 0.786,0.615,0.236,0.768. The artificial intelligence and the three junior dentists respectively detected periapical lesions at an SEN of 0.973,0.962,0.758,0.958; a SPEC of 0.629,0.421,0.404,0.621; a PPV of 0.861,0.651,0.312,0.648; an NPV of 0.689,0.673,0.278,0.546 and an F1-score of 0.914,0.777,0.442,0.773. Conclusion: The artificial intelligence application gave more accurate results, especially in detecting periapical lesions. On the other hand, in caries detection, the underdiagnosis rate was high for both artificial intelligence and junior dentists. Regarding the evaluation time needed, artificial intelligence performed faster, on average.