Periapical periodontitis may manifest as a radiographic lesion radiographically. Periapical
lesions are amongst the most common dental pathologies that present as periapical
radiolucencies on panoramic radiographs. The objective of this research is to assess
the diagnostic accuracy of an artificial intelligence (AI) model based on U-2-Net
architecture in the detection of periapical lesions on dental panoramic radiographs
and to determine whether they can be useful in aiding clinicians with diagnosis of
periapical lesions and improving their clinical workflow. 400 panoramic radiographs
that included at least one periapical radiolucency were selected retrospectively.
780 periapical radiolucencies in these anonymized radiographs were manually labeled
by two independent examiners. These radiographs were later used to train the AI model
based on U-2-Net architecture trained using a deep supervision algorithm. An AI model
based on the U-2-Net architecture was implemented. The model achieved a dice score
of 0.8 on the validation set and precision, recall, and F1-score of 0.82, 0.77, and
0.8 respectively on the test set. This study has shown that an AI model based on U-2-Net
architecture can accurately diagnose periapical lesions on panoramic radiographs.
The research provides evidence that AI-based models have promising applications as
adjunct tools for dentists in diagnosing periapical radiolucencies and procedure planning.
Further studies with larger data sets would be required to improve the diagnostic
accuracy of AI-based detection models.
