AI technology may significantly improve prediction of pulp exposure from radiographic information

Results

The results showed that AI surpassed the performance of students in all groups with an F1-score of 0.71 (P < 0.001). The students’ F1-score in GX-ray+AI and GX-ray+clinical data+AI with model prediction (0.61 and 0.61 respectively) was slightly higher than the F1-score in GX-ray and GX-ray+clinical data (0.58 and 0.59 respectively) with a borderline statistical significance of P = 0.054.

Although the AI model had much better performance than all groups, the participants when given AI prediction, benefited only ‘slightly’. AI technology seems promising, but more explainable AI predictions along with a 'learning curve' are warranted.

Reference:

Shaqayeq Ramezanzade, Tudor Laurentiu Dascalu, Bulat Ibragimov, Azam Bakhshandeh, Lars Bjørndal. Prediction of pulp exposure before caries excavation using artificial intelligence: Deep learning-based image data versus standard dental radiographs,

Journal of Dentistry, Volume 138, 2023, 104732, ISSN 0300-5712, https://doi.org/10.1016/j.jdent.2023.104732.

(https://www.sciencedirect.com/science/article/pii/S0300571223003184)

Keywords:

AI, technology, may, significantly, improve, prediction, pulp, exposure, radiographic, Artificial intelligence; Endodontics; Pulpitis; Machine learning; Dental caries, information