TY - JOUR
T1 - Prediction of pulp exposure before caries excavation using artificial intelligence
T2 - Deep learning-based image data versus standard dental radiographs
AU - Ramezanzade, Shaqayeq
AU - Dascalu, Tudor Laurentiu
AU - Ibragimov, Bulat
AU - Bakhshandeh, Azam
AU - Bjørndal, Lars
N1 - Publisher Copyright:
© 2023 The Authors
PY - 2023/11
Y1 - 2023/11
N2 - Objectives: The objective was to examine the effect of giving Artificial Intelligence (AI)-based radiographic information versus standard radiographic and clinical information to dental students on their pulp exposure prediction ability. Methods: 292 preoperative bitewing radiographs from patients previously treated were used. A multi-path neural network was implemented. The first path was a convolutional neural network (CNN) based on ResNet-50 architecture. The second path was a neural network trained on the distance between the pulp and lesion extracted from X-ray segmentations. Both paths merged and were followed by fully connected layers that predicted the probability of pulp exposure. A trial concerning the prediction of pulp exposure based on radiographic input and information on age and pain was conducted, involving 25 dental students. The data displayed was divided into 4 groups (G): GX-ray, GX-ray+clinical data, GX-ray+AI, GX-ray+clinical data+AI. 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. Conclusions: 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.
AB - Objectives: The objective was to examine the effect of giving Artificial Intelligence (AI)-based radiographic information versus standard radiographic and clinical information to dental students on their pulp exposure prediction ability. Methods: 292 preoperative bitewing radiographs from patients previously treated were used. A multi-path neural network was implemented. The first path was a convolutional neural network (CNN) based on ResNet-50 architecture. The second path was a neural network trained on the distance between the pulp and lesion extracted from X-ray segmentations. Both paths merged and were followed by fully connected layers that predicted the probability of pulp exposure. A trial concerning the prediction of pulp exposure based on radiographic input and information on age and pain was conducted, involving 25 dental students. The data displayed was divided into 4 groups (G): GX-ray, GX-ray+clinical data, GX-ray+AI, GX-ray+clinical data+AI. 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. Conclusions: 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.
KW - Artificial intelligence
KW - Dental caries
KW - Endodontics
KW - Machine learning
KW - Pulpitis
UR - http://www.scopus.com/inward/record.url?scp=85173152166&partnerID=8YFLogxK
U2 - 10.1016/j.jdent.2023.104732
DO - 10.1016/j.jdent.2023.104732
M3 - Journal article
C2 - 37778496
AN - SCOPUS:85173152166
VL - 138
JO - Journal of Dentistry
JF - Journal of Dentistry
SN - 0300-5712
M1 - 104732
ER -