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 -