Journal of dentistry · 2023
Prediction of pulp exposure before caries excavation using artificial intelligence: Deep learning-based image data versus standard dental radiographs.
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Ramezanzade Shaqayeq, Dascalu Tudor Laurentiu, Ibragimov Bulat, Bakhshandeh Azam, Bjørndal Lars
Аннотация
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.
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.