Oral radiology · 2024
Evaluation of the mandibular canal and the third mandibular molar relationship by CBCT with a deep learning approach.
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Ünal Suay Yağmur, Namdar Pekiner Filiz
Аннотация
OBJECTIVE: The mandibular canal (MC) houses the inferior alveolar nerve. Extraction of the mandibular third molar (MM3) is a common dental surgery, often complicated by nerve damage. CBCT is the most effective imaging method to assess the relationship between MM3 and MC. With advancements in artificial intelligence, deep learning has shown promising results in dentistry. The aim of this study is to evaluate the MC-MM3 relationship using CBCT and a deep learning technique, as well as to automatically segment the mandibular impacted third molar, mandibular canal, mental and mandibular foramen.
METHODS: This retrospective study analyzed CBCT data from 300 patients. Segmentation was used for labeling, dividing the data into training (n = 270) and test (n = 30) sets. The nnU-NetV2 architecture was employed to develop an optimal deep learning model. The model's success was validated using the test set, with metrics including accuracy, sensitivity, precision, Dice score, Jaccard index, and AUC.
RESULTS: For the MM3 annotated on CBCT, the accuracy was 0.99, sensitivity 0.90, precision 0.85, Dice score 0.85, Jaccard index 0.78, AUC value 0.95. In MC evaluation, accuracy was 0.99, sensitivity 0.75, precision 0.78, Dice score 0.76, Jaccard index 0.62, AUC value 0.88. For the evaluation of mental foramen; accuracy 0.99, sensitivity 0.64, precision 0.66, Dice score 0.64, Jaccard index 0.57, AUC value 0.82. In the evaluation of mandibular foramen, accuracy was found to be 0.99, sensitivity 0.79, precision 0.68, Dice score 0.71, and AUC value 0.90. Evaluating the MM3-MC relationship, the model showed an 80% correlation with observer assessments.
CONCLUSION: The nnU-NetV2 deep learning architecture reliably identifies the MC-MM3 relationship in CBCT images, aiding in diagnosis, surgical planning, and complication prediction.
METHODS: This retrospective study analyzed CBCT data from 300 patients. Segmentation was used for labeling, dividing the data into training (n = 270) and test (n = 30) sets. The nnU-NetV2 architecture was employed to develop an optimal deep learning model. The model's success was validated using the test set, with metrics including accuracy, sensitivity, precision, Dice score, Jaccard index, and AUC.
RESULTS: For the MM3 annotated on CBCT, the accuracy was 0.99, sensitivity 0.90, precision 0.85, Dice score 0.85, Jaccard index 0.78, AUC value 0.95. In MC evaluation, accuracy was 0.99, sensitivity 0.75, precision 0.78, Dice score 0.76, Jaccard index 0.62, AUC value 0.88. For the evaluation of mental foramen; accuracy 0.99, sensitivity 0.64, precision 0.66, Dice score 0.64, Jaccard index 0.57, AUC value 0.82. In the evaluation of mandibular foramen, accuracy was found to be 0.99, sensitivity 0.79, precision 0.68, Dice score 0.71, and AUC value 0.90. Evaluating the MM3-MC relationship, the model showed an 80% correlation with observer assessments.
CONCLUSION: The nnU-NetV2 deep learning architecture reliably identifies the MC-MM3 relationship in CBCT images, aiding in diagnosis, surgical planning, and complication prediction.