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[Deep learning-based SINet-V2 for localization of root canal orifices in maxillary molars]
Qiyun Shen1, Yujie Fu, Kefan Zhou
1Department of Endodontics, Shanghai Tongji Stomatological Hospital and Dental School, Tongji University; Shanghai Engineering Research Center of Tooth Restoration and Regeneration and Tongji Research Institute of Stomatology. Shanghai 200072, China.
Purpose:
To construct a deep learning-based model for root canal orifice localization, aiming to provide a reference for clinically efficient and precise root canal orifice identification.
Methods:
Eighty maxillary molars were collected. After pulp chamber opening and cleaning, K-files were inserted to confirm root canal orifice positions, and microphotographs of the pulp chamber floor were captured to establish a conventional root canal orifice dataset. A random function was used to allocate 64 samples to the training set and 16 samples to the test set. Manual annotations of root canal orifices in pulp chamber floor images by an endodontic expert using MITK software served as the reference standard. The training set was input into the search identification network version 2 (SINet-V2) to develop a conventional root canal orifice localization model (NCO-LM). Model performance was evaluated against the reference standard using the following metrics: mean intersection over union (MIoU), root canal number prediction accuracy (NER), mean absolute error (MAE), and average connected domain center distance difference(MDis). Subsequently, light-cured composite resin was applied to cover the root canal orifices of the same 80 molars to simulate calcified pulp chambers. Photographs of the simulated calcified pulp chamber floors were acquired to create a calcified root canal orifice dataset. A calcified root canal orifice localization model (CCO-LM) was trained and tested following the NCO-LM methodology.
Results:
Both NCO-LM and CCO-LM demonstrated high similarity to the reference standard with minimal localization deviations. NCO-LM achieved an MIoU of (87.42±5.47)%, MAE of (0.000 594±0.000 351), MDis of (1.63±1.40) pixels, and NER of 98.44%. CCO-LM showed an MIoU of (91.87±4.03) %, MAE of (0.000 801±0.000 543), MDis of (1.91±1.31) pixels, and NER of 96.25%.
Conclusions:
NCO-LM and CCO-LM models based on the SINet-V2 network, which are capable of accurately locating both conventional root canal orifices and hidden orifices covered by simulated calcifications in pulp chamber floor images. These findings provide a preliminary theoretical foundation for artificial intelligence-assisted real-time clinical root canal orifice localization.
