Use of AI for Identifying Instrument Separation Risks

References

[1] Aminoshariae, A., Kulild, J., & Nagendrababu, V. (2021). Artificial intelligence in endodontics: Current applications and future directions. Journal of Endodontics, 47(9), 1352–1357.

[2] Singh, S. (2022). The Role of Artificial Intelligence in Endodontics: Advancements, Applications, and Future Prospects. Well Testing Journal, 31(1), 125-144.

[3] Iqbal, M. K., Kohli, M. R., & Kim, J. S. (2006). A retrospective clinical study of incidence of root canal instrument separation in an endodontics graduate program: A PennEndo database study. Journal of Endodontics, 32(11), 1048–1052.

[4] Jung, W., Lee, K. E., Suh, B. J., Seok, H., & Lee, D. W. (2022). Deep learning for osteoarthritis classification in temporomandibular joint. Oral Diseases, 29(3), 1050–1058.

[5] Martin, B., Zelada, G., Varela, P., Bahillo, J. G., Magán, F., Ahn, S., & Rodriguez, C. (2003). Factors influencing the fracture of nickel–titanium rotary instruments. International Endodontic Journal, 36(4), 262–266.

[6] Nangia, D., & Vats, P. (2022). Clinical applications of artificial intelligence in endodontics. International Journal of Oral Health, 5(1), 1–3.

[7] Özbay, Y., Kazangirler, B. Y., Özcan, C., & Pekince, A. (2021, April). Detection of the separated endodontic instrument on periapical radiographs using a deep learning-based convolutional neural network algorithm. Australian Endodontic Journal, 50(1), 131–139.

[8] Parashos, P., Gordon, I., & Messer, H. H. (2004). The factors affecting defects in rotary nickel-titanium endodontic instruments after clinical use. Journal of Endodontics, 30(10), 722–725.

[9] Pethani, F. (2021). Promises and perils of artificial intelligence in dentistry. Australian Dental Journal, 66(2), 124–135.

[10] Plotino, G., Grande, N. M., Cordaro, M., Testarelli, L., & Gambarini, G. (2009). A review of cyclic fatigue testing of nickel-titanium rotary instruments. Journal of Endodontics, 35(11), 1469–1476.

[11] Rosen, E., Venezia, N. B., Azizi, H., Kamburoglu, K., Meirowitz, A., & Ziv-Baran, T. (2016). Comparing cone-beam computed tomography to periapical radiography in detection of separated instruments retained in the apical third of root canal-filled teeth. Journal of Endodontics, 42(7), 1035–1039.

[12] Solomonov, M., Webber, M., & Keinan, D. (2015). Fractured endodontic instrument: A clinical dilemma Retrieve, bypass or entomb? Journal of the Michigan Dental Association, 97(9), 44–46.

[13] Sousa, V. C., Estrela, L. R. de A., Bueno, M. R., Azevedo, B. C., Guedes, O. A., & Estrela, C. (2025). A novel methodology for detecting separated endodontic instruments with artifact suppression and color map algorithms in CBCT post-processing software. Scientific Reports, 15, 6088.

[14] Suter, B., Lussi, A., & Sequeira, P. (2005). The probability of removing fractured instruments from root canals. International Endodontic Journal, 38(2), 112–123.

[15] Zhou, T., Ye, X., Lu, H., Zheng, X., Qiu, S., Liu, Y., et al. (2022). Dense convolutional network and its application in medical image analysis. Biomed Research International, 2022, Article 2384830.

@article{1710594,
      author = {Elena Petrova, Markus Schneider},
      title = {Use of AI for Identifying Instrument Separation Risks},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {6},
      number = {3},
      pages = {322-328},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1710594.pdf},
      abstract = {Instrument separation in endodontic treatment constitutes a major problem that can hamper successful treatment and long-term survival. Usually, the risk process depends on a clinician's experience, X-ray evaluation, and his own judgment, which are very subjective and may be imprecise in predicting a possible instrument failure. Nowadays, artificial intelligence (AI) presents itself as a good solution, capitalizing on the capacity to analyze massive datasets, unearth hidden patterns, and generate predictive models with greater accuracy. Machine learning and deep learning methods may be able to synthesize parameters such as canal morphology, instrument design, operator variables, and clinical imaging in order to arrive at a prediction as to the chance of separation. Such AI-based decision tools can assist diagnostic precision, reducing errors, and allowing interventions leading to better clinical outcomes. This article reviews AI application in the prediction of risk factors for instrument separation, laying emphasis on the potential benefits, limitations, and consequences for the future of endodontic practice.},
      keywords = {Artificial Intelligence; Endodontics; Instrument Separation; Risk Prediction; Machine Learning; Deep Learning; Canal Morphology; Clinical Decision Support.},
      month = {September},
      doi = {https://doi.org/10.64388/IREV6I3-1710594-7458}
  }