Health Technology/clinical-trials

Deep Learning Model for CBCT Root Canal Analysis Enters Validation Trial — Implications for Endodontic Practice

247 Health News Editorial

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21 May 2026

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3 min read

·clinical-trials

Cairo University launches validation study for deep learning model that automatically analyses root canal curvature in CBCT scans. The technology could standardise endodontic case difficulty assessment and reduce diagnostic variability.

Originally reported by ClinicalTrials.gov

References

  1. 1.Cairo University. (2024). Development and Diagnostic Accuracy of a Deep Learning Model for Root Canal Curvature Analysis in Mandibular Molars Using CBCT Scans. ClinicalTrials.gov. https://clinicaltrials.gov/study/NCT07587060
  2. 2.Patel, S., Durack, C., Abella, F., Roig, M., Shemesh, H., Lambrechts, P., & Lemberg, K. (2015). European Society of Endodontology position statement: the use of CBCT in endodontics. International Endodontic Journal, 48(1), 3-25. https://doi.org/10.1111/iej.12129
  3. 3.Schäfer, E., & Vlassis, M. (2004). Comparative investigation of two rotary nickel-titanium instruments: ProTaper versus RaCe. Part 2. Cleaning effectiveness and shaping ability in severely curved root canals of extracted teeth. International Endodontic Journal, 37(4), 239-248. https://doi.org/10.1111/j.1365-2591.2004.00889.x
  4. 4.Estrela, C., Bueno, M. R., Leles, C. R., Azevedo, B., & Azevedo, J. R. (2008). Accuracy of cone beam computed tomography and panoramic and periapical radiography for detection of apical periodontitis. Journal of Endodontics, 34(3), 273-279. https://doi.org/10.1016/j.joen.2007.12.001
  5. 5.Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L., ... & de Vet, H. C. (2015). STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ, 351, h5527. https://doi.org/10.1136/bmj.h5527
  6. 6.Pruett, J. P., Clement, D. J., & Carnes Jr, D. L. (1997). Cyclic fatigue testing of nickel-titanium endodontic instruments. Journal of Endodontics, 23(2), 77-85. https://doi.org/10.1016/S0099-2399(97)80250-6
  7. 7.Lofthag-Hansen, S., Huumonen, S., Gröndahl, K., & Gröndahl, H. G. (2007). Limited cone-beam CT and intraoral radiography for the diagnosis of periapical pathology. Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontology, 103(1), 114-119. https://doi.org/10.1016/j.tripleo.2006.10.001
  8. 8.Cotton, T. P., Geisler, T. M., Holden, D. T., Schwartz, S. A., & Schindler, W. G. (2007). Endodontic applications of cone-beam volumetric tomography. Journal of Endodontics, 33(9), 1121-1132. https://doi.org/10.1016/j.joen.2007.06.011
  9. 9.Hung, K., Montalvao, C., Tanaka, R., Kawai, T., & Bornstein, M. M. (2020). The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review. Dentomaxillofacial Radiology, 49(1), 20190107. https://doi.org/10.1259/dmfr.20190107
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