The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography
Open accessAbstract
OBJECTIVES:: The distal root of the mandibular first molar occasionally has an extra root, which can directly affect the outcome of endodontic therapy. In this study, we examined the diagnostic performance of a deep learning system for classification of the root morphology of mandibular first molars on panoramic radiographs. Dental cone-beam CT (CBCT) was used as the gold standard. METHODS:: CBCT images and panoramic radiographs of 760 mandibular first molars from 400 patients who had not undergone root canal treatments were analyzed. Distal roots were examined on CBCT images to determine the presence of a single or extra root. Image patches of the roots were segmented from panoramic radiographs and applied to a deep learning system, and its diagnostic performance in the classification of root morphplogy was examined. RESULTS:: Extra roots were observed in 21.4% of distal roots on CBCT images. The deep learning system had diagnostic accuracy of 86.9% for the determination of whether distal roots were single or had extra roots. CONCLUSIONS:: The deep learning system showed high accuracy in the differential diagnosis of a single or extra root in the distal roots of mandibular first molars.
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Zhao, X., Wu, Y., Song, G., Li, Z., Zhang, Y., & Fan, Y. (2018) · Medical Image Analysis · doi:10.1016/j.media.2017.10.002 · Cited by 813
Byrne, M. F., Chapados, N., Soudan, F., Oertel, C., Linares Pérez, M., Kelly, R., Iqbal, N., Chandelier, F., & Rex, D. K. (2019) · Gut · doi:10.1136/gutjnl-2017-314547 · Cited by 635
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Lee, H., Tajmir, S., Lee, J., Zissen, M., Yeshiwas, B. A., Alkasab, T. K., Choy, G., & Do, S. (2017) · Journal of Digital Imaging · doi:10.1007/s10278-017-9955-8 · Cited by 452
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Song, Q., Zhao, L., Luo, X., & Dou, X. (2017) · Journal of Healthcare Engineering · doi:10.1155/2017/8314740 · Cited by 422
Classification of CT brain images based on deep learning networks
Gao, X. W., Hui, R., & Tian, Z. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2016.10.007 · Cited by 351
Automated Detection of Diabetic Retinopathy using Deep Learning.
Lam, C., Yi, D., Guo, M. G., & Lindsey, T. (2018) · PubMed Central · Cited by 326
Trebeschi, S., van Griethuysen, J. J. M., Lambregts, D. M. J., Lahaye, M. J., Parmar, C., Bakers, F. C. H., Peters, N. H. G. M., Beets-Tan, R. G. H., & Aerts, H. J. W. L. (2017) · Scientific Reports · doi:10.1038/s41598-017-05728-9 · Cited by 292
Wang, H., Zhou, Z., Li, Y., Chen, Z., Lu, P., Wang, W., Liu, W., & Yu, L. (2017) · EJNMMI Research · doi:10.1186/s13550-017-0260-9 · Cited by 249
A preliminary examination of the diagnostic value of deep learning in hip osteoarthritis
Xue, Y., Zhang, R., Deng, Y., Chen, K., & Jiang, T. (2017) · PLOS ONE · doi:10.1371/journal.pone.0178992 · Cited by 198
Cited by
Applications of Artificial Intelligence in Dental Medicine: A Critical Review
Sitaras, S., Tsolakis, I. A., Gelsini, M., Tsolakis, A. I., Schwendicke, F., Wolf, T. G., & Perlea, P. (2025) · International Dental Journal · doi:10.1016/j.identj.2024.11.009 · Cited by 42
Metadata
- Authors
- Hiraiwa, T., Ariji, Y., Fukuda, M., Kise, Y., Nakata, K., Katsumata, A., Fujita, H., & Ariji, E.
- Venue
- Dentomaxillofacial Radiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-03
- Volume
- 48
- Issue
- 3
- Pages
- 20180218
- ISSN
- 0250-832X (ISSN-L)
- 1476-542X
- OpenAlex
- W2899380081
- Cited by
- 319
- References
- 14
- FWCI
- 21.97
- Type
- journal article
- Language
- en
- Open access
- green
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- Hiraiwa, T., Ariji, Y., Fukuda, M., Kise, Y., Nakata, K., Katsumata, A., Fujita, H., & Ariji, E. (2019). A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography. Dentomaxillofacial Radiology. https://doi.org/10.1259/dmfr.20180218
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