The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Deep Learning for the Radiographic Detection of Periodontal Bone Loss
Open accessAbstract
We applied deep convolutional neural networks (CNNs) to detect periodontal bone loss (PBL) on panoramic dental radiographs. We synthesized a set of 2001 image segments from panoramic radiographs. Our reference test was the measured % of PBL. A deep feed-forward CNN was trained and validated via 10-times repeated group shuffling. Model architectures and hyperparameters were tuned using grid search. The final model was a seven-layer deep neural network, parameterized by a total number of 4,299,651 weights. For comparison, six dentists assessed the image segments for PBL. Averaged over 10 validation folds the mean (SD) classification accuracy of the CNN was 0.81 (0.02). Mean (SD) sensitivity and specificity were 0.81 (0.04), 0.81 (0.05), respectively. The mean (SD) accuracy of the dentists was 0.76 (0.06), but the CNN was not statistically significant superior compared to the examiners (p = 0.067/t-test). Mean sensitivity and specificity of the dentists was 0.92 (0.02) and 0.63 (0.14), respectively. A CNN trained on a limited amount of radiographic image segments showed at least similar discrimination ability as dentists for assessing PBL on panoramic radiographs. Dentists’ diagnostic efforts when using radiographs may be reduced by applying machine-learning based technologies.
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LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
The Measurement of Observer Agreement for Categorical Data
Landis, J. R., & Koch, G. G. (1977) · Biometrics · doi:10.2307/2529310 · Cited by 81,148
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362
Measuring nominal scale agreement among many raters.
Fleiss, J. L. (1971) · Psychological Bulletin · doi:10.1037/h0031619 · Cited by 8,733
Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation
Powers, D. M. W. (2020) · arXiv · doi:10.48550/arxiv.2010.16061 · Cited by 5,440
STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies
Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L., Lijmer, J. G., Moher, D., Rennie, D., de Vet, H. C. W., et al. (2015) · BMJ · doi:10.1136/bmj.h5527 · Cited by 3,660
A systematic study of the class imbalance problem in convolutional neural networks
Buda, M., Maki, A., & Mazurowski, M. A. (2018) · Neural Networks · doi:10.1016/j.neunet.2018.07.011 · Cited by 3,088
Becker, A. S., Marcon, M., Ghafoor, S., Wurnig, M. C., Frauenfelder, T., & Boss, A. (2017) · Investigative Radiology · doi:10.1097/rli.0000000000000358 · Cited by 352
Cited by
Khanagar, S. B., Al-ehaideb, A., Maganur, P. C., Vishwanathaiah, S., Patil, S., Baeshen, H. A., Sarode, S. C., & Bhandi, S. (2021) · Journal of Dental Sciences · doi:10.1016/j.jds.2020.06.019 · Cited by 676
Convolutional neural networks for dental image diagnostics: A scoping review
Schwendicke, F., Golla, T., Dreher, M., & Krois, J. (2019) · Journal of Dentistry · doi:10.1016/j.jdent.2019.103226 · Cited by 513
Self-citationArtificial intelligence in dentistry-a review
Ding, H., Wu, J., Zhao, W., Matinlinna, J. P., Burrow, M. F., & Tsoi, J. K. H. (2023) · Frontiers in Dental Medicine · doi:10.3389/fdmed.2023.1085251 · Cited by 297
Samaranayake, L., Tuygunov, N., Schwendicke, F., Osathanon, T., Khurshid, Z., Boymuradov, S. A., & Cahyanto, A. (2025) · International Dental Journal · doi:10.1016/j.identj.2025.02.005 · Cited by 240
Self-citationApplications 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
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Metadata
- Authors
- Krois, J., Ekert, T., Meinhold, L., Golla, T., Kharbot, B., Wittemeier, A., Dörfer, C., & Schwendicke, F.
- Venue
- Scientific Reports
- Published
- 2019-06-11
- Volume
- 9
- Issue
- 1
- Pages
- 8495-8495
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W2951985572
- Cited by
- 517
- References
- 8
- FWCI
- 42.96
- Type
- journal article
- Language
- en
- Open access
- gold
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