Both deep learning and integrated models perform well in predicting dental implant fate

Dental implants have a high success rate, but some people experience dental implant failure. It's estimated that about 5 to 10 per cent of dental implants fail, either shortly after a procedure or months or years later.

A study was conducted to investigate the feasibility of predicting dental implant loss risk with deep learning (DL) based on preoperative cone-beam computed tomography.

Six hundred and three patients who underwent implant surgery (279 high-risk patients who did and 324 low-risk patients who did not experience implant loss within 5 years) from January 2012 to January 2020 were enrolled. Three models, a logistic regression clinical model (CM) based on clinical features, a deep learning model based on radiography features, and an integrated model (IM) developed by combining a clinical model with deep learning, were developed to predict the 5-year implant loss risk. The area under the receiver operating characteristic curve (AUC) was used to evaluate the model performance. Time to implant loss was considered for both groups, and Kaplan–Meier curves were created and compared by the log-rank test.

Results:

The integrated model exhibited the best performance in predicting implant loss risk, followed by the deep learning model and the clinical model

Thus, this study offers preliminary evidence that both the deep learning model and integrated model performed well in predicting implant fate within 5 years and thus may greatly facilitate implant practitioners in assessing preoperative risks.

Reference:

Predicting the Risk of Dental Implant Loss Using Deep Learning by Nannan Huang et al. published in the Journal of Clinical Periodontology.

https://doi.org/10.1111/jcpe.13689