Prediction of single implant pink esthetic scores in the esthetic zone using deep learning: A proof of concept.

Abstract

Objectives: This study aimed to develop a deep learning (DL) model for the predictive esthetic evaluation of single-implant treatments in the esthetic zone.

Methods: A total of 226 samples, each comprising three intraoral photographs and 12 clinical features, were collected for proof of concept. Labels were determined by a prosthodontic specialist using the pink esthetic score (PES). A DL model was developed to predict PES based on input images and clinical data. The performance was assessed and compared with that of two other models.

Results: The DL model achieved an average mean absolute error (MAE) of 1.3597, average root mean squared error (MSE) of 1.8324, a Pearson correlation of 0.6326, and accuracies of 65.93% and 85.84% for differences between predicted and ground truth values no larger than 1 and 2, respectively. An ablation study demonstrated that incorporating all input features yielded the best performance, with the proposed model outperforming comparison models.

Conclusions: DL demonstrates potential for providing acceptable preoperative PES predictions for single implant-supported prostheses in the esthetic zone. Ongoing efforts to collect additional samples and clinical features aim to further enhance the model's performance.

Clinical significance: The DL model supports dentists in predicting esthetic outcomes and making informed treatment decisions before implant placement. It offers a valuable reference for inexperienced and general dentists to identify esthetic risk factors, thereby improving implant treatment outcomes.