Prediction of pink esthetic score using deep learning: A proof of concept

Abstract

<h3>Objectives</h3><p>This study aimed to develop a deep learning (DL) model for the predictive esthetic evaluation of single-implant treatments in the esthetic zone.</p><h3>Methods</h3><p>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.</p><h3>Results</h3><p>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.</p><h3>Conclusions</h3><p>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.</p><h3>Clinical significance</h3><p>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.</p>