# Self-Supervised Learning of Deep Embeddings for Classification and Identification of Dental Implants

## TL;DR

This paper introduces a deep learning system for identifying dental implants using self-supervised learning, achieving high accuracy and creating a new dataset for implant design.

## Contribution

The novel Masked Deep Embedding (MDE) pre-training method improves implant detection performance and introduces a labeled dental implant dataset.

## Key findings

- The proposed MDE method achieves a detection performance of AP = 96.1, outperforming supervised baselines.
- A new Implant Design Dataset (IDD) is created with detailed annotations for implant parts.
- Self-supervised learning is effectively applied to limited dental radiograph data.

## Abstract

This study proposes an automated system using deep learning-based object detection to identify implant systems, leveraging recent progress in self-supervised learning, specifically masked image modeling (MIM). We advocate for self-pre-training, emphasizing that its advantages when acquiring suitable pre-training data is challenging. The proposed Masked Deep Embedding (MDE) pre-training method, extending the masked autoencoder (MAE) transformer, significantly enhances dental implant detection performance compared to baselines. Specifically, the proposed method achieves a best detection performance of AP = 96.1, outperforming supervised ViT and MAE baselines by up to +2.9 AP. In addition, we address the absence of a comprehensive dataset for implant design, enhancing an existing dataset under dental expert supervision. This augmentation includes annotations for implant design, such as coronal, middle, and apical parts, resulting in a unique Implant Design Dataset (IDD). The contributions encompass employing self-supervised learning for limited dental radiograph data, replacing MAE’s patch reconstruction with patch embeddings, achieving substantial performance improvement in implant detection, and expanding possibilities through the labeling of implant design. This study paves the way for AI-driven solutions in implant dentistry, providing valuable tools for dentists and patients facing implant-related challenges.