Department of Computer Science Electrical Engineering, University of Missouri(2), Kansas City, MO, USA.
Department of Computer Science Electrical Engineering, University of Missouri(2), Kansas City, MO, USA; Department of Computing and Informatics, Saudi Electronic University, Saudi Arabia.
Comput Biol Med. 2022 Sep;148:105829. doi: 10.1016/j.compbiomed.2022.105829. Epub 2022 Jul 16.
Panoramic radiographs are an integral part of effective dental treatment planning, supporting dentists in identifying impacted teeth, infections, malignancies, and other dental issues. However, screening for anomalies solely based on a dentist's assessment may result in diagnostic inconsistency, posing difficulties in developing a successful treatment plan. Recent advancements in deep learning-based segmentation and object detection algorithms have enabled the provision of predictable and practical identification to assist in the evaluation of a patient's mineralized oral health, enabling dentists to construct a more successful treatment plan. However, there has been a lack of efforts to develop collaborative models that enhance learning performance by leveraging individual models. The article describes a novel technique for enabling collaborative learning by incorporating tooth segmentation and identification models created independently from panoramic radiographs. This collaborative technique permits the aggregation of tooth segmentation and identification to produce enhanced results by recognizing and numbering existing teeth (up to 32 teeth). The experimental findings indicate that the proposed collaborative model is significantly more effective than individual learning models (e.g., 98.77% vs. 96% and 98.44% vs.91% for tooth segmentation and recognition, respectively). Additionally, our models outperform the state-of-the-art segmentation and identification research. We demonstrated the effectiveness of collaborative learning in detecting and segmenting teeth in a variety of complex situations, including healthy dentition, missing teeth, orthodontic treatment in progress, and dentition with dental implants.
全景片是有效牙科治疗计划的一个组成部分，它可以帮助牙医识别阻生牙、感染、恶性肿瘤和其他牙科问题。然而，仅基于牙医评估来筛查异常可能会导致诊断不一致，从而难以制定成功的治疗计划。基于深度学习的分割和目标检测算法的最新进展，为提供可预测和实用的识别提供了可能，有助于评估患者的矿化口腔健康，使牙医能够制定更成功的治疗计划。然而，开发协作模型的工作还不够，这些模型可以通过利用个体模型来提高学习性能。本文描述了一种从全景片中独立创建的牙齿分割和识别模型，通过协作学习来实现的新方法。这种协作技术允许牙齿分割和识别的聚合，通过识别和编号现有的牙齿（最多 32 颗）来产生增强的结果。实验结果表明，所提出的协作模型明显优于单个学习模型（例如，在牙齿分割和识别方面，分别为 98.77%比 96%和 98.44%比 91%）。此外，我们的模型在分割和识别方面优于最先进的研究。我们在各种复杂情况下展示了协作学习在检测和分割牙齿方面的有效性，包括健康的牙列、缺失的牙齿、正在进行的正畸治疗和带有种植牙的牙列。
