Mureșanu Sorana, Hedeșiu Mihaela, Iacob Liviu, Eftimie Radu, Olariu Eliza, Dinu Cristian, Jacobs Reinhilde
Department of Oral and Maxillofacial Surgery and Radiology, Iuliu Hațieganu University of Medicine and Pharmacy, 32 Clinicilor Street, 400006 Cluj-Napoca, Romania.
Department of Computer Science, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.
Diagnostics (Basel). 2024 Oct 21;14(20):2336. doi: 10.3390/diagnostics14202336.
The integration of AI into dentistry holds promise for improving diagnostic workflows, particularly in the detection of dental pathologies and pre-radiotherapy screening for head and neck cancer patients. This study aimed to develop and validate an AI model for detecting various dental conditions, with a focus on identifying teeth at risk prior to radiotherapy. A YOLOv8 model was trained on a dataset of 1628 annotated panoramic radiographs and externally validated on 180 radiographs from multiple centers. The model was designed to detect a variety of dental conditions, including periapical lesions, impacted teeth, root fragments, prosthetic restorations, and orthodontic devices. The model showed strong performance in detecting implants, endodontic treatments, and surgical devices, with precision and recall values exceeding 0.8 for several conditions. However, performance declined during external validation, highlighting the need for improvements in generalizability. YOLOv8 demonstrated robust detection capabilities for several dental conditions, especially in training data. However, further refinement is needed to enhance generalizability in external datasets and improve performance for conditions like periapical lesions and bone loss.
将人工智能（AI）整合到牙科领域有望改善诊断流程，特别是在牙科病变检测以及头颈癌患者放疗前筛查方面。本研究旨在开发并验证一种用于检测各种牙科病症的AI模型，重点是在放疗前识别有风险的牙齿。一个YOLOv8模型在包含1628张标注全景X光片的数据集上进行训练，并在来自多个中心的180张X光片上进行外部验证。该模型旨在检测多种牙科病症，包括根尖周病变、阻生牙、牙根碎片、修复体以及正畸装置。该模型在检测种植体、牙髓治疗和手术器械方面表现出色，在几种情况下精确率和召回率值超过0.8。然而，在外部验证期间性能有所下降，凸显了提高通用性的必要性。YOLOv8在检测多种牙科病症方面展现出强大的能力，尤其是在训练数据中。然而，需要进一步优化以增强在外部数据集中的通用性，并提高对根尖周病变和骨质流失等病症的检测性能。
