Neumayr Julia, Frenkel Elisabeth, Schwarzmaier Julia, Ammar Nour, Kessler Andreas, Schwendicke Falk, Kühnisch Jan, Dujic Helena
Department of Conservative Dentistry and Periodontology, LMU University Hospital, Klinikum der Ludwig-Maximilians-Universität München, Klinik für Zahnerhaltung und Parodontologie, LMU, Goethestraße 70, Munich 80336, Germany.
Department of Conservative Dentistry and Periodontology, LMU University Hospital, Klinikum der Ludwig-Maximilians-Universität München, Klinik für Zahnerhaltung und Parodontologie, LMU, Goethestraße 70, Munich 80336, Germany; Department of Prosthetic Dentistry, Faculty of Medicine, Center for Dental Medicine, Medical Center-University of Freiburg, University of Freiburg, Freiburg, Germany.
J Dent. 2024 Sep;148:105228. doi: 10.1016/j.jdent.2024.105228. Epub 2024 Jul 5.
This ex vivo diagnostic study aimed to externally validate an open-access artificial intelligence (AI)-based model for the detection, classification, localisation and segmentation of enamel/molar incisor hypomineralisation (EH/MIH).
An independent sample of web images showing teeth with (n = 277) and without (n = 178) EH/MIH was evaluated by a workgroup of dentists whose consensus served as the reference standard. Then, an AI-based model was used for the detection of EH/MIH, followed by automated classification and segmentation of the findings (test method). The accuracy (ACC), sensitivity (SE), specificity (SP) and area under the curve (AUC) were determined. Furthermore, the correctness of EH/MIH lesion localisation and segmentation was evaluated.
An overall ACC of 94.3 % was achieved for image-based detection of EH/MIH. Cross-classification of the AI-based class prediction and the reference standard resulted in an agreement of 89.2 % for all diagnostic decisions (n = 594), with an ACC between 91.4 % and 97.8 %. The corresponding SE and SP values ranged from 81.7 % to 92.8 % and 91.9 % to 98.7 %, respectively. The AUC varied between 0.894 and 0.945. Image size had only a limited impact on diagnostic performance. The AI-based model correctly predicted EH/MIH localisation in 97.3 % of cases. For the detected lesions, segmentation was fully correct in 63.4 % of all cases and partially correct in 33.9 %.
This study documented the promising diagnostic performance of an open-access AI tool in the detection and classification of EH/MIH in external images.
Externally validated AI-based diagnostic methods could facilitate the detection of EH/MIH lesions in dental photographs.
本体外诊断研究旨在对一种基于人工智能（AI）的开放获取模型进行外部验证，该模型用于检测、分类、定位和分割釉质/磨牙切牙矿化不全（EH/MIH）。
一组牙医对显示有（n = 277）和无（n = 178）EH/MIH的牙齿的网络图像独立样本进行评估，其共识作为参考标准。然后，使用基于AI的模型检测EH/MIH，接着对结果进行自动分类和分割（测试方法）。确定准确性（ACC）、敏感性（SE）、特异性（SP）和曲线下面积（AUC）。此外，评估EH/MIH病变定位和分割的正确性。
基于图像检测EH/MIH的总体ACC为94.3%。基于AI的类别预测与参考标准的交叉分类在所有诊断决策（n = 594）中达成了89.2%的一致性，ACC在91.4%至97.8%之间。相应的SE和SP值分别在81.7%至92.8%和91.9%至98.7%之间。AUC在0.894至0.945之间变化。图像大小对诊断性能的影响有限。基于AI的模型在97.3%的病例中正确预测了EH/MIH的定位。对于检测到的病变，分割在所有病例中有63.4%完全正确，33.9%部分正确。
本研究证明了一种开放获取的AI工具在检测和分类外部图像中的EH/MIH方面具有良好的诊断性能。
经过外部验证的基于AI的诊断方法可有助于在牙科照片中检测EH/MIH病变。
