Estimation of Alveolar Bone Loss in Periodontitis Using Machine Learning

牙周炎 牙槽 牙科 临床附着丧失 医学 计算机科学 牙缺失 人工智能 口腔健康
作者
Nektarios Tsoromokos,Sarah Parinussa,Frank Claessen,David Anssari Moin,Bruno G. Loos
出处
期刊:International Dental Journal [Elsevier BV]
卷期号:72 (5): 621-627 被引量:27
标识
DOI:10.1016/j.identj.2022.02.009
摘要

The objective of this research was to perform a pilot study to develop an automatic analysis of periapical radiographs from patients with and without periodontitis for the percentage alveolar bone loss (ABL) on the approximal surfaces of teeth using a supervised machine learning model, that is, convolutional neural networks (CNN).A total of 1546 approximal sites from 54 participants on mandibular periapical radiographs were manually annotated (MA) for a training set (n = 1308 sites), a validation set (n = 98 sites), and a test set (n = 140 sites). The training and validation sets were used for the development of a CNN algorithm. The algorithm recognised the cemento-enamel junction, the most apical extent of the alveolar crest, the apex, and the surrounding alveolar bone.For the total of 140 images in the test set, the CNN scored a mean of 23.1 ± 11.8 %ABL, whilst the corresponding value for MA was 27.8 ± 13.8 %ABL. The intraclass correlation (ICC) was 0.601 (P < .001), indicating moderate reliability. Further subanalyses for various tooth types and various bone loss patterns showed that ICCs remained significant, although the algorithm performed with excellent reliability for %ABL on nonmolar teeth (incisors, canines, premolars; ICC = 0.763).A CNN trained algorithm on radiographic images showed a diagnostic performance with moderate to good reliability to detect and quantify %ABL in periapical radiographs.
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