School of Medicine and Dentistry, Griffith University, Gold Coast, Australia.
School of Medicine and Dentistry, Griffith University, Gold Coast, Australia.
Comput Biol Med. 2024 Jun;175:108527. doi: 10.1016/j.compbiomed.2024.108527. Epub 2024 Apr 28.
Cone beam computed tomography periapical volume index (CBCTPAVI) is a categorisation tool to assess periapical lesion size in three-dimensions and predict treatment outcomes. This index was determined using a time-consuming semi-automatic segmentation technique. This study compared artificial intelligence (AI) with semi-automated segmentation to determine AI's ability to accurately determine CBCTPAVI score.
CBCTPAVI scores for 500 tooth roots were determined using both the semi-automatic segmentation technique in three-dimensional imaging analysis software (Mimics Research™) and AI (Diagnocat™). A confusion matrix was created to compare the CBCTPAVI score by the AI with the semi-automatic segmentation technique. Evaluation metrics, precision, recall, F1-score (2×precision×recallprecision+recall), and overall accuracy were determined.
In 84.4 % (n = 422) of cases the AI classified CBCTPAVI score the same as the semi-automated technique. AI was unable to classify any lesion as index 1 or 2, due to its limitation in small volume measurement. When lesions classified as index 1 and 2 by the semi-automatic segmentation technique were excluded, the AI demonstrated levels of precision, recall and F1-score, all above 0.85, for indices 0, 3-6; and accuracy over 90 %.
Diagnocat™ with its ability to determine CBCTPAVI score in approximately 2 min following upload of the CBCT could be an excellent and efficient tool to facilitate better monitoring and assessment of periapical lesions in everyday clinical practice and/or radiographic reporting. However, to assess three-dimensional healing of smaller lesions (with scores 1 and 2), further advancements in AI technologies are needed.
锥形束计算机断层扫描根尖容积指数（CBCTPAVI）是一种用于三维评估根尖病变大小并预测治疗结果的分类工具。该指数是使用耗时的半自动分割技术确定的。本研究比较了人工智能（AI）与半自动分割，以确定 AI 准确确定 CBCTPAVI 评分的能力。
使用三维成像分析软件（Mimics Research™）中的半自动分割技术和 AI（Diagnocat™）确定 500 个牙根的 CBCTPAVI 评分。创建混淆矩阵以比较 AI 与半自动分割技术的 CBCTPAVI 评分。评估指标包括精度、召回率、F1 分数（2×精度×召回率精度+召回率）和总体准确性。
在 84.4%（n=422）的情况下，AI 将 CBCTPAVI 评分与半自动技术分类相同。由于其在小体积测量方面的限制，AI 无法将任何病变分类为指数 1 或 2。当将半自动分割技术分类为指数 1 和 2 的病变排除在外时，AI 表现出精度、召回率和 F1 分数均高于 0.85 的指数 0、3-6；准确性超过 90%。
Diagnocat™ 能够在上传 CBCT 后大约 2 分钟内确定 CBCTPAVI 评分，这可能是一种极好且高效的工具，可以促进在日常临床实践和/或放射报告中更好地监测和评估根尖病变。然而，要评估更小病变（得分 1 和 2）的三维愈合，需要进一步改进 AI 技术。
