Ethical and philosophical issues in medical imaging, multimodal learning and fusion across scales for clinical decision support, and topological data analysis for biomedical imaging : 1st International Workshop, EPIMI 2022, 12th Interna...最新文献

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Predicting Osteoarthritis of the Temporomandibular Joint Using Random Forest with Privileged Information. 基于特权信息的随机森林预测颞下颌关节骨关节炎。

Ethical and philosophical issues in medical imaging, multimodal learning and fusion across scales for clinical decision support, and topological data analysis for biomedical imaging : 1st International Workshop, EPIMI 2022, 12th Interna...

Pub Date : 2022-09-01 DOI: 10.1007/978-3-031-23223-7_7

Elisa Warner, Najla Al-Turkestani, Jonas Bianchi, Marcela Lima Gurgel, Lucia Cevidanes, Arvind Rao

Osteoarthritis of the temporomandibular joint (TMJ OA) is the most common disorder of the TMJ. A clinical decision support (CDS) system designed to detect TMJ OA could function as a useful screening tool as part of regular check-ups to detect early onset. This study implements a CDS concept model based on Random Forest and dubbed RF⁺ to predict TMJ OA with the hypothesis that a model which leverages high-resolution radiological and biomarker data in training only can improve predictions compared with a baseline model which does not use privileged information. We found that the RF⁺ model can outperform the baseline model even when privileged features are not of gold standard quality. Additionally, we introduce a novel method for post-hoc feature analysis, finding shortRunHighGreyLevelEmphasis of the lateral condyles and joint distance to be the most important features from the privileged modalities for predicting TMJ OA.

颞下颌关节骨性关节炎(tmjoa)是颞下颌关节最常见的疾病。临床决策支持(CDS)系统设计用于检测TMJ OA可以作为有用的筛选工具，作为定期检查的一部分，以发现早期发病。本研究实现了一个基于随机森林(Random Forest)的CDS概念模型，并将其称为RF+来预测TMJ OA，假设与不使用特权信息的基线模型相比，仅利用训练中的高分辨率放射学和生物标志物数据的模型可以提高预测。我们发现，即使特权特性不具有黄金标准质量，RF+模型也可以优于基线模型。此外，我们引入了一种新的事后特征分析方法，发现外侧髁和关节距离的shortRunHighGreyLevelEmphasis是预测TMJ OA的特权模式中最重要的特征。

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