Enumerated sparse extraction of important surgical planning features for mandibular reconstruction

[2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC 2020); Montreal, Quebec, Canada, 20-24 July 2020]Because implicit medical knowledge and experience are used to perform medical treatment, such decisions must be clarified when systematizing surgical procedures. We propose an algorithm that extracts low-dimensional features that are important for determining the number of fibular segments in mandibular reconstruction using the enumeration of Lasso solutions (eLasso). To perform the multi-class classification, we extend the eLasso using an importance evaluation criterion that quantifies the contribution of the extracted features. Experiment results show that the extracted 7-dimensional feature set has the same estimation performance as the set using all 49-dimensional features

Analysis of important features in surgical planning for mandibular reconstruction among multiple surgeons

医師は医療機関の設備や方針, 自らの経験を考慮に入れて医療行為を遂行しており, 画一的に最適化された機械学習モデルが受け入れられるとは限らない. 同一症例であっても, 手術計画は担当する医師によって異なる場合があり, データに内在する意思決定の多様性に柔軟に適応できる予測モデルを構築できるかは機械学習が直面する課題の一つと考えられる. 本研究では, 複数医師による下顎骨再建計画を対象に, 腓骨片数の決定に重要な低次元特徴量の解析を行った. 口腔外科医及び歯科技工士3名による合計696の手術計画を対象に, 手術計画を再現可能な7次元特徴量を抽出し, それぞれが重視する特徴量の共通点や差異を明らかにしたので報告する.Surgeons perform surgical treatment by considering the facilities and policies of medical institutions and their own experience. This suggests that a uniformly optimized machine learning model is not always accepted. Since different surgeons may have different surgical plans despite the same case, building a predictive model reflecting the diversity of decision-making process is considered to be one of the challenges facing machine learning. The purpose of this study was to analyze the important features in the mandibular reconstruction plans among multiple surgeons. We extracted 7-dimensional important features from total 696 surgical plans of two oral surgeons and one dental technician, and analyzed the universal properties and differences of the feature sets

Surgical planning model generation by extracting important feature sets in mandibular reconstruction

医師は医学知識と経験を駆使して医療行為を遂行しており, その判断基準が明確になれば手術手技の体系化に繋がると考えられる. これまでに下顎骨再建計画を対象として, 多クラス分類に対応したLasso解列挙を提案したが, 特徴量組の探索に要する計算量が大きい点が課題であった. 本研究では, 頻出特徴量を優先的に選出する多クラス分類問題に対応したLasso解列挙アルゴリズムを提案し, 可読性の高い低次元特徴量群に基づく手術計画モデルの生成を試みた. 232の手術計画例を対象とした実験により, 従来の約76%の計算時間で医師の手術計画を90%以上の正解率で再現する5次元の特徴量組を抽出したので報告する.Because implicit medical knowledge and experience are used to perform medical treatment, such decisions must be clarified when systematizing surgical procedures. We proposed the enumaration of Lasso solutions corresponding to multiple classes in mandibular reconstruction, but the calculation amount required to search feature sets was large. In this study, we propose the enumeration of Lasso solutions algorithm for multi-class classification that preferentioally selects frequently features. Experiments showed that the 5-dimensional feature set which can correctly estimate more than 90% of surgeons' plans with 76% calculation time compared to the previous methods