Automated Planning with Multivariate Shape Descriptors for Fibular Transfer in Mandibular Reconstruction

Objective: This paper introduces methods to automate preoperative planning of fibular segmentation and placement for mandibular reconstruction with fibular flaps. Methods: Preoperative virtual planning for this type of surgery has been performed by manual adjustment of many parameters, or based upon a single feature of the reconstruction. We propose a novel planning procedure formulated as a non-convex minimization problem of an objective function using the multilateral shape descriptors. Results: A retrospective study was designed and 120 reconstruction plans were reproduced using computed tomography images with oral surgeons. The proposed automated planning model was quantitatively compared with both the existing model and the surgeons’ plans. Conclusion: The results show that the developed framework attains stable automated planning that agrees with the surgeons’ decisions. Significance: This method addresses trade-off problems between symmetric reconstruction and restoration of the native contour of the mandible

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

Condyle dislocation following mandibular reconstruction using a fibula free flap: complication cases

Background: Condylar dislocation can arise as a complication in patients who required mandibular and/or condylar reconstruction and were operated on with fibula free flap (FFF) using surgical guides designed using simulation surgery. Surgeons should be aware of the complications in these present cases when planning and performing reconstructions as well as predicting prognoses. Cases presentation: Two cases showed condylar dislocation in mandibular reconstruction using a FFF fixed with a reconstruction plate. Three cases showed condylar dislocation in mandibular reconstruction using a fibula free flap fixed with a mini-plate. Conclusion: Despite the lack of clinical symptoms in these cases following mandibular reconstruction using an FFF, the mandibular condyle was severely displaced away from the glenoid fossa. A surgeon must have sufficient time to consider the use of a long flap with thickness similar to that of the mandible, ways to minimize span and bending, and methods of fixation. The patient, moreover, should be educated on condylar dislocation. Customized CAD/CAM-prototyped temporomandibular condyle-connected plates may be a good alternative even if virtual simulation surgery is to be performed before surgery. These considerations may help reduce the incidence of complications after mandibular reconstruction.ope

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

Automated Planning With Multivariate Shape Descriptors for Fibular Transfer in Mandibular Reconstruction

Objective: This paper introduces methods to automate preoperative planning of fibular segmentation and placement for mandibular reconstruction with fibular flaps. Methods: Preoperative virtual planning for this type of surgery has been performed by manual adjustment of many parameters, or based upon a single feature of the reconstruction. We propose a novel planning procedure formulated as a non-convex minimization problem of an objective function using the multilateral shape descriptors. Results: A retrospective study was designed and 120 reconstruction plans were reproduced using computed tomography images with oral surgeons. The proposed automated planning model was quantitatively compared with both the existing model and the surgeons’ plans. Conclusion: The results show that the developed framework attains stable automated planning that agrees with the surgeons’ decisions. Significance: This method addresses trade-off problems between symmetric reconstruction and restoration of the native contour of the mandible