Berhouet Julien, Favard Luc, Boas David, Voisin Théo, Slimane Mohamed
Équipe reconnaissance de forme et analyse de l'image, université François Rabelais de Tours, école d'ingénieurs polytechnique universitaire de Tours, laboratoire d'informatique EA6300, 64, avenue Portalis, 37200 Tours, France; Western France Orthopedics Society (SOO)/HUGORTHO, 18, rue de Bellinière, 49800 Trélazé, France.
Service d'orthopédie traumatologie, faculté de médecine de Tours, université François Rabelais de Tours, CHRU Trousseau, 1C, avenue de la République, 37170 Chambray-les-Tours, France; Western France Orthopedics Society (SOO)/HUGORTHO, 18, rue de Bellinière, 49800 Trélazé, France.
Orthop Traumatol Surg Res. 2019 Apr;105(2):203-209. doi: 10.1016/j.otsr.2018.10.024. Epub 2019 Feb 11.
The aim of this study was to identify points on the scapula that can be used to predict the anatomy of the native premorbid glenoid.
Forty-three normal scapulas reconstructed in 3D and positioned in a common coordinate system were used. Twenty points distributed over the blade of the scapula (portion considered normal and used as a reference) and the glenoid (portion considered pathological and needing to be reconstructed) were captured manually. Thirteen distances (X) between two points not on the glenoid and 31 distances (Y) between two points of which at least one was on the glenoid were then calculated automatically. A multiple linear regression model was applied to calculate the Y distances from the X distances. The best four equations were retained based on their coefficient of determination (R) to explain a point on the glenoid being reconstructed (p<0.05). In the first scenario, the glenoid was modeled assuming it was completely destroyed. In the second scenario, only the inferior portion of the glenoid was worn.
For a completely destroyed glenoid, the mean error for a chosen distance for a given point on the glenoid was 2.4 mm (4.e-3mm; 12.5mm). For a partially damaged glenoid, the mean error was 1.7mm (4.e-3mm; 6.5mm) for the same distance evaluated for a given point on the glenoid.
DISCUSSION/CONCLUSION: The proposed statistical model was used to predict the premorbid anatomy of the glenoid with an acceptable level of accuracy. A surgeon could use this information during the preoperative planning stage and during the actual surgery by using a new surgical assistance method.
本研究的目的是确定肩胛骨上可用于预测病前天然肩胛盂解剖结构的点。
使用了43个重建为三维并置于共同坐标系中的正常肩胛骨。手动采集了分布在肩胛骨（视为正常部分并用作参考）和肩胛盂（视为病理部分且需要重建）上的20个点。然后自动计算不在肩胛盂上的两点之间的13个距离（X）以及至少有一点在肩胛盂上的两点之间的31个距离（Y）。应用多元线性回归模型根据X距离计算Y距离。根据决定系数（R）保留最佳的四个方程，以解释肩胛盂上待重建的点（p<0.05）。在第一种情况下，假设肩胛盂完全破坏进行建模。在第二种情况下，仅肩胛盂的下部磨损。
对于完全破坏的肩胛盂，肩胛盂上给定一点的选定距离的平均误差为2.4毫米（4.e-3毫米；12.5毫米）。对于部分受损的肩胛盂，肩胛盂上给定一点评估的相同距离的平均误差为1.7毫米（4.e-3毫米；6.5毫米）。
讨论/结论：所提出的统计模型用于预测肩胛盂的病前解剖结构，具有可接受的准确度。外科医生可在术前规划阶段以及实际手术期间通过使用一种新的手术辅助方法来利用此信息。
