Cranio-maxillofacial post-operative face prediction by deep spatial multiband VGG-NET CNN

Am J Transl Res. 2022 Apr 15;14(4):2527-2539. eCollection 2022.ABSTRACTCurrent plastic and reconstructive surgery computational techniques are not precise and take a long time to perform. Therefore, these limitations reduced the adoption of computational techniques. Although computer-aided surgical preparation systems may help to enhance clinical results, minimize operating time and costs, they are too complicated and require detailed manual information, which restricts their usage in doctor-patient communication and clinical decision-making. In order to obtain the optimal aesthetic and reconstruction treatment results, these techniques must be designed and implemented carefully. Computer-aided modeling, planning, and simulation techniques enable the preoperational evaluation of various therapeutic strategies based on the 3D patient models. We offer the new deep-learning architecture for diagnostics, risk stratification, and post-operative simulation for face prediction. Initially, preprocessing was done by using the weighted adaptive median filter and Laplacian partial differential equation-based histogram equalization. Then the target area was converted to 3D for clear visualization by using the Smart restorative frustum model. Finally, the post-operative face prediction was constructed by using the deep spatial Multiband VGG NET CNN. We obtained a face dataset of 313,318 CT and their clinical records from different centers. The algorithms were developed by 21,095 scans (Qu...
Source: American Journal of Translational Research - Category: Research Authors: Source Type: research