Application of neural network for morphological assessment of revascularized autograft remodeling at the stage of implant placement in jaw defects

Abstract

Relevance. The study examines the possibility of using a mathematic model of an artificial neural network (ANN) for comparative quantitative morphological assessment of revascularized autograft remodeling after reconstructive surgery in patients with jaw defects.Materials and methods. Jawbone biopsies of 30 patients were histologically examined during the implant placement stage, 6, 9, 12 months after the reconstructive surgery with a revascularized fibular graft. Histology images were analyzed using a mathematic model created on the basis of GoogLeNet and trained on morphological images of bone maturation. Histology slides were digitized by a scanning microscope Leica SC2 and streamed through the neural network model. Results. During the follow-up period, jaw defect areas demonstrated relatively mature bone tissue formation with various intensity rates of remodeling and maturation of the newly-formed bone. At that, the results of the descriptive histology were consistent with the quantitative results of the ANN mathematic model, created on the basis of the softwarehardware system developed by the authors. The confirmed significance level is 95% or higher.Conclusion. Pathomorphological data of biopsies were studied using an ANN mathematical model, built on a softwarehardware system, which allowed analyzing all microscope fields of view of a histological slide, bypassing random samples, as well as the evaluation by a pathologist of individual microscope fields of view, to exclude the possibility of unrepresentative sampling and the influence of human factor, which significantly increases the significance of the received results.