Cone Beam Computerized Tomography Preoperative Guidance under Feature Point Extraction Algorithm Combined with Autologous Concentrated Growth Factors in Maxillary Dental Implantation with Insufficient Bone Mass - Fang Liu; Wenjing Cui; Tingting Wang; Cheng Qian; Ren Yang; Rongxiu Zhang; Li Xu; Jie Hu; Liang Liu; Kai Zhang (Applied Artificial Intelligence)
Objective It was to assess the efficacy of preoperative guidance using cone beam computed tomography (CBCT) and the application of autologous concentrated growth factors (CGF) for implantation in maxillary teeth with inadequate bone mass.Methods An eigenvector-based multimodal elasticity algorithm was developed. Eighty patients with insufficient bone mass in the maxillary dental region were rolled into control group (n = 40, Bio-Oss bone powder treatment) and observation group (n = 40, autologous CGF and Bio-Oss bone powder treatment). The scale-invariant feature transform (SIFT) algorithm and the hierarchical attribute matching mechanism for elastic registration (HAMMER) were introduced, whose registration time and mutual information value were compared. Therapeutic outcomes of patients were also compared.Results The eigenvector-based multimodal elastic algorithm exhibited a significantly shorter registration time versus SIFT and HAMMER algorithms, and superior accuracy, sensitivity, and specificity (p < .05). Observation group demonstrated greater alveolar ridge elevation and bone augmentation versus control group, implying superior healing outcomes.Conclusion The feature point extraction algorithm is applicable for CBCT image processing in patients with insufficient maxillary bone mass. Autologous CGF exhibited superior therapeutic efficacy.