The application of 3D imaging is at its cusp in craniofacial diagnosis and treatment planning. However, most applications are limited to simple subjective superimposition-based analysis. As the diagnostic accuracy dictates the precision in operability, we propose a novel method that enables objective clinical decision making for patients with mandibular asymmetry. We analyzed cone-beam computed tomography (CBCT) scans of 34 patients who underwent surgical correction for mandibular asymmetry using a high-throughput computing algorithm. Radiomic segmentation of quantitative features of surface and volume followed by exploration resulted in identification of a computed modified absolute mandibular midsagittal plane (cmAMP). Tomographic similarity scan (ToSS) curves were generated via bilateral equidistant scanning in an antero-posterior direction with cmAMP as the reference. ToSS comprised of a comprehensive similarity index (SI) score curve and a segment-wise volume curve. The SI score was computed using the Sørensen–Dice similarity coefficient ranging from 0 to 1. The volumetric analysis was represented as the non-overlapping volume (NOV) and overlapping volume (OV) for each segment, with two segmentation lines, at the mental foramen anteriorly and the intraoral vertical ramus osteotomy region posteriorly. Statistical analysis showed strong negative correlation between the NOV and SI scores for the anterior, middle, and total mandible (P < 0.001). Additionally, a significant correlation was observed between the change in the SI scores for anterior (P = 0.044) and middle segments (P < 0.001) to the total mandible when comparing the data before and after the surgery. This work demonstrated the potential of incorporating ToSS curves in surgical simulation software to improve precision in the clinical decision-making process.
Bibliographical noteFunding Information:
This research was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) , funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI20C0611).
© 2021 Elsevier Ltd
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Health Informatics