Full virtual patient generated by artificial intelligence-driven integrated segmentation of craniomaxillofacial structures from CBCT images.

ABSTRACT

OBJECTIVES: To assess the performance, time-efficiency, and consistency of a convolutional neural network (CNN) based automated approach for integrated segmentation of craniomaxillofacial structures compared with semi-automated method for creating a virtual patient using cone beam computed tomography (CBCT) scans. METHODS: Thirty CBCT scans were selected. Six craniomaxillofacial structures, encompassing the maxillofacial complex bones, maxillary sinus, dentition, mandible, mandibular canal, and pharyngeal airway space, were segmented on these scans using semi-automated and composite of previously validated CNN-based automated segmentation techniques for individual structures. A qualitative assessment of the automated segmentation revealed the need for minor refinements, which were manually corrected. These refined segmentations served as a reference for comparing semi-automated and automated integrated segmentations. RESULTS: The majority of minor adjustments with the automated approach involved under-segmentation of sinus mucosal thickening and regions with reduced bone thickness within the maxillofacial complex. The automated and the semi-automated approaches required an average time of 1.1 min and 48.4 min, respectively. The automated method demonstrated a greater degree of similarity (99.6 %) to the reference than the semi-automated approach (88.3 %). The standard deviation values for all metrics with the automated approach were low, indicating a high consistency. CONCLUSIONS: The CNN-driven integrated segmentation approach proved to be accurate, time-efficient, and consistent for creating a CBCT-derived virtual patient through simultaneous segmentation of craniomaxillofacial structures. CLINICAL RELEVANCE The creation of a virtual orofacial patient using an automated approach could potentially transform personalized digital workflows. This advancement could be particularly beneficial for treatment planning in a variety of dental and maxillofacial specialties.