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

Fernanda Nogueira-Reis; Nermin Morgan; Isti Rahayu Suryani; Cinthia Pereira Machado Tabchoury; Reinhilde Jacobs

doi:10.1016/j.jdent.2023.104829

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

J Dent. 2024 Feb:141:104829. doi: 10.1016/j.jdent.2023.104829. Epub 2023 Dec 30.

Authors

Fernanda Nogueira-Reis¹, Nermin Morgan², Isti Rahayu Suryani³, Cinthia Pereira Machado Tabchoury⁴, Reinhilde Jacobs⁵

Affiliations

¹ OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, University of Leuven Department of Oral & Maxillofacial Surgery, University Hospitals Leuven, KU Leuven Kapucijnenvoer 7, Leuven 3000, Belgium; Department of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas (UNICAMP), Av. Limeira 901, Piracicaba, São Paulo 13414‑903, Brazil.
² OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, University of Leuven Department of Oral & Maxillofacial Surgery, University Hospitals Leuven, KU Leuven Kapucijnenvoer 7, Leuven 3000, Belgium; Department of Oral Medicine, Faculty of Dentistry, Mansoura University, Mansoura, Dakahlia 35516, Egypt.
³ OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, University of Leuven Department of Oral & Maxillofacial Surgery, University Hospitals Leuven, KU Leuven Kapucijnenvoer 7, Leuven 3000, Belgium; Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Universitas Gadjah Mada, Yogyakarta, Indonesia.
⁴ Department of Biosciences, Division of Biochemistry, Piracicaba Dental School, University of Campinas (UNICAMP), Av. Limeira 901, Piracicaba, São Paulo 13414‑903, Brazil.
⁵ OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, University of Leuven Department of Oral & Maxillofacial Surgery, University Hospitals Leuven, KU Leuven Kapucijnenvoer 7, Leuven 3000, Belgium; Department of Dental Medicine, Karolinska Institutet, Box 4064, Huddinge, Stockholm 141 04, Sweden. Electronic address: reinhilde.jacobs@ki.se.

PMID: 38163456
DOI: 10.1016/j.jdent.2023.104829

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.

Keywords: Artificial intelligence; Computer neural networks; Computer-generated 3D imaging; Cone-beam computed tomography.

MeSH terms

Artificial Intelligence*
Cone-Beam Computed Tomography / methods
Humans
Image Processing, Computer-Assisted / methods
Neural Networks, Computer
Spiral Cone-Beam Computed Tomography*