Detection of extracranial and intracranial calcified carotid artery atheromas in cone beam computed tomography using a deep learning convolutional neural network image segmentation approach

Shahd A Alajaji; Rula Amarin; Radi Masri; Tiffany Tavares; Vandana Kumar; Jeffery B Price; Ahmed S Sultan

doi:10.1016/j.oooo.2023.08.009

Detection of extracranial and intracranial calcified carotid artery atheromas in cone beam computed tomography using a deep learning convolutional neural network image segmentation approach

Oral Surg Oral Med Oral Pathol Oral Radiol. 2023 Aug 17:S2212-4403(23)00620-X. doi: 10.1016/j.oooo.2023.08.009. Online ahead of print.

Authors

Shahd A Alajaji¹, Rula Amarin², Radi Masri², Tiffany Tavares³, Vandana Kumar⁴, Jeffery B Price⁵, Ahmed S Sultan⁶

Affiliations

¹ Department of Oncology and Diagnostic Sciences, School of Dentistry, University of Maryland Baltimore, MD, USA; Division of Artificial Intelligence Research, University of Maryland School of Dentistry, Baltimore, MD, USA; Department of Oral Medicine and Diagnostic Sciences, College of Dentistry, King Saud University, Riyadh, Saudi Arabia.
² Department of Advanced Oral Sciences and Therapeutics, School of Dentistry, University of Maryland, Baltimore, MD, USA.
³ Department of Comprehensive Dentistry, UT Health San Antonio, School of Dentistry, San Antonio, TX, USA.
⁴ Department of Oncology and Diagnostic Sciences, School of Dentistry, University of Maryland Baltimore, MD, USA.
⁵ Department of Oncology and Diagnostic Sciences, School of Dentistry, University of Maryland Baltimore, MD, USA; Division of Artificial Intelligence Research, University of Maryland School of Dentistry, Baltimore, MD, USA.
⁶ Department of Oncology and Diagnostic Sciences, School of Dentistry, University of Maryland Baltimore, MD, USA; Division of Artificial Intelligence Research, University of Maryland School of Dentistry, Baltimore, MD, USA; Marlene and Stewart Greenebaum Comprehensive Cancer Center, University of Maryland, Baltimore, MD, USA. Electronic address: Asultan1@umaryland.edu.

PMID: 37770329
DOI: 10.1016/j.oooo.2023.08.009

Abstract

Objective: We leveraged an artificial intelligence deep-learning convolutional neural network (DL CNN) to detect calcified carotid artery atheromas (CCAAs) on cone beam computed tomography (CBCT) images.

Study design: We obtained 137 full-volume CBCT scans with previously diagnosed CCAAs. The DL model was trained on 170 single axial CBCT slices, 90 with extracranial CCAAs and 80 with intracranial CCAAs. A board-certified oral and maxillofacial radiologist confirmed the presence of each CCAA. Transfer learning through a U-Net-based CNN architecture was utilized. Data allocation was 60% training, 10% validation, and 30% testing. We determined the accuracy of the DL model in detecting CCAA by calculating the mean training and validation accuracy and the area under the receiver operating characteristic curve (AUC). We reserved 5 randomly selected unseen full CBCT volumes for final testing.

Results: The mean training and validation accuracy of the model in detecting extracranial CCAAs was 92% and 82%, respectively, and the AUC was 0.84 with 1.0 sensitivity and 0.69 specificity. The mean training and validation accuracy in detecting intracranial CCAAs was 61% and 70%, respectively, and the AUC was 0.5 with 0.93 sensitivity and 0.08 specificity. Testing of full-volume scans yielded an AUC of 0.72 and 0.55 for extracranial and intracranial CCAAs, respectively.

Conclusion: Our DL model showed excellent discrimination in detecting extracranial CCAAs on axial CBCT images and acceptable discrimination on full-volumes but poor discrimination in detecting intracranial CCAAs, for which further research is required.