Artificial intelligence in maxillary artery anatomy analysis: conceptual approach rationale

The maxillary artery demonstrates considerable anatomical variability, creating substantial challenges in preoperative planning for maxillofacial surgical interventions. Traditional preoperative imaging methods require significant time for data interpretation and depend heavily on specialist expertise. The accumulation of large DICOM medical image datasets creates prerequisites for applying machine learning methods and deep neural networks to automate vascular structure analysis. This work presents a conceptual rationale for applying artificial intelligence technologies to identify anatomical variations of the maxillary artery based on computed tomography and cone-beam computed tomography data analysis. We analyze the current state of deep learning algorithm applications in medical visualization of head and neck vascular structures, systematize known anatomical variations of the maxillary artery and their clinical significance, and formulate technical requirements for potential automated analysis system architecture. The proposed conceptual approach includes using convolutional neural networks for semantic segmentation of the vascular network, three-dimensional reconstruction algorithms for visualizing topographic relationships, and a classification system for identified structural variants by surgical risk degree. We substantiate the necessity of creating a specialized training dataset of annotated maxillary artery images to ensure high recognition accuracy. We discuss potential advantages of automated analysis, including standardization of diagnostic approaches, reduction of preoperative planning time, and minimization of intraoperative complications related to vascular injury. We acknowledge existing technical and organizational limitations of implementing such systems, including the need for validation on large clinical cohorts and integration into existing medical information systems.