Exploring the Impact of Artificial Intelligence and Machine Learning in the Diagnosis and Management of Esthesioneuroblastomas: A Comprehensive Review

Abstract

Esthesioneuroblastomas (ENBs) present unique diagnostic and therapeutic challenges due to their rare and complex clinical presentation. In recent years, artificial intelligence (AI) and machine learning (ML) have emerged as promising tools in various medical specialties, revolutionizing diagnostic accuracy, treatment planning, and patient outcomes. However, their application in ENBs remains relatively unexplored. This comprehensive literature review aims to evaluate the current state of AI and ML technologies in ENB diagnosis, radiological and histopathological imaging, and treatment planning. By synthesizing existing evidence and identifying gaps in knowledge, this review aims to showcase the potential benefits, limitations, and future directions of integrating AI and ML into the multidisciplinary management of ENBs.

Keywords: artificial intelligence in radiology; artificial intelligence(ai); esthesioneuroblastoma and seizures; machine learning healthcare data; machine learning models.

Figure 1. Anatomy of the sinonasal cavity and proximal anatomical structures.

Image Credit: Adapted by Frank Gaillard from the original illustration by Patrick J. Lynch, Creative Commons Attribution 2.5 License 2006 [3]

Figure 2. CT image of an esthesioneuroblastoma, depicted as a large enhancing mass centered on the midline of the anterior cranial fossa and involving both orbits.

Image Credit: Frank Gaillard, 2010 [14]; Creative Commons License

Figure 3. Friedman's Fundamental Theorem of Biomedical Informatics depicting the synergistic utilization of AI technologies with healthcare clinicians.

Image Credit: Author Raj Patel