A DEEP LEARNING DECISION SUPPORT SYSTEM FOR FUNCTIONAL ENDOSCOPIC SINUS SURGERY USING COMPUTED TOMOGRAPHY SCANS

• Celina Gruber Department of Industrial Engineering, Stellenbosch University, South Africa
• Eldon Burger Department of Industrial Engineering, Stellenbosch University, South Afric https://orcid.org/0000-0002-2689-3586

DOI:

https://doi.org/10.7166/35-3-3097

Abstract

Chronic sinusitis is a common disease that significantly affects quality of life. To treat chronic sinusitis, functional endoscopic sinus surgery (FESS) is frequently considered. FESS alleviates chronic sinusitis symptoms by restoring natural sinus drainage. Otolaryngologists rely on computed tomography (CT) reports to establish whether FESS is appropriate. To enhance sinus CT reports and improve decision-making, a segmentation model is developed. Both 2D and 3D segmentation models were compared, with the 3D model achieving marginally better results. The model accurately segments the sinus system, including the nasal cavity, achieving a mean Dice coefficient of 0.889 ± 0.028. The resulting 3D visualisation of the segmented sinus system enables quick identification of opacified regions, helping otolaryngologists to make informed decisions about the appropriateness of FESS. This automated approach reduces the time required to compile reports, improves the precision of clinical evaluations, and ultimately enhances patient care.