Development of a Movil App for the Preoperative Evaluation of Sinus CT Scan: One Step Towards Artificial Intelligence

Abstract

Introduction: The recent technology revolution that we have experienced has generated
extensive interest in the use of artificial intelligence (AI) in the development
of various systems and solutions in medicine. In the field of Otorhinolaryngology,
we are seeing the first efforts to take advantage of this flourishing area. Objective:
We sought to describe the development process of a mobile app created through a
collaborative effort between ENT surgeons and biomedical engineers. This app has
the intention to optimize the preoperative evaluation of paranasal sinus tomography
(CT) to improve safety and outcomes in Endoscopic Sinus Surgery (ESS). Methods:
The development of the app followed the prioritization method for MoSCoW specifications.
We used the information collected from surveys of 29 Rhinology experts
from different parts of the world, who evaluated anatomical variants on sinus CT
scans. Two regression models were used to predict difficulty and risk using statistical
learning. Conclusion: Via statistical modelling, we have developed a user-friendly
tool that will ideally help surgeons assess the risk and difficulty of ESS based on
the pre-operative CT scan of the sinuses. This is an exercise that demonstrates the
efficacy of the collaborative efforts between surgeons and engineers to leverage AI
tools and promote better solutions for our patients.