Endoluminal larynx anatomy model – towards facilitating deep learning and defining standards for medical images evaluation with artificial intelligence algorithms

Abstract

The pioneering nature of this work covers the answers to two questions: (1) Is an up-to-date anatomical model of the larynx needed for modern endoscopic diagnostics, and (2) can such a digital segmentation model be utilized for deep learning purposes. The idea presented in this article has never been proposed before, and this is a breakthrough in numerical approaches to aerodigestive videoendoscopy imaging. The approach described in this article assumes defining a process for data acquisition, integration, and segmentation (labeling), for the needs of a new branch of knowledge: digital medicine and digital diagnosis support expert systems. The first and crucial step of such a process is creating a digital model of the larynx, which has to be then validated utilizing multiple clinical, as well as technical metrics. The model will form the basis for further artificial intelligence (AI) requirements, and it may also contribute to the development of translational medicine.

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