Authors: I. Carboni Bisso; L. Tarsia; I. Fernández Ceballos; D. Acevedo; M. Florencia Curtois; N. Mastropasqua; M. Risk; Carolina Lockhart; V. Burgos; V. Cotik and M. Las Heras.
Abstract:
This study aims to create an artificial intelligence model capable of accurately identifying bronchial segments during broncho-endoscopic navigation. To achieve this, we analyzed 126 videos from bronchoscopic procedures conducted on critically ill patients at a university hospital in Buenos Aires, Argentina.
A dataset of consistently annotated videos, captured by bronchoscopists with varied expertise, was established. Inter-annotator agreement for image classification was evaluated using Cohen’s kappa coefficient. Images of multiple bronchial segments were used as input to train a convolutional neural network in order to obtain a classification model. This paper presents the annotation schema, labeling guidelines, the developed corpus, and some preliminary results.
More information:
https://doi.org/10.1007/978-3-031-80366-6_12
