An Automatic Grading System for Neonatal Endotracheal Intubation with Multi-Task Convolutional Neural Network

Neonatal endotracheal intubation (ETI) is an intricate medical procedure that poses considerable challenges, demanding comprehensive training to effectively address potential complications in clinical practice. However, due to limited access to clinical opportunities, ETI training relies heavily on physical manikins to develop a certain level of competence before clinical exposure. Nonetheless, traditional training methods prove ineffective due to scarcity of expert instructors and the absence of internal situational awareness within the manikins, preventing thorough performance assessment for both trainees and instructors. To address this gap, there is a need to develop an automatic grading system that can assist trainees in performance assessment. In this paper, we proposed a multi-task Convolutional Neural Network (MTCNN) based model for assessing ETI proficiency, specifically targeting key performance features recommended by expert instructors. The model comprises three modules: an ETI simulation module that captures the ETI procedures performed on a standard neonatal task trainer manikin, an automatic grading module that extracts and grades the identified key performance features, and a data visualization module that presents the assessment results in a user-friendly manner. The experimental results demonstrated that the proposed automatic grading system achieved an average classification accuracy of 93.6%. This study established the successful integration of intuitive observed features with latent features derived from multivariate time series (MTS) data, coupled with multi-task deep learning techniques, for the automatic assessment of ETI performance.

The proposed automatic grading system facilitates an enhanced neonatal endotracheal intubation training experience for neonatologists.