Abstract 349: Deep Learning Detection of Chest Compression Location Using Transesophageal Echocardiography
Authors: Teran Felipe , Peng Yifan , Owyang Clark G , Wray Trenton C , Hipskind John E , Lessard Justine , Bédard Michel William , Lanthier Chantal , Nazerian Peiman , de Villa Eleonora , Nogueira Jonathan , Doynow Daniel , Clinton Michelle , Myslik Frank , Prager Ross , Arntfield Robert , Salinas Pedro D , Dieiev Vladyslav , Woo Michael Y , Thavanathan Rajiv , Puskas Graeme , Singh Karan , Bhat Priyanka , Horn Jackson , Buchanan Brian M , Baig Nadia , Burns Katharine , Kennedy Kelsey , Haines Lawrence , Naraghi Leily , Singh Harpriya , Secko Michael , Singer Daniel , Taylor Maria , Joyce John M , DeMasi Stephanie , Jafry Zan M , Phan Tammy , Truong Natalie , Robinson Evan , Haycock Korbin H , Hansen Allyson , Derr Charlotte , Andrus Phillip , Razzak Junaid , Palasz Joanna , Kaviyarasu Aarthi , Sands Nathaniel A , Lin Mingquan , Abella Benjamin S 
Published: December 2023

Background:
Recent work evaluating intra-arrest transesophageal echocardiography (TEE) in cardiac arrest (CA) has demonstrated that chest compression (CC) location is a strong predictor of return of spontaneous circulation. However, advanced skills to interpret images during resuscitation represent an important barrier to implementation of this modality. Deep Learning (DL) models have been increasingly used to perform the automated interpretation of TEE images. Given the unique challenges during the interpretation of images, DL-based automation represents an attractive adjunct to facilitate the use of intra-arrest TEE. We sought to evaluate the feasibility of a DL model to identify CC location in TEE images obtained during CA resuscitation.

Methods:
We analyzed videos from out-of-hospital and in-hospital CA patients evaluated with TEE collected through the Resuscitative TEE Collaborative Registry (NCT04972526). We reviewed and selected images for the DL algorithm, using midesophageal long axis clips. Videos were selected based on the visualization of anatomical structures necessary to identify the CC location. Images were annotated by an expert classifying them in CC over the LV (CC-LV) vs CC over the left ventricular outflow tract (CC-LVOT). We used TEENet, an end-to-end DL approach with input of TEE clips containing single compression cycles, to classify clips in CC-LV and CC-LVOT. The TEENet architecture includes two 3D convolution layers, followed by a global average pooling layer, and then appends a fully connected layer with a sigmoid layer. We used the F1-score as the evaluation metric for the model's performance.

Results:
Ten videos containing a total of 47 single CC cycle clips were selected for inclusion in the model, each clip with an average of 25 frames. The model was trained using 80% of the data (37 clips) and tested using the remaining 20% (10 clips). The model achieved an F1-score of 0.7273 in classifying CC-LV from CC-LVOT.

Conclusion:
We report the creation of a novel DL-model with acceptable precision and recall performance in the detection of CC location using intra-arrest TEE videos. This work is a proof-of-concept for future research aimed to evaluate the potential of DL-assisted TEE-guided CA resuscitation.