Development and testing of a deep learning algorithm to detect lung consolidation among children with pneumonia using hand-held ultrasound

by David Kessler, Meihua Zhu, Cynthia R. Gregory, Courosh Mehanian, Jailyn Avila, Nick Avitable, Di Coneybeare, Devjani Das, Almaz Dessie, Thomas M. Kennedy, Joni Rabiner, Laurie Malia, Lorraine Ng, Megan Nye, Marc Vindas, Peter Weimersheimer, Sourabh Kulhare, Rachel Millin, Kenton Gregory, Xinliang Zheng, Matthew P. Horning, Mike Stone, Fen Wang, Christina Lancioni

Background and objectives

Severe pneumonia is the leading cause of death among young children worldwide, disproportionately impacting children who lack access to advanced diagnostic imaging. Here our objectives were to develop and test the accuracy of an artificial intelligence algorithm for detecting features of pulmonary consolidation on point-of-care lung ultrasounds among hospitalized children.

Methods

This was a prospective, multicenter center study conducted at academic Emergency Department and Pediatric inpatient or intensive care units between 2018–2020. Pediatric participants from 18 months to 17 years old with suspicion of lower respiratory tract infection were enrolled. Bedside lung ultrasounds were performed using a Philips handheld Lumify C5-2 transducer and standardized protocol to collect video loops from twelve lung zones, and lung features at both the video and frame levels annotated. Data from both affected and unaffected lung fields were split at the participant level into training, tuning, and holdout sets used to train, tune hyperparameters, and test an algorithm for detection of consolidation features. Data collected from adults with lower respiratory tract disease were added to enrich the training set. Algorithm performance at the video level to detect consolidation on lung ultrasound was determined using reference standard diagnosis of positive or negative pneumonia derived from clinical data.

Results

Data from 107 pediatric participants yielded 117 unique exams and contributed 604 positive and 589 negative videos for consolidation that were utilized for the algorithm development process. Overall accuracy for the model for identification and localization of consolidation was 88.5%, with sensitivity 88%, specificity 89%, positive predictive value 89%, and negative predictive value 87%.

Conclusions

Our algorithm demonstrated high accuracy for identification of consolidation features on pediatric chest ultrasound in children with pneumonia. Automated diagnostic support on an ultraportable point-of-care device has important implications for global health, particularly in austere settings.