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Diagnostic accuracy of an automated classifier for the detection of pleural effusions in patients undergoing lung ultrasound

Rushil Chaudhary, Jordan Ho, Delaney Smith, Saad Hossain, Jaswin Hargun, Blake VanBerlo, Niall Murphy, Ross Prager, Kiran Rikhraj, Jared Tschirhart, Robert Arntfield

The American Journal of Emergency Medicine · 2025 · https://doi.org/10.1016/j.ajem.2025.01.041

This study aims to develop and validate a deep learning model for detecting pleural effusions in lung ultrasound images, with adaptable performance characteristics tailored to specific clinical scenarios.

Automated real-time detection of lung sliding using artificial intelligence: a prospective diagnostic accuracy study

Hans Clausdorff Fiedler, Ross Prager, Delaney Smith, Derek Wu, Chintan Dave, Jared Tschirhart, Benjamin Wu, Blake Van Berlo, Richard Malthaner, Robert Arntfield

CHEST · 2024 · 10.1016/j.chest.2024.02.011

In patients with suspected pneumothorax, what is the real-time diagnostic accuracy of AI-LUS to recognize the absence of lung sliding?

Intra-video positive pairs in self-supervised learning for ultrasound

Blake VanBerlo, Alexander Wong, Jesse Hosey, Robert Arntfield

arXiv preprint · 2024 · 10.48550/ARXIV.2403.07715

In this study, we investigated the effect of utilizing proximal, distinct images from the same B-mode ultrasound video as pairs for SSL.

Improving the generalizability and performance of an ultrasound deep learning model using limited multicenter data for lung sliding artifact identification

Derek Wu, Delaney Smith, Blake VanBerlo, Amir Roshankar, Hoseok Lee, Brian Li, Faraz Ali, Marwan Rahman, John Basmaji, Jared Tschirhart, Alex Ford, Bennett VanBerlo, Ashritha Durvasula, Claire Vannelli, Chintan Dave, Jason Deglint, Jordan Ho, Rushil Chaudhary, Hans Clausdorff, Ross Prager, Scott Millington, Samveg Shah, Brian Buchanan, Robert Arntfield

Diagnostics · 2024 · 10.3390/diagnostics14111081

We report a multicenter study that exploits limited available external data to improve the generalizability and performance of our lung sliding model while identifying poorly performing subgroups to inform future iterative improvements.

Self-supervised pretraining improves performance and inference efficiency in multiple lung ultrasound interpretation tasks

Blake VanBerlo, Brian Li, Jesse Hoey, Alexander Wong

IEEE Access · 2023 · 10.1109/access.2023.3337398

In this study, we investigated whether self-supervised pretraining could produce a neural network feature extractor applicable to multiple classification tasks in B-mode lung ultrasound analysis.

Exploring the utility of self-supervised pretraining strategies for the detection of absent lung sliding in M-mode lung ultrasound

Blake VanBerlo, Brian Li, Alexander Wong, Jesse Hoey, Robert Arntfield

IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops · 2023 · 10.1109/cvprw59228.2023.00309

This study explores the efficacy of self-supervised pretraining in improving performance for lung sliding classification in M-mode lung ultrasound images, highlighting its superiority over full supervision.

Prospective real-time validation of a lung ultrasound deep learning model in the ICU

Chintan Dave, Derek Wu, Jared Tschirhart, Delaney Smith, Blake VanBerlo, Jason Deglint, Faraz Ali, Rushil Chaudhary, Bennett VanBerlo, Alex Ford, Marwan A. Rahman, Joseph McCauley, Benjamin Wu, Jordan Ho, Brian Li, Robert Arntfield

Critical Care Medicine · 2023 · 10.1097/01.ccm.0000906084.96293.1b

This prospective study assessed the accuracy of a deep learning model deployed in real-time at the bedside to differentiate between normal and abnormal lung parenchyma on lung ultrasound in critically ill patients.

Introducing medical students to deep learning through image labelling: a new approach to meet calls for greater artificial intelligence fluency among medical trainees

Jared Tschirhart, Amadene Woolsey, Jamila Skinner, Khadija Ahmed, Courtney Fleming, Justin Kim, Chintan Dave, Robert Arntfield

Canadian Medical Education Journal · 2022 · 10.36834/cmej.75074

Our approach bridges the gap between medical education and AI by empowering medical students to excel in medical imaging and labeling, fostering competence while generating valuable data for AI integration in clinical practice.

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