Deep Breathe

Research

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Arntfield, R., et al. "Automation of lung ultrasound interpretation via deep learning for the classification of normal versus abnormal lung parenchyma: A multicenter study". Diagnostics 11.11 (2021): 2049.

This study introduces automated LUS interpretation using deep learning to address these challenges. By training a neural network on a large dataset of labeled LUS images, the study aimed to distinguish between normal and abnormal lung patterns.

11.04.2021

Ultrasound

Research

Arntfield, R., et al. "Development of a convolutional neural network to differentiate among the etiology of similar appearing pathological B lines on lung ultrasound: a deep learning study". BMJ open 11.3 (2021): e045120.

This study investigated whether deep learning techniques could enhance diagnostic specificity in lung ultrasound imaging. A convolutional neural network was trained on ultrasound images depicting B lines associated with various respiratory pathologies.

02.02.2021

Ultrasound

Research

Dave, C., et al. "Prospective Real-Time Validation of a Lung Ultrasound Deep Learning Model in the ICU". Critical Care Medicine 51.2 (2023): 301-309.

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.

02.01.2023

Ultrasound

Research

Tschirhart, J., et al. "Introducing medical students to deep learning through image labelling: a new approach to meet calls for greater artificial intelligence fluency among medical trainees". Canadian Medical Education Journal. (2023)

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.

VanBerlo, B., et al. "Accurate assessment of the lung sliding artefact on lung ultrasonography using a deep learning approach". Computers in Biology and Medicine 148 (2022): 105953.

Using deep learning methods, our study developed a binary classifier capable of accurately distinguishing between the presence and absence of the lung sliding artifact on lung ultrasound images.

08.09.2022

Ultrasound

Research

VanBerlo, B., et al. "Enhancing Annotation Efficiency with Machine Learning: Automated Partitioning of a Lung Ultrasound Dataset by View". Diagnostics 12.10 (2022): 2351.

We propose a method leveraging hierarchical organization of annotating tasks to increase efficiency in annotating lung ultrasound images, demonstrating significant time savings and higher throughput in view-specific annotation tasks.

09.28.2022

Ultrasound

Research