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1-6 of 10 publications

Clausdorff Fiedler, Hans Jurgen, et al. "Automated real-time detection of lung sliding using artificial intelligence: a prospective diagnostic accuracy study."

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

Research
Ultrasound

Wu, Derek, et al. "Improving the Generalizability and Performance of an Ultrasound Deep Learning Model Using Limited Multicenter Data for Lung Sliding Artifact Identification." Diagnostics 14.11 (2024): 1081.

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.

Ultrasound
Research

VanBerlo, Blake, et al. "Intra-video Positive Pairs in Self-Supervised Learning for Ultrasound." arXiv preprint arXiv:2403.07715 (2024).

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

Research
Ultrasound

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.

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.

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.

Ultrasound
Research
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