Publications

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Author [ Title(Desc)] Type Year
A
B. VanBerlo et al., “Accurate assessment of the lung sliding artefact on lung ultrasonography using a deep learning approach”, Computers in Biology and Medicine, vol. 148, no. 0010-4825, 2022.
H. Clausdorff Fiedler et al., “Automated Real-Time Detection of Lung Sliding Using Artificial Intelligence: A Prospective Diagnostic Accuracy Study”, Chest, p. S0012--3692, 2024.
L. A. Groves, VanBerlo, B. , Veinberg, N. , Alboog, A. , Peters, T. M. , and Chen, E. C. S. , “Automatic segmentation of the carotid artery and internal jugular vein from 2D ultrasound images for 3D vascular reconstruction”, International Journal of Computer Assisted Radiology and Surgery, 2020.
R. Arntfield et al., “Automation of Lung Ultrasound Interpretation via Deep Learning for the Classification of Normal versus Abnormal Lung Parenchyma: A Multicenter Study”, Diagnostics, vol. 11, p. 2049, 2021.
D
L. A. Groves, VanBerlo, B. , Peters, T. M. , and Chen, E. C. S. , “Deep learning approach for automatic out-of-plane needle localisation for semi-automatic ultrasound probe calibration”, Healthcare Technology Letters, vol. 6, pp. 204–209, 2019.
R. Arntfield 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, vol. 11, 2021.
E
B. VanBerlo et al., “Enhancing Annotation Efficiency with Machine Learning: Automated Partitioning of a Lung Ultrasound Dataset by View”, Diagnostics, vol. 12, no. 10, p. 2351, 2022. diagnostics-12-02351.pdf
B. VanBerlo, Li, B. , Wong, A. , Hoey, J. , and Arntfield, R. , “Exploring the Utility of Self-Supervised Pretraining Strategies for the Detection of Absent Lung Sliding in M-Mode Lung Ultrasound”, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 3076–3085.
I
L. A. Groves, Li, N. , VanBerlo, B. , Veinberg, N. , Peters, T. M. , and Chen, E. C. S. , “Improving central line needle insertions using in-situ vascular reconstructions”, Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, pp. 1-7, 2020.
D. Wu et al., “Improving the Generalizability and Performance of an Ultrasound Deep Learning Model Using Limited Multicenter Data for Lung Sliding Artifact Identification”, Diagnostics, vol. 14, no. 11, p. 1081, 2024.
B. VanBerlo, Ross, M. A. S. , Rivard, J. , and Booker, R. , “Interpretable Machine Learning Approaches to Prediction of Chronic Homelessness”, pp. 1–14, 2020.
B. VanBerlo, Wong, A. , Hoey, J. , and Arntfield, R. , “Intra-video positive pairs in self-supervised learning for ultrasound”, Frontiers in Imaging, vol. 3, 2024.
P
C. Dave et al., “Prospective Real-Time Validation of a Lung Ultrasound Deep Learning Model in the ICU”, Critical Care Medicine, vol. 51, pp. 301–309, 2023.
S
B. VanBerlo, Li, B. , Hoey, J. , and Wong, A. , “Self-Supervised Pretraining Improves Performance and Inference Efficiency in Multiple Lung Ultrasound Interpretation Tasks”, IEEE Access, vol. 11, pp. 135696-135707, 2023.
B. VanBerlo, Hoey, J. , and Wong, A. , “A Survey of the Impact of Self-Supervised Pretraining for Diagnostic Tasks in Medical X-ray, CT, MRI, and Ultrasound”, BMC Medical Imaging, vol. 24, no. 1, p. 79, 2024.