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4. Author Correction: High-resolution, large-scale laboratory measurements of a sandy beach and dynamic cobble berm revetment

Author

Blenkinsopp, Chris E., Bayle, Paul M., Conley, Daniel C., Masselink, Gerd, Gulson, Emily, Kelly, Isabel, Almar, Rafael, Turner, Ian L., Baldock, Tom E., Beuzen, Tomas, McCall, Robert T., Rijper, Huub, Reniers, Ad, Troch, Peter, Gallach-Sanchez, David, Hunter, Alan J., Bryan, Oscar, Hennessey, Gwyn, Ganderton, Peter, Tissier, Marion, Kudella, Matthias, and Schimmels, Stefan

Published
2021
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5. High-resolution, large-scale laboratory measurements of a sandy beach and dynamic cobble berm revetment

Author

Blenkinsopp, Chris E., Bayle, Paul M., Conley, Daniel C., Masselink, Gerd, Gulson, Emily, Kelly, Isabel, Almar, Rafael, Turner, Ian L., Baldock, Tom E., Beuzen, Tomas, McCall, Robert T., Rijper, Huub, Reniers, Ad, Troch, Peter, Gallach-Sanchez, David, Hunter, Alan J., Bryan, Oscar, Hennessey, Gwyn, Ganderton, Peter, Tissier, Marion, Kudella, Matthias, and Schimmels, Stefan

Published
2021
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7. Blind testing of shoreline evolution models

Author

Montaño, Jennifer, Coco, Giovanni, Antolínez, Jose A. A., Beuzen, Tomas, Bryan, Karin R., Cagigal, Laura, Castelle, Bruno, Davidson, Mark A., Goldstein, Evan B., Ibaceta, Raimundo, Idier, Déborah, Ludka, Bonnie C., Masoud-Ansari, Sina, Méndez, Fernando J., Murray, A. Brad, Plant, Nathaniel G., Ratliff, Katherine M., Robinet, Arthur, Rueda, Ana, Sénéchal, Nadia, Simmons, Joshua A., Splinter, Kristen D., Stephens, Scott, Townend, Ian, Vitousek, Sean, and Vos, Kilian

Published
2020
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9. Labeling Poststorm Coastal Imagery for Machine Learning: Measurement of Interrater Agreement

Author

Goldstein, Evan B., primary, Buscombe, Daniel, additional, Lazarus, Eli D., additional, Mohanty, Somya D., additional, Rafique, Shah Nafis, additional, Anarde, Katherine A., additional, Ashton, Andrew D., additional, Beuzen, Tomas, additional, Castagno, Katherine A., additional, Cohn, Nicholas, additional, Conlin, Matthew P., additional, Ellenson, Ashley, additional, Gillen, Megan, additional, Hovenga, Paige A., additional, Over, Jin‐Si R., additional, Palermo, Rose V., additional, Ratliff, Katherine M., additional, Reeves, Ian R. B., additional, Sanborn, Lily H., additional, Straub, Jessamin A., additional, Taylor, Luke A., additional, Wallace, Elizabeth J., additional, Warrick, Jonathan, additional, Wernette, Phillipe, additional, and Williams, Hannah E., additional

Published
2021
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10. Machine Learning Decision Support for Detecting Lipohypertrophy With Bedside Ultrasound: Proof-of-Concept Study.

Author

Bandari, Ela, Beuzen, Tomas, Habashy, Lara, Raza, Javairia, Xudong Yang, Kapeluto, Jordanna, Meneilly, Graydon, and Madden, Kenneth

Subjects
INSULIN therapy, DIAGNOSIS of diabetes, ULTRASONIC imaging, MACHINE learning, CONVOLUTIONAL neural networks
Abstract

Background: The most common dermatological complication of insulin therapy is lipohypertrophy. Objective: As a proof of concept, we built and tested an automated model using a convolutional neural network (CNN) to detect the presence of lipohypertrophy in ultrasound images. Methods: Ultrasound images were obtained in a blinded fashion using a portable GE LOGIQ e machine with an L8-18I-D probe (5-18 MHz; GE Healthcare). The data were split into train, validation, and test splits of 70%, 15%, and 15%, respectively. Given the small size of the data set, image augmentation techniques were used to expand the size of the training set and improve the model’s generalizability. To compare the performance of the different architectures, the team considered the accuracy and recall of the models when tested on our test set. Results: The DenseNet CNN architecture was found to have the highest accuracy (76%) and recall (76%) in detecting lipohypertrophy in ultrasound images compared to other CNN architectures. Additional work showed that the YOLOv5m object detection model could be used to help detect the approximate location of lipohypertrophy in ultrasound images identified as containing lipohypertrophy by the DenseNet CNN. Conclusions: We were able to demonstrate the ability of machine learning approaches to automate the process of detecting and locating lipohypertrophy. [ABSTRACT FROM AUTHOR]

Published
2022
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