Using deep learning–derived image features in radiologic time series to make personalised predictions: proof of concept in colonic transit data

OBJECTIVES: Siamese neural networks (SNN) were used to classify the presence of radiopaque beads as part of a colonic transit time study (CTS). The SNN output was then used as a feature in a time series model to predict progression through a CTS. METHODS: This retrospective study included all patien...

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Detalles Bibliográficos
Autores principales: Kelly, Brendan S., Mathur, Prateek, Plesniar, Jan, Lawlor, Aonghus, Killeen, Ronan P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
Materias:
Experimental
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10244854/
https://www.ncbi.nlm.nih.gov/pubmed/37284869
http://dx.doi.org/10.1007/s00330-023-09769-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10244854/
https://www.ncbi.nlm.nih.gov/pubmed/37284869
http://dx.doi.org/10.1007/s00330-023-09769-9

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