Browsing ODA Open Digital Archive by Author "Strumke, Inga"

Now showing items 1-7 of 7

    • Artificial intelligence in dry eye disease 

      Storås, Andrea Marheim; Strumke, Inga; Riegler, Michael Alexander; Grauslund, Jakob; Hammer, Hugo Lewi; Yazidi, Anis; Halvorsen, Pål; Gundersen, Kjell Gunnar; Utheim, Tor Paaske; Jackson, Catherine Joan (The ocular surface;Volume 23, January 2022, Peer reviewed; Journal article, 2021-12-01)
      Dry eye disease (DED) has a prevalence of between 5 and 50%, depending on the diagnostic criteria used and population under study. However, it remains one of the most underdiagnosed and undertreated conditions in ophthalmology. ...

    • Explaining deep neural networks for knowledge discovery in electrocardiogram analysis 

      Hicks, Steven; Isaksen, Jonas L; Thambawita, Vajira L B; Ghouse, Jonas; Ahlberg, Gustav; Linneberg, Allan; Grarup, Niels; Strumke, Inga; Ellervik, Christina; Olesen, Morten Salling; Hansen, Torben; Graff, Claus; Holstein-Rathlou, Niels-Henrik; Halvorsen, Pål; Maleckar, Mary Margot Catherine; Riegler, Michael; Kanters, Jørgen K (Scientific Reports;11, Article number: 10949 (2021), Peer reviewed; Journal article, 2021-05-26)
      Deep learning-based tools may annotate and interpret medical data more quickly, consistently, and accurately than medical doctors. However, as medical doctors are ultimately responsible for clinical decision-making, any ...

    • Huldra: a framework for collecting crowdsourced feedback on multimedia assets 

      Hammou, Malek; Midoglu, Cise; Hicks, Steven; Storås, Andrea; Sabet, Saeed; Strumke, Inga; Riegler, Michael; Halvorsen, Pål (MMSys: ACM Multimedia Systems;MMSys '22: Proceedings of the 13th ACM Multimedia Systems Conference, Conference object, 2022)
      Collecting crowdsourced feedback to evaluate, rank, or score multimedia content can be cumbersome and time-consuming. Most of the existing survey tools are complicated, hard to customize, or tailored for a specific asset ...

    • Identifying Important Proteins in Meibomian Gland Dysfunction with Explainable Artificial Intelligence 

      Storås, Andrea; Magnø, Morten Schjerven; Fineide, Fredrik; Thiede, Bernd; Chen, Xiangjun; Strumke, Inga; Halvorsen, Pål; Utheim, Tor Paaske; Riegler, Michael Alexander (Peer reviewed; Journal article, 2023)
      Meibomian gland dysfunction is the most common cause of dry eye disease and leads to significantly reduced quality of life and social burdens. Because meibomian gland dysfunction results in impaired function of the tear ...

    • Impact of image resolution on deep learning performance in endoscopy image classification: An experimental study using a large dataset of endoscopic images 

      Thambawita, Vajira L B; Strumke, Inga; Hicks, Steven; Halvorsen, Pål; Parasa, Sravanthi; Riegler, Michael (Diagnostics;Volume 11 / Issue 12, Peer reviewed; Journal article, 2021-11-24)
      Recent trials have evaluated the efficacy of deep convolutional neural network (CNN)- based AI systems to improve lesion detection and characterization in endoscopy. Impressive results are achieved, but many medical studies ...

    • On evaluation metrics for medical applications of artificial intelligence 

      Hicks, Steven A.; Strumke, Inga; Thambawita, Vajira L B; Hammou, Malek; Riegler, Michael Alexander; Halvorsen, Pål (Scientific Reports;12, Article number: 5979 (2022), Peer reviewed; Journal article, 2022-04-08)
      Clinicians and software developers need to understand how proposed machine learning (ML) models could improve patient care. No single metric captures all the desirable properties of a model, which is why several metrics ...

    • Usefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysis 

      Storås, Andrea; Andersen, Ole Emil; Lockhart, Sam; Thielemann, Roman; Gnesin, Filip; Thambawita, Vajira L B; Hicks, Steven; Kanters, Jørgen K.; Strumke, Inga; Halvorsen, Pål; Riegler, Michael (Peer reviewed; Journal article, 2023)
      Deep neural networks are complex machine learning models that have shown promising results in analyzing high-dimensional data such as those collected from medical examinations. Such models have the potential to provide ...