Abstract #2988

Deep learning-based detection of liver disease using MRI

Mark A Pinnock^1,2, Yipeng Hu^1,2, Alan Bainbridge³, David Atkinson⁴, Rajeshwar P Mookerjee⁵, Stuart A Taylor⁴, Dean C Barratt^1,2, and Manil D Chouhan⁴

¹Centre for Medical Image Computing, University College London, London, United Kingdom, ²Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom, ³Department of Medical Physics and Biomedical Engineering, University College London Hospitals NHS Foundation Trust, London, United Kingdom, ⁴Centre for Medical Imaging, Division of Medicine, University College London, London, United Kingdom, ⁵Institute for Liver and Digestive Health, Division of Medicine, University College London, London, United Kingdom

Traditional approaches to MRI detection of liver disease require specialist hardware, sequences and post-processing. Here we propose a deep learning (DL) based model for the detection of liver disease using standard T2-weighted anatomical sequences, as an early feasibility study for the potential of DL-based classification of liver disease severity. Our DL model achieved a diagnostic accuracy of 0.92 on unseen data and achieved a test accuracy of 0.75 when trained with relevant anatomical segmentation masks without images, demonstrating potential scanner/sequence independence. Lastly, we used DL interpretability techniques to analyse failure cases.

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