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Challenges in Deep Learning Applied to the Knee Joint Magnetic Resonance Imaging: A Survey

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Proceedings of Seventh International Congress on Information and Communication Technology

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 465))

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Abstract

Worldwide, knee joint complaints are most frequent in all age groups. Trauma, inflammation, infection, tumor, or aging that can damage the knee joint are detected with an MRI. MRI represents a standard technique for assessing knee joint anatomical structures, and it supports diagnosis, disease monitoring, or treatment planning. However, the reading and assessment of knee MRIs are time-consuming and can result in misdiagnosis. Therefore, it is crucial to elaborate intelligent and standardized methodologies of MRI investigation, to discover various knee lesions, increase diagnostic efficiency, and reduce bias or error in the evaluation due to human limitations such as fatigue, to name only one of them. This article reviews recent works that address the application of deep learning and discusses the related challenges in knee joint MRI analysis.

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Authors and Affiliations

  1. “1 Decembrie 1918”, University of Alba Iulia, Alba Iulia, Romania

    Manuella Kadar

  2. Victor Babes University of Medicine and Pharmacy, Timisoara, Romania

    Alexei Botnari

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  1. Manuella Kadar
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  2. Alexei Botnari
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Correspondence to Manuella Kadar .

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Editors and Affiliations

  1. Middlesex University, London, UK

    Xin-She Yang

  2. The University of Reading, Reading, UK

    Simon Sherratt

  3. JIS University, Kolkata, India

    Nilanjan Dey

  4. Global Knowledge Research Foundation, Ahmedabad, India

    Amit Joshi

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Kadar, M., Botnari, A. (2023). Challenges in Deep Learning Applied to the Knee Joint Magnetic Resonance Imaging: A Survey. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Seventh International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 465. Springer, Singapore. https://doi.org/10.1007/978-981-19-2397-5_42

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