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A Novel Method Based on CNN-LSTM to Characterize Knee Osteoarthritis from Radiography

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Proceedings of the National Academy of Sciences, India Section B: Biological Sciences Aims and scope Submit manuscript

Abstract

This is particularly true for the senior population, whose quality of life has been drastically reduced as a result of the increasing incidence of several health problems. Over 27 million people in the United States suffer from osteoarthritis of the knee (OAK), a painful condition that may severely limit mobility. When the articular cartilage between the tibia and femur in the knee is damaged, osteoarthritis of the knee develops. OAK symptoms include a loss of mobility and the inability to walk normally due to knee pain, detected using an X-ray. We detail here a novel method of evaluating the severity of knee osteoarthritis (OA) by X-ray analysis. Modern methods are comprised of pre-processing, feature extraction using a convolutional neural network (CNN), and classification with latent semantic modeling (LSM) (LSTM). Data from the osteoarthritis initiatives (OAI) database, which is available to the public, was utilized to evaluate the methodology proposed. The current method has been shown to be effective, and the OAI database has information on KL grade assessment for both knees. OAK is the subject of state-of-the-art, global observational investigation by experts using a program called OAI. This collection was created to serve as a one-stop shop for researchers seeking the scholarly materials they need to systematically examine OA indicators as a possible endpoint for the advanced stages of the illness. The statistics reveal a mean accuracy of 100%. When compared to earlier deep learning approaches, these outcomes are much superior.

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Data Availability

The dataset will be made available on request.

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Acknowledgements

We gratefully acknowledge the assistance and cooperation extended by Dr. Madhuchandra R, Associate professor, department of Orthopedics, Karnataka Institute of Medical Science(KIMS), Hubballi.

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There was no specific grant from a funding agency in the public, commercial, or nonprofit sectors for this research.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, KLE Institute of Technology Hubballi, Hubballi, Karnataka, India

    S. Y. Malathi & Geeta R. Bharamagoudar

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  1. S. Y. Malathi
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  2. Geeta R. Bharamagoudar
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Contributions

MSY contribued to data creation, visualization, software, validation, writing—original draft preparation; GRB contribued to data collection, methodology, conceptualization, writing—review and editing.

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Correspondence to S. Y. Malathi.

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Significance statement: This research has significant implications for improving the quality of life for individuals suffering from knee osteoarthritis. By providing a reliable and accurate means of assessment, it may lead to earlier detection, better treatment decisions, and improved patient outcomes. Furthermore, the utilization of deep learning techniques in this area establishes a new benchmark for forthcoming research and innovation in medical image analysis, particularly for osteoarthritis and other associated health conditions.

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Malathi, S.Y., Bharamagoudar, G.R. A Novel Method Based on CNN-LSTM to Characterize Knee Osteoarthritis from Radiography. Proc. Natl. Acad. Sci., India, Sect. B Biol. Sci. 94, 423–438 (2024). https://doi.org/10.1007/s40011-023-01545-5

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