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

• Published in: National Academy of Sciences, India. Proceedings. Section B. Biological Sciences Vol. 94; no. 2; pp. 423 - 438
• Main Authors: Malathi, S. Y., Bharamagoudar, Geeta R.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: New Delhi Springer India 01.04.2024; Springer Nature B.V
• Subjects: Arthritis; Behavioral Sciences; Biomedical and Life Sciences; Cartilage (articular); Cartilage diseases; Deep learning; Femur; Knee; Life Sciences; Mobility; Neural networks; Nucleic Acid Chemistry; Osteoarthritis; Plant Biochemistry; Quality of life; Radiography; Research Article; Statistical analysis; Tibia; Deep learning; Radiography; Knee osteoarthritis; X-ray
• ISSN: 0369-8211; 2250-1746
• DOI: 10.1007/s40011-023-01545-5

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.