A DATASET AT A GLANCE...

Osteoarthritis of the knee refers to the wear and tear of the cartilage or tissues cushioning the knee bones, causing swelling and pain that makes people harder to move. Though this disease is common among the aged individuals, it is also found to affect the younger adults in recent times because of being obese, working with repetitive or continuous limb movements, affected by injury, suffering from nutritional deficiency or getting it as a side effect of other health ailments. Generally, the onset of this disease or its severity can be diagnosed using X-rays with the Kellgren-Lawrence (KL) grading system. The KL grading system classifies the severity of osteoarthritis as healthy, doubtful, minimal, moderate and severe, based on the growth of bone spurs, joint space narrowing and sclerosis. Since the disease can affect the mood and the normal life activities, earlier diagnosis of its severity with improved accuracy is much desired. Researchers aiming to solve this purpose using machine learning approaches could use the “Knee Osteoarthritis Dataset with KL Grading – 2018” dataset from Kaggle (https://www.kaggle.com/datasets/tommyngx/kneeoa). This dataset is acquired from the Osteoarthritis Initiative (OAI), which investigated the biomarkers leading to osteoarthritis. Additionally, this dataset encompassed about 4130 X-ray images with 8260 knee joints, which were acquired from 4796 people within the age range between 45 and 79. Further, the researchers showing interest to use this dataset should acknowledge the following in their contribution.

·   Chen, Pingjun (2018), “Knee Osteoarthritis Severity Grading Dataset”, Mendeley Data, V1, doi: 10.17632/56rmx5bjcr.1