Researchers from the Indian Institute of Technology, Guwahati have developed a Deep Learning (DL)-based framework, ‘OsteoHRNet’, that automatically assesses the Knee Osteoarthritis (OA) severity from X-rays images.
This AI-based model can assess the severity level of the disease and assist medical practitioners remotely for a more accurate diagnosis.
Researchers say that Knee Osteoarthritis is the most common musculoskeletal disorder in the world and has a prevalence of 28% in India. There is no possible cure for Knee OA except total joint replacement at an advanced stage hence an early diagnosis is essential for pain management and behavioral corrections.
MRI and CT scans provide a 3-D image of the knee joints for effective diagnosis of Knee OA but their availability is limited and expensive. For routine diagnosis X-Ray imaging is very effective and more economically feasible.
The team has developed an AI-based model to automatically assess the severity of Knee OA.
Dr Palash Ghosh, Assistant Professor, Department of Mathematics, IIT Guwahati, said, “Compared to others, our model can pinpoint the area which is medically most important to decide the severity level of knee osteoarthritis thus helping medical practitioners detect the disease accurately at an early stage.”
The proposed approach is not a direct plug-and-play of popular deep models. It uses an efficient Deep Convolutional Neural Network (CNN) i.e an algorithm from image recognition.
This model predicts knee OA severity according to the World Health Organization (WHO) approved Kellgren and Lawrence (KL) grading scale that ranges from grade 0 (low severity) to 4 (high severity). It is built upon one of the most recent deep models, called the High-Resolution Network (HRNet), to capture the multi-scale features of knee X-rays.
Prof Arijit Sur, Department of Computer Science and Engineering, IIT Guwahati said, “Although simple, the proposed model may be a good starting point for analyzing inexpensive radiographic modalities such as X-rays. Our group is currently focusing on how efficient Deep Learning based models can be designed so that we can work on inexpensive and easily available modalities such as very low-resolution radiographic images or even photos taken from radiographic plates by a smartphone.”
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