Knee Osteoarthritis is the most common musculoskeletal disorder in the world
Researchers from the Indian Institute of Technology Guwahati have developed a Deep Learning (DL)-based framework, namely OsteoHRNet, that automatically assesses the Knee Osteoarthritis (OA) severity from X-rays images. This AI-based model can be used to detect the severity level of the disease and assist medical practitioners remotely for a more accurate diagnosis.
Researchers have been working to enhance automatic knee osteoarthritis detection from X-ray images or radiographs to assist clinical evaluation. In this direction, the IIT Guwahati team has developed an AI-based model to automatically assess the severity of Knee OA.
Speaking about the Knee OA prediction model, 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. The AI-based model 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.
Speaking about the further application of this work 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 easy to available modalities such as very low-resolution radiographic images or even photos taken from radiographic plates by a smartphone”
The team is further working to reconfigure these models in such a way that they can be deployed in resource-constrained devices so that medical professionals can easily get an initial but accurate guess for the diagnosis. This work has the potential to mitigate the severe shortage of skilled personnel in this field, especially in rural India.
The research has been accepted for publication in the journal Multimedia Tools and Applications. It was carried out by Rohit Kumar Jain, an MTech Data Science student (now graduated) under the joint supervision of Prof. Sur and Dr Ghosh. The research team also includes former Ph.D. students of Prof. Sur at IIT Guwahati, Dr Prasen Kumar Sharma, and Dr Sibaji Gaj (now a research fellow at Cleveland Clinic, Ohio, USA).