Osteo HRNet

July 11, 2023

Researchers from the Indian Institute of Technology (IIT) Guwahati have developed an AI-based framework called Osteo HRNet. This framework aims to revolutionize the assessment of Knee Osteoarthritis (OA) severity through the automatic analysis of X-ray images.

Addressing a prevalent condition

Knee osteoarthritis affects a significant portion of the population in India, with a prevalence rate of 28 percent. The condition poses challenges as there is no known cure, except for total joint replacement in advanced stages. Early diagnosis becomes crucial for effective pain management and corrective measures.

Overcoming limitations of traditional methods

While MRI and CT scans offer a 3D view of knee joints for accurate diagnosis, their limited availability and high cost hinder their widespread use. X-ray imaging, on the other hand, emerges as a more economically feasible option for routine diagnosis.

Introducing the Osteo HRNet framework

The Osteo HRNet framework developed by the researchers at IIT Guwahati utilizes deep learning techniques to assess the severity of Knee OA. It incorporates the Kellgren and Lawrence (KL) grading scale, a widely accepted standard for classifying the disease severity. The framework harnesses the power of the High-Resolution Network (HRNet) to capture multi-scale features of knee X-rays, enhancing the accuracy of the analysis.

Advancing diagnosis and treatment

The Osteo HRNet framework is poised to bring about significant advancements in diagnosing Knee OA. By automatically assessing the severity level of the disease, medical practitioners can make more informed decisions remotely, leading to more accurate diagnoses and tailored treatment plans. The framework’s ability to pinpoint the medically crucial areas further enhances its utility.

Future prospects

The researchers are committed to further refining the AI-based model to accommodate inexpensive radiographic modalities, such as low-resolution images or photos captured using smartphones. This would enable efficient deployment of the model in resource-constrained environments, empowering medical professionals to obtain initial and accurate diagnoses. The potential of this work extends to addressing the shortage of skilled personnel, particularly in rural India.

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