IIT-Madras Team Has Developed Algorithms to Help Detect Multiple Sclerosis

by Dr. Trupti Shirole on  August 4, 2015 at 12:45 AM News on IT in Healthcare
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Multiple Sclerosis (MS) is a disease in which the protective sheath covering the nerves gets destroyed, disrupting the communication between the brain and the rest of the body. This leads to difficulty in speech, sight, and the ability to move. The number of MS patients has increased in India in recent years. It is estimated that there are between 100,000 and 200,000 MS patients in India. According to the All India Institute of Medical Sciences (AIIMS), which carried out a study in 2013 on the patients of multiple sclerosis it treats, about 70-80% of patients were in the 18-35 years age group. Researchers at IIT-Madras (IIT-M) have developed algorithms that could help detect MS which could be easily missed.
 IIT-Madras Team Has Developed Algorithms to Help Detect Multiple Sclerosis
IIT-Madras Team Has Developed Algorithms to Help Detect Multiple Sclerosis

Ganapathy Krishnamurthi, professor in IIT-M's department of engineering design, who led the research, said, "The task of accurate delineation of regions (segmentation) of the brain affected with MS is a difficult and time-consuming affair. Owing to this, significant variability can be observed in the regions marked by different radiologists on the same image. In case of MS, only 50% of the marked area would match each other. Our research focuses on development of automated methods to perform accurate segmentation of disorders such as MS and glioma. These segmentations were important for doctors to obtain quantitative metrics for treatment monitoring and planning, as well as for surgical operations."

Krishnamurthi further added, "While working in collaboration with Thiruvananthapuram's Sree Chitra Thirunal Institute of Medical Sciences and Technology, we identified that accurate labeling of disorder-affected regions in brain MRI could be a difficult affair due to its complex shape and vague boundaries. Moreover, it is a tedious task since radiologists cannot visualize in 3D and the task needs to be performed slice by slice. This led to research on automated methods for identification of glioma (brain tumors) affected regions from MRI images. However, the core algorithms developed in the process were such that they could be used in the detection of other disorders as well. Multiple Sclerosis is a chronic disease which is visible as several small lesions which can be easily missed. This being a particularly difficult task, we decided to extend the research scope and tackle this problem as well."

Krishnamurthi explained, "Deep Learning are recent methods in machine learning developed based on the interpretation of how human brain and nervous systems work - neural networks. These networks consist of stacked layers consisting of several mathematical models of neurons, which is the computational equivalent of information processing in the brain. Although these methods have been around for more than a decade, recent developments in computational resources have made large and complex networks with near-human performance possible."

Krishnamurthy also said, "Our next steps in this endeavor would be to test extensively with more clinical data to assess the effectiveness of the software and subsequently deploy the software for use by our clinical collaborators. Based on the performance in a clinical setting (purely for evaluation) we will try to get regulatory approval for our software. Since training accurate models require large amounts of data, ethical committee approvals from various hospitals would be required. We are already in collaboration with Sree Chitra Thirunal Hospital and are confident of seeing the product put in use in a span of two to four years."

Krishnamurthi added, "These methods when implemented can substantially reduce the time and cost for diagnosis of various brain diseases like MS. The algorithms for image analysis are basically a tool for diagnosis and aids clinicians to judge progression of disease and efficacy of therapy. For instance, in large clinical trials these automated algorithms can be used to analyze patient data."

Source: IANS

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