Friday December 14, 2018

IIT-M researchers develop algorithms to detect MS with accuracy

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New Delhi: Researchers at IIT-Madras (IIT-M) have developed algorithms that could help detect multiple sclerosis (MS) which, since it is visible as several small lesions, could be easily missed.

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 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 percent of the marked areas would match each other,” Ganapathy Krishnamurthi, professor in IIT-M’s department of engineering design, who led the research, told a media outlet.

He added that the team’s research focusses on development of automated methods to perform accurate segmentation of disorders, such as MS and Glioma.

Explaining further, he said that these segmentations were important for doctors to obtain quantitative metrics for treatment monitoring and planning, as well as for surgical operations.

The number of multiple sclerosis 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, which carried out a study in 2013 on the patients of multiple sclerosis it treats, about 70-80 percent of patients were in the 18-35 age group.

Krishnamurthi shared that while working in collaboration with Thiruvananthapuram’s Sree Chitra Thirunal Institute of Medical Sciences and Technology, the team identified that accurate labelling 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,” he said.

He added that this led to the 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,” he said.

The symptoms of MS include weakness or numbness of limbs, blurring, partial or complete loss of vision, slurred speech, dizziness, tremors, lack of coordination and tingling sensation or pain in the body.

The team, comprising Suthirth Vaidya and Abhijith Chunduru, final year integrated masters (B.Tech+M.Tech) students from the engineering design department under the guidance of Krishnamurthi and M. Ramanathan, used technology known as ‘Deep Learning’, which is inspired by advances in neuroscience and is loosely based on the interpretation of information processing and communication within the nervous system.

“Deep Learning is one of the recent methods developed in machine learning, based on the interpretation of how human brain and nervous systems – the neural networks- work. 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,” Krishnamurthi explained.

Voice recognition on Android smartphone, Google’s self-driving car and automatic photo tagging feature on Facebook are all powered by Deep Learning.

The team, which emerged victorious in the recently held Longitudinal Multiple Sclerosis Segmentation challenge at International Symposium on Biomedical Imaging (ISBI) 2015 at New York, is currently in the process of building a software tool that can be used by clinicians.

“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,” he said.

So, will it make MS treatment/diagnosis cheaper?

“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 tools for diagnosis and aid 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,” Krishnamurthi added.

(IANS)

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Women Hit Especially Hard In Congo’s Worst Ebola Outbreak

For the afflicted, the road to recovery is long and lonely.

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Ebola, WHO, UNICEF, congo, Uganda, women
Congolese health workers register people and take their temperatures before they are vaccinated against Ebola in the village of Mangina in North Kivu province of the Democratic Republic of Congo. VOA

The Democratic Republic of Congo is in the throes of its worst-ever Ebola outbreak, with more than 420 cases in the country’s volatile east, and a mortality rate of just under 60 percent. But this outbreak — the nation’s tenth known Ebola epidemic — is unusual because more than 60 percent of patients are women.

Among them is Baby Benedicte. Her short life has already been unimaginably difficult.

At one month old, she is underweight, at 2.9 kilograms. And she is alone. Her mother had Ebola, and died giving birth to her. She’s spent the last three weeks of her life in a plastic isolation cube, cut off from most human contact. She developed a fever at eight days old and was transferred to this hospital in Beni, a town of some half-million people in the east of the Democratic Republic of Congo.

More than 400 people have been diagnosed with Ebola here since the beginning of August, and more than half of them have died in a nation the size of Western Europe that struggles with insecurity and a lack of the most basic infrastructure and services. That makes this the second-worst Ebola outbreak in history, after the hemorrhagic fever killed more than 11,000 people in West Africa between 2013 and 2016.

This is 10th outbreak to strike the vast country since 1976, when Ebola was first identified in Congo. And this particular outbreak is further complicated by a simmering civil conflict that has plagued this region for more than two decades.

Guido Cornale, UNICEF’s coordinator in the region, says the scope of this outbreak is clear.

“It has become the worst outbreak in Congo, this is not a mystery,” he said.

What is mysterious, however, is the demographics of this outbreak. This time, more than 60 percent of cases are women, says the government’s regional health coordinator, Ndjoloko Tambwe Bathe.

“All the analyses show that this epidemic is feminized. Figures like this are alarming. It’s true that the female cases are more numerous than the male cases,” he said.

Congo, Uganda, ebola, Women
Health workers walk with a boy suspected of having been infected with the Ebola virus, at an Ebola treatment center in Beni, near Congo’s border with Uganda. VOA

Bathe declined to predict when the outbreak might end, though international officials have said it may last another six months. Epidemiologists are still studying why this epidemic is so skewed toward women and children, Cornale said.

“So now we can only guess. And one of the guesses is that woman are the caretakers of sick people at home. So if a family member got sick, who is taking care of him or her? Normally, a woman,” he said.

Or a nurse. Many of those affected are health workers, who are on the front line of battling this epidemic. Nurse Guilaine Mulindwa Masika, spent 16 days in care after a patient transmitted the virus to her. She says it was the fight of her life.

“The pain was enormous, the pain was constant,” she said. “The headache, the diarrhea, the vomiting, and the weakness — it was very, very bad.”

Congo, Ebola, Women
Marie-Roseline Darnycka Belizaire, World Health Organization (WHO) Epidemiology Team Lead, talks to women as part of Ebola contact tracing, in Mangina, Democratic Republic of Congo. VOA

For the afflicted, the road to recovery is long and lonely. Masika and her cured colleagues face weeks of leave from work to ensure the risk of infection is gone. In the main hospital in the city of Beni, families who have recovered live together in a large white tent, kept four meters from human contact by a bright orange plastic cordon. They yell hello at their caretakers, who must don protective gear if they want to get any closer.

And for Baby Benedicte, who is tended to constantly by a nurse covered head to toe in protective gear, the future is uncertain. Medical workers aren’t entirely sure where her father is, or if he is going to come for her.

Also Read: Congo Start Trials For Drugs Against Ebola

She sleeps most of the day, the nurse says, untroubled by the goings-on around her. Meanwhile, the death toll rises. (VOA)