{"id":103490,"date":"2019-11-11T17:01:27","date_gmt":"2019-11-11T17:01:27","guid":{"rendered":"https:\/\/news.microsoft.com\/?p=435144"},"modified":"2019-11-11T17:01:27","modified_gmt":"2019-11-11T17:01:27","slug":"srl-diagnostics-microsoft-consortium-creates-new-ai-tool-to-diagnose-cervical-cancer-faster","status":"publish","type":"post","link":"https:\/\/sickgaming.net\/blog\/2019\/11\/11\/srl-diagnostics-microsoft-consortium-creates-new-ai-tool-to-diagnose-cervical-cancer-faster\/","title":{"rendered":"SRL Diagnostics-Microsoft consortium creates new AI tool to diagnose cervical cancer faster"},"content":{"rendered":"

A cytopathologist at SRL Diagnostics\u2019 Central Reference Laboratory in Mumbai, screens a Pap smear sample for the screening of cervical cancer under his microscope. His trained eyes work with an apparent effortlessness. However, there is an unspoken urgency in his actions as he strives to complete the set of samples for the day. Along with his team of five members, he screens about 200 slides for cervical cancer every day, apart from another 100 slides for diagnosing other types of cancers.<\/p>\n

SRL Diagnostics, the largest diagnostics laboratory company in India, has been witnessing an increase in the demand for cervical cancer screening.<\/p>\n

According to estimates by the World Health Organization (WHO)<\/a>, cervical cancer is the fourth most frequent cancer among women worldwide. India accounts for 16% of the global burden of the disease. Unlike breast cancer, which is more prevalent in urban India, cervical cancer is frequent in the rural regions of the country where literacy and awareness of the fatal disease is low. Effective screening and early detection are the key measures to reduce its incidence.<\/p>\n

\u201cThe proportion of cytopathologists in India is very low with respect to the number of patients. At SRL Diagnostics, we receive more than 100,000 Pap smear samples every year and there are only a few trained cytopathologists who are supposed to examine such slides. What\u2019s more, nearly 98% of these samples are normal and it\u2019s only the remaining 2% that requires further intervention. We were looking for ways to ensure our cytopathologists were able to find those 2% abnormal samples faster,\u201d says Dr. Arnab Roy, Technical Lead \u2013 New Initiatives & Knowledge Management, SRL Diagnostics.<\/p>\n

\"Arindam
Arindam Haldar, CEO, SRL Diagnostics<\/figcaption><\/figure>\n

In September 2018, SRL Diagnostics partnered with Microsoft<\/a> to create an AI Network for Pathology to ease the burden of cytopathologists and histopathologists. The idea was to co-create an AI-powered API that would do the first-level check and offload normal slides to help cytopathologists spend more time on what matters most \u2013 slides that show signs of abnormality.<\/p>\n

\u201cSRL Diagnostics has always been committed to championing innovation in the field of clinical diagnostics in India and has led the industry by embracing change as early adopters of many in vitro diagnostics technologies. Our partnership with Microsoft to co-create solutions having the potential to generate both social and business value, is another first-of-its-kind effort in the Indian diagnostics industry,\u201d says Arindam Haldar, CEO, SRL Diagnostics.<\/p>\n

\u201cWe looked at various elements of histopathology and homed in on the early detection of cervical cancer because it is a leading killer disease for women and cervical smear based screening, which is the gold standard, is patchy in implementation across the planet. We thought it was a good opportunity to apply AI and automation to drive efficiency and save lives,\u201d says Prashant Gupta, Program Director, Microsoft Azure Global Engineering.<\/p>\n

Building the cervical cancer image detection API<\/strong><\/p>\n

Developing the Cervical Cancer Image Detection API required the AI algorithm to assess smear tile images (region of interest) as a trained cytopathologist would examine them. To enable this, cytopathologists studied digitally scanned versions of Whole Slide Imaging (WSI) slides manually and marked their observations, which were used as training data for the AI model.<\/p>\n

Initially, SRL Diagnostics appointed one cytopathologist to annotate the image slides. Each WSI comprises as many as 1,800 tile images and it was difficult for a single individual to interpret a large quantity of image tiles from each slide in ways that can be consumed by the AI algorithm. And, then, there was the challenge of subjectivity too.<\/p>\n

\u201cDifferent cytopathologists examine different elements in a smear slide in a unique manner even if the overall diagnosis is the same. This is the subjectivity element in the whole process, which many a time is linked to the experience of the expert,\u201d reveals Dr. Roy.<\/p>\n

\"A
A digitally scanned version of a Whole Slide Imaging (WSI) slide, which is used to train the AI model.<\/figcaption><\/figure>\n

\u201cWe wanted to create an AI algorithm that could identify areas that everybody was looking at and create a consensus on the areas assessed,\u201d adds Manish Gupta, Principal Applied Researcher, Microsoft Azure Global Engineering, who worked closely with the team at SRL Diagnostics to develop the Cervical Cancer Image Detection API.<\/p>\n

The project was then expanded to include five cytopathologists across multiple labs and different locations. Their efforts resulted in the annotation of thousands of tile images of cervical smear, each comprising about 300-400 cells. The issue of subjectivity was addressed by creating discordant and concordant notes on each sample image. The images for which annotations were found to be discordant \u2013 that is if they were viewed differently by three team members \u2013 were sent to senior cytopathologists for final analysis.<\/p>\n

The collaboration has already started showing results with the first API for screening cervical cancer ready for internal preview at SRL Diagnostics.<\/p>\n

As part of the new workflow, the large digital WSI scans from Device Image Scanners are uploaded to Azure where the Cervical Cancer Image Detection API runs. The AI-powered Cervical Cancer Image Detection API can quickly screen liquid-based cytology slide images for detection of cervical cancer in the early stages and return insights to pathologists in labs.<\/p>\n

The AI model can now differentiate between normal and abnormal smear slides with accuracy and is currently under validation in labs. It can also classify smear slides based on the seven-subtypes of cervical cytopathological scale. The classification includes interpretations ranging between normal and the pre-cancerous to cancerous stages. The purported use of the model lies in assisting cytopathologists to review fewer areas, 20 as of now, on a whole slide liquid-based cytology image and validate the positive cases thus bringing in greater efficiency and speeding up the initial screening process.<\/p>\n

\u201cThe API has the potential of increasing the productivity of a cytopathology section by about four times. In a future scenario of automated slide preparation with assistance from AI, cytopathologists can do a job in two hours what would earlier take about eight hours!\u201d exclaims Dr. Roy.<\/p>\n","protected":false},"excerpt":{"rendered":"

A cytopathologist at SRL Diagnostics\u2019 Central Reference Laboratory in Mumbai, screens a Pap smear sample for the screening of cervical cancer under his microscope. His trained eyes work with an apparent effortlessness. However, there is an unspoken urgency in his actions as he strives to complete the set of samples for the day. Along with […]<\/p>\n","protected":false},"author":2,"featured_media":103491,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[49],"tags":[135,50],"class_list":["post-103490","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-microsoft-news","tag-artificial-intelligence","tag-recent-news"],"_links":{"self":[{"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/posts\/103490","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/comments?post=103490"}],"version-history":[{"count":0,"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/posts\/103490\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/media\/103491"}],"wp:attachment":[{"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/media?parent=103490"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/categories?post=103490"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/tags?post=103490"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}