Mumbai-based startup Qure uses an AI imaging tool qER to save precious minutes for emergency room staff to take action based on head CT scans. After deployment in India and several other countries, qER is now entering the US where 75 million CT scans are performed every year. A couple of weeks ago, Qure received US FDA 510 (k) clearance for this product. What makes it special is a four-in-one clearance. The tool has been cleared for triaging four critical conditions--intracranial bleeds, mass effect (due to spaces in the brain filling up), midline shift (in the brain's alignment), and cranial fractures.

A chest X-ray, analyzed by Qure.ai's software, picks up on abnormalities that suggest the likelihood of COVID-19 infection. X-rays are one of the quickest, simplest ways to diagnose the disease, and an army of AI specialists around the world are trying to speed up how the images are used to find cases. Most cite the lack of data as the prime obstacle to broader adoption of AI. For all the frantic effort to coordinate life-saving work around the globe during the COVID-19 pandemic, the digital age finds itself hampered in one very specific respect: information. Teams of artificial intelligence researchers are trying to bring decades of technology to bear on the problem of diagnosing and treating the disease, but the data they need to develop their software programs is scattered around the globe, making it practically inaccessible. The painful lack of data is evident in one particular use case for AI, the development of diagnostic tests for COVID-19 based on X-rays or on "computed tomography" scans of the lungs.

No one knows who gave Rahul Roy tuberculosis. Roy's charmed life as a successful trader involved traveling in his Mercedes C class between his apartment on the plush Nepean Sea Road in South Mumbai and offices in Bombay Stock Exchange. He cared little for Mumbai's weather. He seldom rolled down his car windows – his ambient atmosphere, optimized for his comfort, rarely changed. Historically TB, or "consumption" as it was known, was a Bohemian malady; the chronic suffering produced a rhapsody which produced fine art. TB was fashionable in Victorian Britain, in part, because consumption, like aristocracy, was thought to be hereditary. Even after Robert Koch discovered that the cause of TB was a rod-shaped bacterium – Mycobacterium Tuberculosis (MTB), TB had a special status denied to its immoral peer, Syphilis, and unaesthetic cousin, leprosy. TB became egalitarian in the early twentieth century but retained an aristocratic noblesse oblige. George Orwell may have contracted TB when he voluntarily lived with miners in crowded squalor to understand poverty. Unlike Orwell, Roy had no pretentions of solidarity with poor people. For Roy, there was nothing heroic about getting TB. He was embarrassed not because of TB's infectivity; TB sanitariums are a thing of the past. TB signaled social class decline. He believed rickshawallahs, not traders, got TB.

Of the world's 10 million people in the world diagnosed with both tuberculosis (TB) and drug-resistant tuberculosis in 2017, 2.7 million live in India, making us a country with the highest burden of the disease, according to the World Health Organization (WHO). Using the contact-free sensor that's placed under your mattress, Dozee tracks and analyzes your heart health, respiration, sleep quality, stress levels and more. What's worse, many remain undiagnosed and those who are detected with TB are only diagnosed weeks after they get it. With this delay, these unsuspecting carriers spread the disease to others in their homes or workplaces. This is particularly a risk for young children.

If you follow the recent advances in medical technology and artificial intelligence, you may have heard people make bold claims that AI will replace tomorrow's doctors. While there are still ways to go for technology to reach these sci-fi levels, many companies are actively designing AI systems that will accompany doctors or assist them with their daily tasks. One particularly challenging task has been to enable algorithms to examine medical images and make intelligent conclusions, create reports, or provide recommendations. CEO, Prashant Warier, about the strides his company has made in automating the analysis of head CTs and chest X-rays. Mohammad Saleh, Medgadget: Can you tell us about your background and how you came to be a part of Qure.ai? I have been a data scientist for pretty much my whole career.

New Delhi: We profile two enterprise tech start-ups that are using artificial intelligence (AI) to solve real world problems. TransOrg Analytics is a Gurgaon-based data science company that uses machine learning (ML)algorithms to predict customer behaviour. Founded by Naveen Jain, TransOrg offers automated machine learning solutions on the cloud. Jain and team have built Clonizo (inspired by the word'cloning'), an artificial intelligence (AI) engine that identifies customers who demonstrate similar behaviours to an "ideal" group of customers. It not only helps in identifying customer look-alikes (clones) but also in predicting customer behaviours.

That's how quickly brain damage happen when the cells get no oxygen in a stroke or in some brain injuries. Both can have tragic consequences -- paralysis, memory loss, speech difficulties and even death. But doctors can't start treatment without an initial diagnosis, and that requires reading a CT scan as soon as the test's completed. Unfortunately, that's not what usually happens, said Prashant Warier, co-founder of Qure.ai, a member of our Inception startup accelerator program. "Radiologists typically have a backlog of cases," he said.

Reading a chest X-ray is tough. So much so that even radiologists get it right only around 70-80% of the time. What if they got help from'machine eyes'? It was trained on over 1.5 million X-rays to detect 15 chest abnormalities, ranging from tuberculosis to potentially cancerous lung nodules. To test the product, Qure.ai, in December 2017, began collaborating with radiologists at the Bengaluru hospitals of the Columbia Asia health-care group.

Radiologists, technicians and doctors are hard to find in small towns, so entrepreneurs are using machine learning and artificial intelligence to bridge the gap BENGALURU: As a researcher at Xerox Research Centre in Bengaluru, healthcare researcher Geetha Manjunath always wondered why thermal images weren't used to detect breast cancer. "I thought it would be a non-invasive, non-radiation and non-intrusive method," she says, adding that some women are reluctant to have regular mammograms. She spent three years doing her research and analysis and in 2016, co-founded Niramai, an artificial intelligence-based healthcare startup which uses thermal imaging and artificial intelligence (AI) to screen for breast cancer. Niramai has signed up a couple of hospitals for its subscription model. More than a billion diagnostic tests are done in India every year in thousands of small labs and centres, many without the right equipment.