Path Math: How AI Can Find a Way Around Pathologist Shortage

Ever since a Dutch cloth merchant accidentally discovered bacteria in 1676, microscopes have been a critical tool for medicine. Today's microscopes are 800,000 times more powerful than the human eye, but they still need a person to scrutinize what's under the lens.

That person is usually a pathologist - and that's a problem. Worldwide, there are too few of these doctors who interpret lab tests to diagnose, monitor and treat disease.

Now SigTuple, a member of our Inception startup incubator program, is testing an AI microscope that could help address the pathologist shortage. The GPU-powered device automatically scans and analyzes blood smears and other biological samples to detect problems.

One in a Million

The dearth of pathologists is crucial problem in the poorest countries, where patients lacking a proper diagnosis are often given inappropriate treatments, according to studies published this month in The Lancet medical journal. In sub-Saharan Africa, for example, there is a single pathologist for every million people, the journal reported.

But the problem isn't confined to poor countries. In China, there's one pathologist for every 130,000 people, The Lancet reported. That compares with 5.7 per 100,000 people in the U.S., according to a the most recent figures available. And in the U.S., studies predict the number of pathologists will shrink to 3.7 per 100,000 people by 2030.

In India, that's now 1 pathologist per 65,000 people - a total of 20,000 pathologists available to treat the nation of 1.3 billion people, said Tathagato Rai Dastidar, co-founder and chief technology officer of Bangalore-based SigTuple.

'There is a human cost here. In many places, where there is no pathologist, a half-trained technician will write out a report and cases will go undetected until it's too late,' Dastidar said.

Low Cost, High Performance Microscope

SigTuple's device isn't the first automated microscope. Instruments known as digital slide scanners automatically convert glass slides to digital images and interpret the results. But SigTuple's microscope sells for a fraction of the price of digital slide scanners, making it affordable for most labs, including those in the developing world.

The company's AI microscope works by scanning slides under its lens and then using GPU-accelerated deep learning to analyze the digital images either on SigTuple's AI platform in the cloud or on the microscope itself. It uses different deep learning models to analyze blood, urine and semen.

The microscope performs functions like identifying cells, classifying them into categories and subcategories, and calculating the numbers of different cell types.

For a blood smear, for example, Shonit - that's Sanskrit for blood - identifies red and white blood cells and platelets, pinpoints their locations and calculates ratios of different types of white blood cells (commonly known as differential count). It also computes 3D information about cells from their 2D images using machine learning techniques.

In studies SigTuple conducted with some of India's leading labs, Shonit's accuracy matched that of other automated analyzers. It also successfully identified rare varieties of cells that both pathologists and automated tools usually miss.

Expert Review in the Cloud

In addition to providing a low-cost method for interpreting slides, Dastidar sees SigTuple's AI platform as an ideal tool for providing expert review of tests when no expert is available. As well as automating analysis, it stores data in the cloud so any pathologist anywhere can interpret test results.

The company's cloud platform also makes it far easier for pathologists to collaborate on difficult cases.

'Before that would have meant shipping the slide from one lab to another,' Dastidar said.

SigTuple next plans a formal trial of Shonit and is beginning to roll it out commercially.

For more information about SigTuple and Shonit, watch Dastidar's GTC talk or read SigTuple's recent paper, Analyzing Microscopic Images of Peripheral Blood Smear Using Deep Learning.