Continuously learning AI pathologist: An AI powered smart microscope - - PowerPoint PPT Presentation

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Continuously learning AI pathologist: An AI powered smart microscope - - PowerPoint PPT Presentation

Continuously learning AI pathologist: An AI powered smart microscope that can automatically scan different biological samples Tathagato Rai Dastidar, Co-founder & Chief Scientific Officer, SigTuple The Wonder Tool! The clinical microscope


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Continuously learning AI pathologist: An AI powered smart microscope that can automatically scan different biological samples

Tathagato Rai Dastidar, Co-founder & Chief Scientific Officer, SigTuple

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The Wonder Tool!

The clinical microscope

Still a gold standard for detecting various types of abnormalities in clinical laboratories

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… and the Wonder Person!

The clinical pathologist

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But there aren’t enough ...

HOW MANY OF YOU ARE THERE? NOT ENOUGH

Some numbers: In India,

  • 1.2 Billion people
  • 20,000 pathologists!
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The continuously learning AI pathologist

Introducing ...

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Transformation over a year ...

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How it works

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Seeing is believing!

Enabling remote review and collaboration

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Multi purpose ...

Blood Urine Semen

… and many more in the pipeline

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Continuous learning

Unidentified cells

New data Re-training Extensive validation Deployment

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Continuous learning

  • Predictions model become better, without any

changes in the hardware

  • Predictors for new disease conditions made

available

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Where we are ...

  • Several clinical studies with leading

laboratories

  • High correlation of statistical indices

with existing state-of-the-art

  • Adept at finding rarer cells which a

pathologist or existing machines typically miss ○ Proven across multiple studies

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Where all does AI play a role?

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Scanning

0.5mm diameter

  • Impossible to capture entire slide (75mm X

25mm) ○ Location and size of “analyzable area” uncertain ○ Use case dependent definition of “good area”

  • Intelligent way to figure out “promising” areas
  • Machine learned models to identify “good

images” and “good movement directions”

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Classification

  • Images are 4k X 3k, with thousands of objects

○ Cannot be classified as a whole

  • General approach: Localization followed by classification
  • Performance a major bottleneck for CNN based localization

RBC WBC Platelet Normal, elliptical, fragmented ... Neutrophil, lymphocyte, monocyte ... Normal, large ...

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Volume estimation

Red blood cells under a microscope What they actually look like

3D characteristic estimation from 2D images

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Preventive maintenance

Machine

  • peration logs

AI/ML based analysis Dear customer, your device is likely to develop motor problems in the next 48 hours ...

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Thank you! Questions?