Medical imaging and assisted diagnosis
99% accuracy in detection, 86.6% accuracy in diagnosis
i-Nside is disrupting the healthcare industry, building a suite of cutting-edge new tools to help doctors provide patients with the best medical care. Through its patent portfolio, applied artificial intelligence systems, and sustainable mobile technology, i-Nside is bringing reliable and cost-efficient medical devices to physicians and patients all over the world, whether they’re in the biggest city or the most remote desert.
Dr. Laurent Schmoll is the founder and CEO of i-Nside. He is an ear, nose, and throat (ENT) specialist with over 25 years of experience in the medical field. His company won the Health Pitch Challenge at Hacking Health Strasbourg in March 2015.
i-Nside is a worldwide leader in endoscopic technology. With a small device you can attach to any smartphone, i-Nside can take professional-grade medical images of the human ear and use them to diagnose problems. With so many medical images on file, i-Nside wanted to build a diagnostic platform that would be able to assist doctors in identifying ear problems.
1. AVOID TECHNICAL DEBT: What’s the best way to build custom visual recognition into your product?
Getting started with visual recognition and machine learning can be both challenging and expensive. As a small team, i-Nside needed a cost-effective and easy way to build very advanced artificial intelligence technology into its product without incurring technical debt.
2. TRUSTWORTHY RESULTS: How do you ensure that your visual recognition results are accurate?
The stakes are pretty high when it comes to visual recognition and something as life-changing as a medical diagnosis. i-Nside needed a solution that would not only provide accurate results for a very esoteric data set (pictures of the insides of ears) but would also be able to improve with more training.
i-Nside uses Clarifai’s visual recognition solution to build an accurate medical diagnosis platform that helps doctors all over the world provide the best medical care to their patients.
Diagnosing ear problems is a very specialized field of expertise within medicine. General practitioners usually refer people with ear problems to Ear, Nose, and Throat (ENT) specialists. i-Nside wanted to build a diagnostic tool that would assist general practitioners and nurses to identify and treat ear problems accurately, thereby making the best medical care accessible to anyone in the world.
With over 100,000 ear images collected from their widely distributed endoscopic tool, i-Nside asked Clarifai to build a custom visual recognition model especially for ear pictures and video. Now, Clarifai’s visual recognition technology powers the software layer in i-Nside’s line of endoscopic hardware, enabling the tool to not only take pictures of the ear but also to analyze the results - all in one small, affordable, mobile package that anyone can use!
Minimum costs, maximum results
i-Nside had to prove that an assisted diagnosis tool could work before they could get the funding and approvals to put it into production. However, like many startups, they faced a “chicken or egg” problem - they didn’t have the funding to build an expensive A.I. visual recognition product, but they wouldn’t receive more support unless they proved visual recognition worked.
i-Nside partnered with Clarifai to create a cost-effective custom visual recognition model that they built into a beta product to demonstrate the power and accuracy of visual recognition without breaking the bank.
"Even though we work with the best hospitals in the world, we are still a small startup. Clarifai helped us prove our idea in an affordable and speedy way."
A custom model for unique needs
While Clarifai’s core model can recognize over 11,000 general concepts, ear diseases unsurprisingly are not among those core tags. i-Nside needed a special custom model built for the sole purpose of analyzing ear patterns.
Clarifai’s team of data scientists used their expertise to train a custom model on i-Nside’s batch of ear images. It only took a couple of weeks for the custom model to be fully trained to recognize ear problems with near perfect accuracy. The i-Nside team was then able to access the custom model through Clarifai’s API with just a few lines of code.
“We looked into IBM Watson, but they were not a cost-effective option. Clarifai gave us stellar customer support and a simple, well-documented API that allowed us to plug-and-play without incurring financial or technical debt.”
Changing the world, one image at a time
Now that Clarifai’s custom model is powering their endoscopic diagnosis tool, i-Nside can deliver accurate diagnoses to doctors in every corner of the world. Traditionally underserved markets like some parts of Africa, Asia, and South America now have access to the best specialist knowledge in medical care. As i-Nside continues to collect more endoscopic imagery, Clarifai’s model gets smarter and delivers even more accurate results by learning from the feedback.
And endoscopic diagnoses are just the start. i-Nside is hoping to expand both their imaging hardware and their artificially intelligent diagnosis tool to other fields of medicine like oncology (cancer) and radiology (medical imaging like x-rays).
“Our diagnosis tool is meant to augment doctors, not replace them. We decided to work with Clarifai because our philosophies are very aligned - we believe that artificial intelligence can amplify human intelligence, but it’s not a substitute.”
DIY with Clarifai
Now that you’ve been inspired by i-Nside's mission to change the world, it’s time to build your own app. Clarifai’s core model includes tags for over 11,000 concepts you can apply to your business. All it takes is three simple lines of code - sign up for a developer API account to get started for free!
Once you’ve signed up for a developer account, head over to Applications and make a new one. Make sure you nab that Client ID and Client Secret:
Now, head over to https://github.com/clarifai/clarifai-nodejs. There, you’ll find our Node.js client, which makes this process even easier. To set up your environment, download the clarifai node.js file and stick it in your project.
Boo yah. You’re set up. Now head over to your Node project and just require the Clarifai client:
Remember that Client ID and Client Secret you nabbed earlier? We’re gonna use those now. You can either paste them in this function directly, or save them in an environment variable.
Now for the fun part. You can easily tag an image with just 3 lines of code:
You’re all set! Now you can easily make like Yelp and tag and sort your images to your heart’s desire. If you’d like to see a more in-depth example, check out clarifai_sample.js in the GitHub repo.