Despite being treatable, tuberculosis kills 1.6 million people every year.
This is because TB treatment is time- and cost-intensive, requiring extensive patient monitoring.
In developing countries, where the disease is most deadly, monitoring involves a form of testing that has been used for hundreds of years. Clinicians study samples of lung fluid (called sputum) under a microscope and manually count the number of TB bacteria present, which sometimes reach into the hundreds.
This method may be cheaper than other available tests, but it’s only accurate 50 percent of the time.
Cambridge Consultants, a U.K.-based consultancy, has set out to investigate whether an AI-powered monitoring system could provide a feasible alternative for keeping tabs on this killer.
The result is BacillAi, a system that uses an AI-powered smartphone app and a standard-grade microscope to capture and analyze samples of sputum.
“With BacillAi, we wanted to tackle two main questions,” explained Richard Hammond, technology director of the Medical Technologies Division at Cambridge Consultants. “Can AI improve a labor-intensive, difficult process in healthcare diagnostics? And how could you go about making it available to those who need it most, even in the most remote and low-resource areas?”
Putting Manual Processes Under the Microscope
The current process for monitoring TB patients is inefficient and ineffective. Medical professionals review any number of patient samples a day, identifying and counting every single cell. This can take up to 40 minutes per case.
And the difficulty doesn’t stop there. Stains used to distinguish cells in the lung fluid can vary in strength between samples, and adjusting a microscope’s optical focus can alter colors.
Clinicians monitoring TB under these conditions face both mental and physical strain. With such a high risk of human error, patients often receive poor-quality results that arrive too late for them to start vital treatment.
To tackle this conundrum, Cambridge Consultants trained a deep learning system using data gathered from cultured surrogate bacteria and artificial sputum.
Developed on the NVIDIA DGX POD reference architecture with NetApp storage, known as ONTAP AI, the resulting convolutional neural network (CNN) can identify, count and classify TB cells in a matter of minutes.
The final BacillAi concept consists of a standard low-cost microscope, modified with a mount for a smartphone, and an app with the CNN at its heart.
A product like BacillAi could help clinicians determine the state of a patient’s health faster and more consistently than is currently possible. Patients would also have improved chances of fighting the disease.
Solving Challenges at Scale
A multidisciplinary team worked on developing BacillAi in Cambridge Consultants’ purpose-built deep learning research facility, which is powered by ONTAP AI. The space is designed specifically for discovering, developing and testing machine learning approaches in a secure environment.
The same research facility also developed Aficionado, an AI music classifier, Vincent, which turns your squiggles into art, and SharpWave, a tool that creates clear, undistorted views of the real world from a damaged or obscured moving image.
Discover Cambridge Consultants’ innovative approaches for yourself at The AI Summit, in San Francisco, Sept. 25-26.