Cambridge AI geared to fight killer TB with other diseases on radar
A Cambridge innovation hothouse is at the heart of a new initiative to use Artificial Intelligence technology to stem a tide of deaths from TB in resource-starved countries.
Cambridge Consultants has developed BacillAi, a concept system that harnesses AI and standard, low-cost hardware to improve treatment monitoring of TB – the second largest cause of death by infectious disease in the developing world.
The BacillAi system is part of Cambridge Consultants’ continuing mission to move AI beyond the hype.
TB’s high mortality rate is due to a variety of factors, including a lack of available, affordable diagnosis and inconsistent results acquired in patient follow-up.
TB is monitored by taking a sputum sample and manually counting cells under a microscope. In low-resource countries, this is very difficult.
There are few skilled staff working in difficult conditions. Clinicians may need to review ten patients per day, while for each patient they may need to count hundreds of cells through a microscope. This leads to eye strain for clinicians and poor quality, slow results for patients.
BacillAi is an end-to-end concept system that uses a smartphone to capture images from an ordinary laboratory microscope. Stained sputum sample images are analysed using a deep learning algorithm, specifically a convolutional neural network, to identify, count and classify TB cells, in order to determine the disease state of the patient.
The results of the test are returned to the clinician via a dedicated app. An automated system to count cells and classify treatment progression offers a variety of benefits, including increased consistency, higher throughput and the automatic digitisation of results.
The development of BacillAi was a hugely multidisciplinary project, requiring an expert team to overcome a series of technical challenges.
Dr Kathleen England, senior TB diagnostic adviser and formerly of Médecins Sans Frontières, said: “The only monitoring tools we have today for assessing whether a TB patient’s treatment is working are AFB smear and culture, when available. The reading of slides requires training and highly-skilled microscopists, which are currently a limitation in many countries.
“A system that removes human variability in counting, reduces skill level, increases throughput and shares information via a network would be very beneficial for patient smear examination.”
Richard Hammond, technology director at Cambridge Consultants, added: “Our team is focused on applying our deep learning expertise to real-world, high-impact challenges in healthcare.
“Today, we’ve demonstrated the feasibility of a deep learning-based system using practical and readily-available hardware components in the treatment of TB. Eventually we could see a platform like BacillAi assisting doctors to diagnose and monitor treatment for a host of conditions beyond TB in low-resource settings.”
BacillAi was developed using Cambridge Consultants’ purpose-built deep learning research facility. This laboratory implements NVIDIA DGX POD™ architecture using NetApp ONTAP AI, which provides petabyte-scale, high-performance, all-flash storage.