Breakthrough innovation specialist Cambridge Consultants has developed BacillAi, a concept system that harnesses Artificial Intelligence (AI) and standard, low-cost hardware to improve treatment monitoring of tuberculosis (TB) in resource-limited countries. With 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, addressing practical, high-impact challenges and improving lives on a global scale.

Using machine intelligence to achieve the seemingly impossible

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 analyzed 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 digitization of results.

The development of BacillAi was a hugely multidisciplinary project, requiring an expert team to overcome a series of technical challenges. These include:

  • Developing a high-performance AI system that functions with sub-optimal images (such as low contrast) and that can be implemented on devices with limited computing power
  • Designing an imaging system and phone mount that easily aligns the smartphone camera to the microscope optics and relays good images
  • Developing an artificial sputum and culturing bacterial cells to create the large number of slides required to support AI training
  • Bringing the system together for the user with an intuitive, user-friendly app interface and easy-to-assemble hardware 

Cambridge Consultants works at the leading-edge of advances in AI, applying deep learning in applications including anomaly detection in telecommunications networks, self-driving cars, frictionless user interfaces and medical diagnosis and treatment. TB cell identification is particularly difficult due to the presence of other similar looking cells in sputum samples, variance in stain strength and variance in color between imaging devices. These conditions cause traditional, rule-defined computer vision techniques to fail, as they are unable to account for such breadth of data. By contrast, this rich, varied dataset creates the ideal conditions to train a robust and accurate deep learning system for cell identification.

Dr. Kathleen England, senior TB diagnostic advisor and formerly of Médecins Sans Frontières, commented: “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, commented: “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.

Notes to editors

Cambridge Consultants (剑桥顾问公司)开发突破性产品,产生和授权知识产权,并为全球的客户提供商业资讯,解决与技术相关的关键问题。60多年来,公司一直致力于帮助客户转化商业机会,包括推出市场领先的产品,进入新市场,引入新技术扩大现有市场。公司拥有超过900名员工,包括工程师、科学家、数学家和设计师,遍布于英国剑桥、美国波士顿、东京和新加坡办事处,为医疗和生命科学、工业和能源、消费和零售以及通讯和基础设施等多个行业提供解决方案。欲了解更多信息,请访问:

Cambridge Consultants是 Capgemini Invent (凯捷集团的创新、咨询和转型品牌)的子公司。Capgemini Invent帮助管理者设想和构建他们组织的未来。公司在全球设有30多个办事处和25个创意工作室,是一支拥有7000多名员工的强大团队,将战略、技术、数据科学和创意设计与深厚的行业专业知识和洞察力相结合,开发新的数字解决方案和未来的商业模式。

凯捷集团是通过技术来为客户改造和管理业务的全球领导者,而Capgemini Invent是凯捷集团不可或缺的一部分。集团的宗旨是通过技术释放人类的潜能,实现包容性和可持续的未来。它是一个及负社会责任的多元化组织,在近50个国家拥有27万名团队成员。凭借其50年的悠久历史和深厚的行业专业知识,凯捷集团受到客户的信赖,在快速发展和充满创新的云、数据、人工智能、连接、软件、数字工程和平台技术的推动下,能够满足客户从战略、设计到运营的所有业务需求。集团2020年的全球营收为160亿欧元。

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BacilAi close up.jpg, 10MB
BacillAi in use.jpg, 15.3MB
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BacillAi system 2.jpg, 17.2MB


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