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


ケンブリッジコンサルタンツはアルトラン(Altran)の一員です。 アルトランは、エンジニアリングおよびR&Dサービスにおいて広く認められた世界的リーダーであり、変革とイノベーションの課題に対応する独自の価値提案をお客様に提供しています。コンセプト創造から産業化に至るまで、アルトランはお客様が明日の製品やサービスを開発するサポートをいたします。35年以上にわたり、自動車、航空、宇宙、防衛および海軍、鉄道、インフラと輸送、産業と消費者製品、生命科学、通信、半導体と電子機器、ソフトウェアおよびインターネット、金融および公共部門など、様々なお客様を支援してきました。今日、アルトランは30か国以上に50,000人を超える従業員を擁し、コンサルティング、デジタルトランスフォーメーション、テクノロジー&エンジニアリングサービスのグローバルリーダーであるキャップジェミニ(Capgemini)の中核を担っています。キャップジェミニ・グループはイノベーションの最前線に立ち、進化を続けるクラウド、デジタル及び各種プラットフォーム分野で、顧客のあらゆるビジネス機会に対応致します。キャップジェミニは、50年以上にわたり蓄積してきた優れた実績と業界固有の専門知識を基に、戦略から運用まで、一連のサービスを通じて、企業が目指すビジネスビジョンの実現を支援しています。キャップジェミニの信念は、「テクノロジーに関わるビジネス価値は人を通じて具現化される」ことであり、この信念こそが社の原動力となっています。キャップジェミニは、世界約50か国270,000人に及ぶチームメンバーで構成される多文化企業であり、アルトランを含むグループ全体の2019年度売上は、170億ユーロです。People matter, results count.(人にこだわり、成果にコミット。)アルトランの詳細は をご参照ください。


BacilAi close up.jpg, 10MB
BacillAi in use.jpg, 15.3MB
BacillAi team.jpg, 1.4MB
BacillAi system 2.jpg, 17.2MB


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