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A Novel Malaria Pathogen Detection System for Rural Communities

About the project

  • Accurate and rapid diagnosis of malaria parasites before treatment is of utmost
    importance to reduce malaria mortality and morbidity.
  • While microscopy remains the gold standard and rapid detection test (RDTs) is the present mainstay of malaria diagnosis in most large health clinics and hospitals, the quality of microscopy is frequently inadequate, and the accuracy of RDTs is reportedly falling due to specific parasite antigenic genes mutations.
  • The detection is cumbersome in specifically remote and rural areas, which can impede the diagnosis and treatment. Delay in receiving treatment for uncomplicated malaria is often reported to increase the risk of developing severe malaria, but access to treatment remains low in most rural areas, where the burden of disease is high.
  • The objective of this project is to develop an innovative cyber-critical technology framework for early malaria pathogen detection. The proposed translational technology solution can be useful for other diseases and regions globally.

Current members

Research and development

  1. Innovative R &D on an Intelligent Algorithm Suite using Thick Blood Smears
  2. Development of a Cloud and Edge Based Cyber Critical Technology
  3. Cost-effective Hardware Development for Edge Deployment
  4. Field testing and validation of prototype

Current and past activities and events

Project Activity 1 KVW: Kick off Virtual Webinar

The Project Launch and kick off Virtual Webinar was held on 30th June 2022, involving key project team members from Australia, India, and USA .

Project Activity 2 VW1:Virtual Webinar January 2023

  • Stay tuned for updates on Project Activity 2
  • Register for attendance/participation in Virtual Webinar scheduled in January 2023

Contact us

Artificial Intelligence for Malaria Pathogen Detection Team(Team AI4MPD)
Prof. Girija Chetty (Girija.chetty@canberra.edu.au )
Dr. Meena Jha (m.jha@cqu.edu.au)

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Why do you need to develop an Intelligent Algorithm Suite based on Thick Blood Smears?

An intelligent algorithm suite is one of the innovative outcome from this project, and it is needed for malaria pathogen detection in thick blood smears, aimed to reduce false negatives. This requires collaboration with different malaria researchers, clinical experts, and health workers on the ground. The existing RAT (Rapid Antigen Tests) cannot detect malaria with 100% accuracy, due to specific gene mutations problem, and requires gold standard lab-based microscopy. However, it cannot be deployed in remote areas in the current form which is cumbersome and prerequisites significant level of experience from the Microscopists. Hence the proposal for an intelligent algorithm suite.

How would the algorithm suite be deployed for detecting malaria in rural communities?

Development of a Cloud and Edge Based Cyber Critical Technology suite for Inference and Training is one cutting edge outcome from this project. This technology suite will be deployed in a cloud and Edge-based analytics engine, bringing the gold standard of diagnostic reference to remote areas. This will help address the problems due to potentially erroneous microscopic diagnosis and gene mutations. Accurate detection of different types of malaria pathogens will eliminate the need for traditional microscopy. The analytics engine and associated technology tools will allow enhanced predictive, prescriptive, and descriptive analytics for rapid disease detection and development of disease treatment plans, assisting health professionals significantly in remote and rural communities.

What is the benefit of Cloud and Edge Based Cyber Critical Technology for rural health settings?

A cost-effective hardware development for edge deployment is one of the key outcomes of this project. The digital diagnostic pathology application will be deployed on Edge devices, allowing lower latency and improved cost-effectiveness, in addition to better privacy and data regulatory compliance. The application deployment on hardware will consist of three components: image acquisition, image analysis using AI/ML, and image storage for audit and continuous learning purposes. These three components of the application hardware will allow acquisition of digital images corresponding to thick blood smear slides and perform intelligent detection, and the centralized storage/archival of referenced images and results will allow future evaluation and audit of diagnostic quality.

How would the validation and testing of this innovative technology prototype would be done?

Extensive field trials will be conducted to evaluate the proposed technology solution. Feedback provided through evaluation and end user survey responses will inform further enhancements to ensure a resilient and adaptive system. A wide cohort of users from rural areas of India will be contacted by our Indian partners, along with an active engagement of technical and domain experts, and senior pathologists, for a comprehensive evaluation of the system. The required protocols in terms of participants’ consent, institutional review board approvals, and implementation of end-user survey questionnaires for technology evaluation will be central to evaluation efforts.