The Intelligent Autonomous Microscope: An AI platform for multiplex parasite diagnosis at the point of care

Objective

A new generation of diagnostic systems available at the point of care (POC) could save lives and reduce the spread of infectious diseases worldwide through early detection and treatment. Optical microscopy remains the gold standard for the diagnosis of many parasitological diseases; however, its accuracy is dependent on the availability and expertise of the analyst at the POC. This limitation is increased by the dependence on labour-intensive examination processes, lack of standardization, high interobserver variability, insufficient precision in sample quantification and, as a consequence, a high misdiagnosis rate. This project introduces an AI diagnostic system leveraging existing microscopes and mobile technology providing a comprehensive and holistic sample analysis rather than just detecting individual pathogens. MultiplexAI is a scalable, low-cost, autonomous AI diagnostic system for the POC that upgrades any optical microscope into an AI agent able to accurately identify any parasite in a sample. We will collect data, train, deploy and evaluate the integrated system to detect multiple diseases including malaria and parasitic Neglected Tropical Diseases. The project will pursue the following objectives and methodological steps: 1) To design a trustworthy AI system, ensuring technical and social robustness, and adherence to WHO AI ethical principles of safety, transparency, explainability, accountability, equity, and sustainability; 2) To develop AI foundational models for microscopy analysis capable of automating the detection, differentiation and quantification of multiple parasites causing disease and integrate them into an automatic mobile microscopy system; 3) To validate the system in laboratory settings; 4) To undertake a performance evaluation study in clinical workflows of four countries in SSA; 5) To assess usability, acceptability and feasibility with end-users and evaluate the cost-effectiveness of its implementation; 6) To model and evaluate the health impact of introducing our system to improve diagnosis and surveillance at both local and national level; and 7) To execute a regulatory roadmap for compliance in EU and SSA, and determine a path to market. Overall, this project aims to unleash the AI revolution leveraging mobile technologies and upgrading millions of optical microscopes into a network of intelligent POC devices, capable of performing high-throughput sample analysis to provide reliable and ubiquitous diagnostics and medical knowledge for everyone, everywhere.

Fields of science (EuroSciVoc)

CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.

• medical and health sciences health sciences infectious diseases malaria
• social sciences political sciences political transitions revolutions
• natural sciences physical sciences optics microscopy

Keywords

Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)

• Parasitology
• Automated Microscopy
• Artificial Intelligence
• Deep learning
• Computer Vision
• Diagnostic Technology
• Epidemiological Surveillance
• Point-of-Care
• Image Processing Algorithms
• NTDs

Coordinator

Participants (8)