Get Involved

Phenotype Image Data and Digital Learning Innovation (PhIDDLI)

Dr Michael Delves (LSHTM), Dr Rajat Dhawan (LSE), Dr Maurizio Gioli (SOAS) and Dr Edgar Whitley (LSE) - £32,000
1 October 2019 to 31 March 2021 (Data Grant BSA28)


Feasibility study of commercial off-the-shelf (COTS) artificial intelligence and machine learning (AI/ML) solutions for automated cell isolation and identification of drug-treated malaria parasites from fluorescent microscope images to prepare them for future AI/ML-based phenotypic analysis.


  1. Using a pre-acquired dataset of raw microscope images of malaria parasites, evaluate and optimise AI/ML algorithms to efficiently and accurately extract cells of interest.
  2. Determine which AI/ML approach is most appropriate for this problem.

Apply solution to a newly generated dataset.