Project Title: Using infrared spectroscopy and machine-learning to improve malaria diagnosis in low-income communities
Project Description: Short description of the Project/Consultancy/Platform: This project will validate the potential of mid-infrared spectroscopy & deep learning for malaria screening in areas with varying prevalence rates, and demonstrate the integration of this approach to improve case detection and care. This grant will support all the activities and will cover some personnel costs. a) Evaluate sensitivity and specificity of the MIR-ML approach in areas with different levels of transmission. Recent surveys in rural Ulanga and Kilombero... Short description of the Project/Consultancy/Platform: This project will validate the potential of mid-infrared spectroscopy & deep learning for malaria screening in areas with varying prevalence rates, and demonstrate the integration of this approach to improve case detection and care. This grant will support all the activities and will cover some personnel costs. a) Evaluate sensitivity and specificity of the MIR-ML approach in areas with different levels of transmission. Recent surveys in rural Ulanga and Kilombero districts in south-eastern Tanzania have demonstrated strong heterogeneity in malaria prevalence. Overall, malaria prevalence has significantly reduced over the years, but parasite prevalence rates vary from as low as <1% in Ifakara town to as high as 40%, less than 30km south. Since Tanzania has ~1 health facility/village, the spatial variations can allow us to assess the validity of the new approach in areas of varying parasite densities and transmission intensities. b) Assess the diagnostic thresholds of the MIR-ML approach, and its suitability in areas of varying endemicity levels: First, we will aliquot malaria-positive specimen to achieve parasite density ranges of <10/μL up to >10,000/μL, then examine accuracies of MIR-ML at those ranges. Second, we will evaluate statistical correlations between the sensitivity of the ML-MIR approach and natural infection intensities observed in the field, so as to detect thresholds below or beyond which such correlations decay. By extension, this study could also enable in-depth evaluation of the biological basis of observed test results. We can already extract the most dominant peaks on the mid-infrared spectra, which will form the basis for studying the biological basis of “infected” and “non-infected” signals c) Develop an online cloud-based system that integrates data collected from different malaria diagnostic stations in candidate villages, and makes this data available for iterative training of the deep learning algorithms. The diagnostic accuracies by any of the contributing sites will be constantly increased. Currently, clinicians working in nearby health facilities do not have a way to communicate diagnostic outcomes among themselves, yet this is an important aspect of the surveillance-response initiative. This approach will therefore help other facilities regardless of diagnostic methods used, to be aware of new infections in their areas of jurisdiction or neighboring areas. This will improve human-machine interactions and overall diagnostic experiences. Please attach the following documents for all contracts/agreements 1. Notice of Award – letter, award notice, or agreement from the sponsor 2. Scope of work – description of research activities 3. Budget – detailed budget and budget narrative, clearly broken down by requested cost Centre 4. Cash flow projection 5. Prime Agreement ( if subcontract) 6. Approved proposal for a prime agreement
Principal Investigator : Fredros Okumu
Department Name :
Time frame: (2020-08-01) - (2022-07-30)
Swiss Tropical and Public Health Institute (Normal)
External Collaborating Partners
None added yet.