AI Case Study

London Hospital for Tropical Diseases is testing an AI powered microscope which detects the presence of malaria parasites in blood with the same accuracy as microscopists

Deep learning powered microscopes can be used to identify and count of malarial parasites in blood smear in 20 minutes. This facilitates faster detection of the disease and address shortages of trained staff.

Industry

Healthcare

Healthcare Providers And Services

Project Overview

"The optical microscope remains a widely-used tool for
diagnosis and quantitation of malaria. An automated
system that can match the performance of well-trained
technicians is motivated by a shortage of trained
microscopists. We have developed a computer vision
system that leverages deep learning to identify malaria
parasites in micrographs of standard, field-prepared thick
blood films. The prototype application diagnoses P.
falciparum with sufficient accuracy to achieve competency
level 1 in the World Health Organization external
competency assessment, and quantitates with sufficient
accuracy for use in drug resistance studies. A suite of new
computer vision techniques—global white balance,
adaptive nonlinear grayscale, and a novel augmentation
scheme—underpin the system’s state-of-the-art
performance."

Reported Results

Achieved same accuracy as trained microscopists

Technology

"The solution required a combination of both deep learning and traditional computer algorithms used for segmenting things of interest within images."

Function

Operations

General Operations

Background

It can be very difficult and time consuming to detect cases of malarial parasites. "In cases of very low infection levels, just a single malaria parasite might appear among 100,000 red blood cells." This has been compared to finding a needle in a haystack. Automated microscopes can be used to provide standardised detection of diseases resulting in efficiency and quality gains.

Benefits

Risk reduction - Predictive diagnosis

Data