NCT02932072: Autoscope

Field Evaluation of a Device for Automated Malaria Microscopy (Autoscope Version 2)

Completed Last updated 17 September 2020

What this trial tests

trial in Malaria in 793 participants. Completed in 19 July 2017.

Timeline

10 November 2016

Primary endpoint
19 July 2017

19 July 2017

Quick facts

Lead sponsor	University of Oxford
Status	Completed
Study type	OBSERVATIONAL
Enrollment	793
Start date	10 November 2016
Primary completion	19 July 2017
Estimated completion	19 July 2017

Conditions studied

• Malaria — all drugs for Malaria →

Sponsor

University of Oxford

Who can join

Adults 6 Months to 75, any sex, with Malaria. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

Microscopy remains a key indicator in drug efficacy testing performed in the context of clinical trials for monitoring existing antimalarials or in the context of regulatory clinical trials for registration of new drugs. It is one of the main diagnostic methods for malaria diagnosis in general, as in an ideal setting it can provide low-cost accurate diagnosis, determine the density of parasites in the blood, and accurately differentiate between different malaria parasite species, characteristics vital to the implementation of global plans for drug efficacy monitoring. Malaria rapid tests (RDTs), while useful for case management, do not provide information on the parasite density nor the species differentiation necessary for research and drug efficacy assessment. Microscopy therefore retains key advantages over a number of newer technologies, but its reliability is severely impeded by dependence on high technical competence of the human operators as well as availability of high quality equipment and reagents. Recent studies have demonstrated the frequent poor specificity and sensitivity associated with manual microscopy diagnostics in operational conditions , , . Advances in digital microscopy performance and affordability have now opened the door to potentially significant improvements in the performance of malaria diagnostic microscopy, overcoming serious deficiencies in current drug efficacy assessment, and more broadly in malaria diagnosis and management.

Intellectual Ventures Laboratory (IVL), in collaboration with Global Good Fund (GG), has developed an initial microscope prototype to support its research into dark field imaging of unstained malaria slides. The system consists of low cost electromechanical components for scanning a standard slide, an optical train with a high numerical aperture objective, and an image capture system. Captured images are analyzed with custom image analysis software developed at GG/IVL, using algorithms that are designed for automatic malaria diagnosis, without user input. Additionally, image processing algorithms have been built around detection of Giemsa-stained malaria slides which is the current standard for malaria microscopy. Initial results show excellent potential for sensitivity and specificity which exceeds that of typical manual microscopists in the field. Based on the positive market and needs assessment in January, 2013, given by stakeholders in the malaria diagnostics community, GG/IVL are pursuing improvement and integration of this algorithm into a portable microscope platform with characteristics similar to the prototype microscope already developed at GG/IVL for dark field imaging. The prototype Autoscope was first tested in field settings in Thailand in Nov 2014 - Jan 2015 at clinics operated by the Shoklo Malaria Research Unit (SMRU). The goal of the first field evaluation was to assess the Autoscope in with respect to its diagnostic performance and also its suitability for harsh conditions typically encountered in field clinics. Further, user feedback on the design and functionality was sought. The Autoscope and the accompanying image analysis algorithms have since been further developed and a new version is now available for testing.

Publications & conference data

No peer-reviewed publications indexed yet for this trial. Completed trials usually publish results within 12-18 months.