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Can we use artificial intelligence for microscopic parasite diagnosis?

Evaluation of a digital health ecosystem using artificial intelligence for microscopic analysis of parasitological samples

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ISRCTN
Registry ID
ISRCTN98669958
Enrollment
209
Registered
2020-10-21
Start date
2021-01-01
Completion date
Unknown
Last updated
2022-04-25

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Blood parasites (malaria, filaria and other NTDs such as Chagas disease and Leishmania) Infections and Infestations

Interventions

This is a multi-centre, observational study to evaluate the benefits of digitalization of collected preparations from subjects with a suspected parasitological disease. Generated data will be used to

Sponsors

SpotLab S.L.
Lead Sponsor

Eligibility

Inclusion criteria

Inclusion criteria: 1. All preparations likely to be positive for a parasite from the laboratory sample collection that are properly stained and where the morphology of the parasite is well preserved. 2. All preparations that have been previously anonymized without the possibility of reversing the coding.

Exclusion criteria

Exclusion criteria: 1. All preparations that are not properly stained and the morphology of the parasite is not well preserved 2. All those preparations that have not been previously anonymized

Design outcomes

Primary

MeasureTime frame
1. Standard procedure for remote analysis of digitized parasitological samples, measured by the number of samples analysed by web platform (TeleSpot) and analysis time per sample 2. Repository of digitized parasite images with each parasitic form appearing in the image correctly marked and tagged measured by the number of tagged samples (images) for each parasitic form and % agreement among users (reviewers) throughout the study 3. Accuracy of the AI algorithm developed measured by % of agreement among experts and AI algorithm throughout the study 4. Usability report based on the results from SUS scale and specific product questionnaires evaluating the remote analysis process at the beginning and the end of the study

Secondary

MeasureTime frame
There are no secondary outcome measures

Countries

Bolivia, Brazil, Malaysia, Spain

Outcome results

None listed

Source: ISRCTN (via WHO ICTRP) · Data processed: Feb 4, 2026