Crowdsourced human-based computational approach for tagging peripheral blood smear sample images from Sickle Cell Disease patients using non-expert users.

José María Buades Rubio, Gabriel Moyà-Alcover, Antoni Jaume-I-Capó, Nataša Petrović

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Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

0.2field-weighted citation impact, top 55% of its field

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

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3 · Its place in the literature

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0 citing papers in PubMed, 1 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

José María Buades RubioUGiVIA Research Group, Department of Mathematics and Computer Science, University of the Balearic Islands, 07122, Palma, Spain.

Gabriel Moyà-AlcoverUGiVIA Research Group, Department of Mathematics and Computer Science, University of the Balearic Islands, 07122, Palma, Spain.

Antoni Jaume-I-CapóUGiVIA Research Group, Department of Mathematics and Computer Science, University of the Balearic Islands, 07122, Palma, Spain. antoni.jaume@uib.es.

Nataša PetrovićUGiVIA Research Group, Department of Mathematics and Computer Science, University of the Balearic Islands, 07122, Palma, Spain.

Universitat de les Illes Balears · ES

Funding

MCIN/AEI/10.13039/501100011033 PID2019-104829RA-I00

6 · The paper itself

Abstract

In this paper, we present a human-based computation approach for the analysis of peripheral blood smear (PBS) images images in patients with Sickle Cell Disease (SCD). We used the Mechanical Turk microtask market to crowdsource the labeling of PBS images. We then use the expert-tagged erythrocytesIDB dataset to assess the accuracy and reliability of our proposal. Our results showed that when a robust consensus is achieved among the Mechanical Turk workers, probability of error is very low, based on comparison with expert analysis. This suggests that our proposed approach can be used to annotate datasets of PBS images, which can then be used to train automated methods for the diagnosis of SCD. In future work, we plan to explore the potential integration of our findings with outcomes obtained through automated methodologies. This could lead to the development of more accurate and reliable methods for the diagnosis of SCD.

Indexed as

Anemia, Sickle CellCrowdsourcingSkin NeoplasmsHumansProbabilityReproducibility of Results

Identifiers

PMID38216623

DOI10.1038/s41598-024-51591-w

PMCPMC10786843

OpenAlexW4390789154

What OpenQuestion holds

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.