ReviewCureus2026
Detecting Pseudothrombocytopenia in the Era of Artificial Intelligence: Integration of Automated Hematology, Digital Morphology, and Expert Review.
Review in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pseudothrombocytopenia is an artefactual reduction in platelet count that occurs during laboratory testing, most commonly because ethylenediaminetetraacetic acid (EDTA) induces in vitro platelet aggregation. Although clinically benign, it may lead to unnecessary investigations, treatment, transfusion, referral, procedural delay, and patient anxiety when not recognised. Advances in hematology analyzers, including impedance, optical, fluorescence, digital morphology, and artificial intelligence (AI)-assisted technologies, offer opportunities to improve recognition of platelet aggregation and reduce reporting of artefactual thrombocytopenia. This narrative review summarises literature relating to pseudothrombocytopenia, automated platelet counting, digital morphology systems, and AI applications in hematology. Current evidence indicates that analyzer flags and platelet histograms provide useful screening signals but should not replace peripheral blood smear review. Optical and fluorescence platelet channels can improve platelet counting in selected EDTA-dependent samples, while digital morphology systems facilitate documentation of platelet aggregates and support platelet estimation. Emerging AI-assisted workflows are best understood as workflow-support tools that integrate analyzer data, sample timing, channel discordance, digital images, and expert review; they should not be treated as autonomous diagnostic systems. The strongest practical model combines automated platelet channels, digital morphology, AI-supported triage, clear report communication, and expert clinical oversight. Evidence remains heterogeneous, largely platform-specific, and limited by the lack of direct AI-versus-conventional workflow comparisons. Future research should validate integrated systems across diverse laboratory environments and assess clinically relevant outcomes, including diagnostic accuracy, false-positive and false-negative consequences, workflow efficiency, cost, and patient management.
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Registered trials
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.