What does this research mean for the field?

AI-powered bone marrow analysis reveals that patients with myeloproliferative neoplasms and splanchnic vein thrombosis exhibit distinct histological features, notably an absence of essential thrombocythemia and an enrichment of prefibrotic myelofibrosis, highlighting its value in complementing conventional diagnosis. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This study aims to evaluate the accuracy of diagnosing myeloproliferative neoplasms (MPN) with splanchnic vein thrombosis (SVT) using conventional histology and artificial intelligence.

May 16, 2026American Journal of Hematology

Accuracy of Diagnosis in Myeloproliferative Neoplasms With Splanchnic Vein Thrombosis ( MPN ‐ SVT )

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Authors

BSBharath Ram Sreedhar HRHosuk Ryou AOAoife O'Riordan

Discussion

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Overview

Randomized trial evaluates diagnostic accuracy of clinical and AI methods in MPN-SVT, suggesting improvements in assessment accuracy.

Key Points

This study aims to evaluate the accuracy of diagnosing myeloproliferative neoplasms (MPN) with splanchnic vein thrombosis (SVT) using conventional histology and artificial intelligence.
Included 34 patients diagnosed with MPN-SVT, with retrospective data collection on clinical features, blood parameters, and bone marrow assessments.
Combined expert histopathologist evaluation with artificial intelligence analysis of bone marrow histology to assess diagnostic concordance.
Data were analyzed for concordance rates between clinical, histological, and AI diagnostics, using categorizations based on WHO criteria.
Concordance between clinical diagnosis and expert histology was 50%, while it was only 33.3% across all diagnostic methods for MF and PV.
AI analysis returned a 41.6% concordance for histology, particularly approaching 50% for clinical PV cases.
Significant discrepancies in ET categorization were observed, with AI analysis suggesting more prefibrotic-MF and PV classifications than clinical diagnoses.

Cite This Study

Sreedhar et al. (2026) studied this question.

synapsesocial.com/papers/6a080a41a487c87a6a40c367 https://doi.org/10.1002/ajh.70362

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May 16, 2026American Journal of Hematology

Accuracy of Diagnosis in Myeloproliferative Neoplasms With Splanchnic Vein Thrombosis ( MPN ‐ SVT )

View Full Paper

Ask AI

Bookmark

Authors

BSBharath Ram Sreedhar HRHosuk Ryou AOAoife O'Riordan

Discussion

Member takes

Overview

Randomized trial evaluates diagnostic accuracy of clinical and AI methods in MPN-SVT, suggesting improvements in assessment accuracy.

Key Points

This study aims to evaluate the accuracy of diagnosing myeloproliferative neoplasms (MPN) with splanchnic vein thrombosis (SVT) using conventional histology and artificial intelligence.
Included 34 patients diagnosed with MPN-SVT, with retrospective data collection on clinical features, blood parameters, and bone marrow assessments.
Combined expert histopathologist evaluation with artificial intelligence analysis of bone marrow histology to assess diagnostic concordance.
Data were analyzed for concordance rates between clinical, histological, and AI diagnostics, using categorizations based on WHO criteria.
Concordance between clinical diagnosis and expert histology was 50%, while it was only 33.3% across all diagnostic methods for MF and PV.
AI analysis returned a 41.6% concordance for histology, particularly approaching 50% for clinical PV cases.
Significant discrepancies in ET categorization were observed, with AI analysis suggesting more prefibrotic-MF and PV classifications than clinical diagnoses.

Cite This Study

Sreedhar et al. (2026) studied this question.

synapsesocial.com/papers/6a080a41a487c87a6a40c367 https://doi.org/10.1002/ajh.70362

Also Consider

Synapse has enriched 4 closely related papers on similar clinical questions. Consider them for comparative context:

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