Towards artificial intelligence-driven pathology assessment for hematological malignancies.

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

3 citing papers in PubMed.

1. Application of Pathomic Features for Differentiating Dysplastic Cells in Patients with Myelodysplastic Syndrome.Bioengineering (Basel, Switzerland) · 2024
   Article
2. Deep learning application of the discrimination of bone marrow aspiration cells in patients with myelodysplastic syndromes.Scientific reports · 2022
   Article
3. A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects.Journal of medical Internet research · 2022
   Review

Corrections and comments

• Commented on by
  Machine Learning of Bone Marrow Histopathology Identifies Genetic and Clinical Determinants in Patients with MDS.2021
• Commented on by
  PMID 34233291
• Commented on by
  PMID 35015673

Authors and funding

1 author.

Funding

Abstract