Machine learning and augmented human intelligence use in histomorphology for haematolymphoid disorders

Ahmad Nanaa, Zeynettin Akkus, Winston Y. Lee, Liron Pantanowitz, Mohamed E. Salama

Research output: Contribution to journalReview articlepeer-review

1 Scopus citations

Abstract

Advances in digital pathology have allowed a number of opportunities such as decision support using artificial intelligence (AI). The application of AI to digital pathology data shows promise as an aid for pathologists in the diagnosis of haematological disorders. AI-based applications have embraced benign haematology, diagnosing leukaemia and lymphoma, as well as ancillary testing modalities including flow cytometry. In this review, we highlight the progress made to date in machine learning applications in haematopathology, summarise important studies in this field, and highlight key limitations. We further present our outlook on the future direction and trends for AI to support diagnostic decisions in haematopathology.

Original languageEnglish (US)
Pages (from-to)400-407
Number of pages8
JournalPathology
Volume53
Issue number3
DOIs
StatePublished - Apr 2021

Keywords

  • artificial intelligence
  • haematopathology
  • leukaemia
  • lymphoma
  • Machine learning

ASJC Scopus subject areas

  • Pathology and Forensic Medicine

Fingerprint Dive into the research topics of 'Machine learning and augmented human intelligence use in histomorphology for haematolymphoid disorders'. Together they form a unique fingerprint.

Cite this