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

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

doi:10.1016/j.pathol.2020.12.004

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 journal › Review article › peer-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 language	English (US)
Pages (from-to)	400-407
Number of pages	8
Journal	Pathology
Volume	53
Issue number	3
DOIs	https://doi.org/10.1016/j.pathol.2020.12.004
State	Published - Apr 2021

Keywords

Machine learning
artificial intelligence
haematopathology
leukaemia
lymphoma

ASJC Scopus subject areas

Pathology and Forensic Medicine

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10.1016/j.pathol.2020.12.004

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title = "Machine learning and augmented human intelligence use in histomorphology for haematolymphoid disorders",

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.",

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AU - Nanaa, Ahmad

AU - Akkus, Zeynettin

AU - Lee, Winston Y.

AU - Pantanowitz, Liron

AU - Salama, Mohamed E.

PY - 2021/4

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N2 - 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.

AB - 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.

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KW - leukaemia

KW - lymphoma

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

Abstract

Keywords

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