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Deep learning tools for the automated analysis of hematopathology whole slide images and the development of prognostic algorithms for hematopoietic stem cell transplant recipients

Award Number: K38HL159128
ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

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PROJECT SUMMARY/ABSTRACT The bone marrow is the primary site of hematopoiesis and its examination is central to the diagnosis and management of patients with hematological diseases. Pathology slides from clinical hematopathology services represent a treasure trove of real-world data on the biology of human bone marrow. However, human examination is time-intensive and limited in its quantitative precision, preventing our ability to perform large- scale studies or identify new morphologic markers of disease. Machine learning and image processing methods can be applied to the analysis of whole-slide images (WSIs), which will lead to improvements in our understanding of the hematological system, as well as our ability to diagnose and manage hematological diseases. The objective of this research is to build deep learning-based tools for the automated classification of bone marrow aspirates and use these tools to study hematopoiesis in hematopoietic stem cell transplant recipients. The central hypothesis is that automated methods can be developed for the classification, characterization, and quantification of cell morphology on human bone marrow aspirates and that these tools can identify morphologic features predictive of outcome after hematopoietic stem cell transplant. The long-term goal is to develop a suite of artificial intelligence tools for the quantitative analysis of hematopathology WSIs that can be used to improve our understanding of hematopoiesis and our management of patients with hematologic diseases through the development of digital hematopathology tools, stains, and biomarkers for research and clinical applications. This approach is innovative because it applies cutting-edge image analysis technology to the quantitative and scalable study of human bone marrow specimens from clinical pathology archives to drive discovery of new biological insights and clinical biomarkers.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2023 ( Subtotal = -$6,848 )
2023	2021	REGENTS OF THE UNIVERSITY OF CALIFORNIA, SAN FRANCISCO, THE	1855 FOLSOM ST STE 425	SAN FRANCISCO	CA	94143	SAN FRANCISCO	USA	Blood Diseases and Resources Research	000	1	8/21/2023	NEW	-$6,848
														Subtotal = -$6,848

Issue Date FY: 2022 ( Subtotal = $0 )
2022	2021	REGENTS OF THE UNIVERSITY OF CALIFORNIA, SAN FRANCISCO, THE	1855 FOLSOM ST STE 425	SAN FRANCISCO	CA	94143	SAN FRANCISCO	USA	Blood Diseases and Resources Research	000	1	7/28/2022	NEW	$0
														Subtotal = $0

Issue Date FY: 2021 ( Subtotal = $119,318 )
2021	2021	REGENTS OF THE UNIVERSITY OF CALIFORNIA, SAN FRANCISCO, THE	1855 FOLSOM ST STE 425	SAN FRANCISCO	CA	94143	SAN FRANCISCO	USA	Blood Diseases and Resources Research	000	1	7/19/2021	NEW	$119,318
														Subtotal = $119,318

Grand Total All Awards = $112,470

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Deep learning tools for the automated analysis of hematopathology whole slide images and the development of prognostic algorithms for hematopoietic stem cell transplant recipients

Award Number: K38HL159128

ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

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