SBIR/STTR Research Award

BANFF-AID: Banff Automated Nephrology Feature Framework - Artificial Intelligence Diagnosis

Award identifier 4edffda76ca993060e206fc9b88d5f85d17b28e018188d2e1ec11d4e23d0076bAwarding agency Department of Health and Human Services · National Institutes of Health

Award amount

$306,873

Program and phase

SBIR · Phase I

Award year

2024

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Award details

Awarding agency
Department of Health and Human Services
Branch
National Institutes of Health
Recipient company
KITWARE INC
Proposal award date
2024-09-04
Contract end date
2025-08-31
UEI
DK6LPWMS5LP5
Solicitation number
PA23-230
Topic code
400

Research abstract

Project Summary (30 lines) Banff Lesion Scores are utilized globally for assessing the condition of allografts, particularly donor kidneys. They are also used to predict the overall health of the transplanted organ. However, visual human assessment, coupled with imprecise definitions, leads to inter-observer and intra-observer variability. This subjectivity in evaluation often results in conservative decisions, causing potentially viable donor organs to be discarded, representing missed opportunities. Furthermore, detailed visual assessment by pathologists is time consuming, and its monotony can lead to increased errors and worker burnout. Increasing automation of this analysis will alleviate both of these challenges. Our project, BANFF-AID, Banff Automated Nephrology Feature Framework - Artificial Intelligence Diagnosis, aims to transform and significantly improve this process by introducing artificial intelligence (AI)-driven automated score generation and a user-centric web-based workflow for kidney biopsies. Through the integration of custom-built AI models that extract essential features from biopsy slides, our system delivers accurate and continuous (non-discretized) Banff Lesion Scores. Our approach of using AI-produced image segmentation of features of interest ensures that the results are explainable and the method is transparent. This approach also empowers pathologists to intervene and make corrections to the segmentation in case of incorrect scoring due to inaccurate feature segmentation. Our software framework builds upon previous efforts supported by various NIH grants, providing a strong foundation. In Phase I, our proof of concept development will focus on renal tissues featuring arteriosclerosis, glomerulosclerosis, and IFTA (interstitial fibrosis and tubular atrophy). We will be working with H&E (hematoxylin and eosin) stained frozen sections using more precise definitions than those employed in routine histopathology practice. Moving into Phase II, our goal is to expand the scope to compute multiple Banff Lesion Scores from multiple slides and multiple stains. This comprehensive approach will reduce the burden on the pathologists, allowing them to focus on interpreting measurements and providing diagnosis. 1

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Awards with the same topic code

Verified by an exact topic code match. This is not a text-similarity match.

Research funding history

243 SBIR/STTR records share this UEI (1998–2025). Reported award amounts total $140,194,302. Reported amounts are not federal obligations.

Phases: Phase I, Phase II, Phase II – Direct to Phase II

Agencies: Department of Defense, Department of Health and Human Services, Department of Energy, National Science Foundation, National Aeronautics and Space Administration, Department of Commerce

Agency intelligence

Department of Health and Human Services

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