A Deep Learning Model to Quantify Arteriosclerosis in Donor Kidney Biopsies
量化供体肾活检中动脉硬化的深度学习模型
基本信息
- 批准号:10601825
- 负责人:
- 金额:$ 28万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-09-16 至 2024-08-31
- 项目状态:已结题
- 来源:
- 关键词:ArteriesArteriosclerosisArtificial IntelligenceBiopsyBlood VesselsCessation of lifeChronicChronic Kidney FailureClinicalComputer softwareComputersCost of IllnessData SetEnsureEvaluationFeesFibrosisFreezingFrozen SectionsFundingGoalsHealth Care CostsHealthcare SystemsHistologicHistologyHumanImageImage AnalysisKidneyKidney DiseasesKidney TransplantationKnowledgeLaboratoriesLegalLifeMachine LearningMalignant neoplasm of prostateManualsMeasuresMedicareMicroscopeMicroscopicModelingNamesOrganOrgan DonorOutcomeOutputPathologicPathologistPathologyPatient CarePatient-Focused OutcomesPatientsPerformancePersonal SatisfactionPersonsPhaseProcessQuantitative EvaluationsReproducibilityResearch PersonnelSavingsScientistSecureSlideSmall Business Technology Transfer ResearchTechniquesTissuesTransplantationTrustUniversitiesVascular DiseasesWashingtonbasecloud basedcommercial applicationcomputerizeddeep learningdeep learning modeldigitalglomerulosclerosisimage processingimprovedinnovationinterstitialkidney biopsymalignant breast neoplasmmeetingspower analysispublic health relevancerenal damagesoftware developmentstandard of caretechnological innovationtoolusabilitywhole slide imaging
项目摘要
ABSTRACT
More people die every year from kidney disease than breast or prostate cancer. Kidney
transplantation is life-saving, yet the donor organ shortage and high organ discard rate
contributes to 13 deaths daily among patients awaiting transplant. The decision to use or
discard a donor kidney relies heavily on microscopic quantitation of chronic damage by
pathologists. The current standard of care relies on a manual process that is subject to
significant human variability and inefficiency, resulting in potentially healthy kidneys being
discarded and potentially damaged kidneys being transplanted inappropriately. Our team
developed the first Deep Learning model to quantify percent global glomerulosclerosis in donor
kidney frozen section biopsy whole slide images. We developed a cloud-based platform to apply
the Deep Learning model to analyze kidney biopsy whole slide images in under 6 minutes with
accuracy and precision equal to or greater than current standard of care pathologists. We have
also developed a Deep Learning model to quantify interstitial fibrosis on donor kidney biopsy
whole slide images. This innovative approach has the potential to transform donor kidney biopsy
evaluation by improving pathologist efficiency, accuracy, and precision ultimately resulting in
optimized donor organ utilization, improved patient outcomes, and diminished health care costs.
The goal of this project is to develop a Deep Learning technique for quantification of
arteriosclerosis, to support evaluation of donor kidneys prior to transplantation. This will be
achieved by assembling a team of expert pathologists and computer scientists specializing in
machine learning. The proposal will evaluate the accuracy and precision of the arteriosclerosis
Deep Learning model. The functionality of the Trusted Kidney software platform will be
improved beyond the current usable product into a commercially viable solution for multiple
laboratories.
摘要
项目成果
期刊论文数量(0)
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Joseph P Gaut其他文献
Joseph P Gaut的其他文献
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{{ truncateString('Joseph P Gaut', 18)}}的其他基金
A Deep Learning Model to Improve Pathologist Interpretation of Donor Kidney Biopsies
改善病理学家对供体肾活检的解释的深度学习模型
- 批准号:
9678574 - 财政年份:2018
- 资助金额:
$ 28万 - 项目类别:
A Deep Learning Model to Improve Pathologist Interpretation of Donor Kidney Biopsies
改善病理学家对供体肾活检的解释的深度学习模型
- 批准号:
10266188 - 财政年份:2018
- 资助金额:
$ 28万 - 项目类别:
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