Automatic Pathology Software for Diagnosis of Non-Alcoholic Fatty Liver Disease
- 技术优势
- Automatic – Supervised machine learning to automatically learn rules for classification of white regions Multiple Applications – Can be used in quantification of steatosis grade, rapid assessment of candidate donor livers in the transplant setting, and biopsy index database search Faster – Quicker classification of biopsies More accurate – Minimizes variability observed in diagnosed made by human pathologists
- 详细技术说明
- The invention is a supervised machine learning approach for automatically classifying “white-regions” of liver biopsies into 1 of 7 categories, thus providing an important decision support system for pathologists. White-region classification can be applied in multiple analyses performed on liver biopsies, with assessment of steatosis (fat) grade (SG) perhaps being the most direct.
- *Abstract
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With the development of high resolution scanners for liver (and other) biopsies, automated methods that process digitized images can now be used in the clinic. This invention provides a fully automated way to accurately and quickly estimate SG without the inherent variability of human assessment. Currently the method can accurately classify more than 90% of the white regions, which include steatosis (fat) and the important anatomical landmarks such as bile ducts, hepatic arteries and portal veins.
Although SG is a key factor in the diagnosis and staging of common liver diseases, pathologist’s manual assessment of SG is semiquantitative, discontinuous, and variable. Variation in the assessment of steatosis, necroinflammation, and fibrosis can lead to errors in diagnosis and staging of Non-Alcoholic Fatter Liver Disease (NAFLD), the most common liver disease in the United States. The goal of this work is to provide computational methods for quantification of these key histological features with less variable scores to improve patient outcome.
- *IP Issue Date
- None
- *IP Type
- Download
- *Principal Investigation
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Name: Joseph Bockhorst, Assistant Professor
Department:
Name: Samer Gawrieh, Associate Professor
Department:
Name: Scott Vanderbeck
Department:
- *Publications
- Gawrieh, S., Knoedler, D.M., Saeian, K. Wallace, J.R., and Komorowski, R.A. Effects of interventions onintra- and interobserver agreement on interpretation of nonalcoholic fatty liver disease history. Annals ofDiagnostic Pathology. 2011. 15: 19-24.
- 申请号码
- Non-Confidential Summary
- 国家/地区
- 美国
