340 Machine Learning Segmentation of Amyloid Load in Ligamentum Flavum Specimens From Spinal Stenosis Patients
OBJECTIVES/GOALS: Wild-type transthyretin amyloid (ATTRwt) deposits have been found to deposit in the ligamentum flavum (LF) of spinal stenosis patients prior to systemic and cardiac amyloidosis, and is implicated in LF hypertrophy. Currently, no precise method of quantifying amyloid deposits exists...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Cambridge University Press
2022-04-01
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| Series: | Journal of Clinical and Translational Science |
| Online Access: | https://www.cambridge.org/core/product/identifier/S2059866122001935/type/journal_article |
| _version_ | 1811155290320535552 |
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| author | Andy Y. Wang Vaishnavi Sharma Harleen Saini Joseph N. Tingen Alexandra Flores Diang Liu Mina G. Safain James Kryzanski Ellen D. McPhail Knarik Arkun Ron I. Riesenburger |
| author_facet | Andy Y. Wang Vaishnavi Sharma Harleen Saini Joseph N. Tingen Alexandra Flores Diang Liu Mina G. Safain James Kryzanski Ellen D. McPhail Knarik Arkun Ron I. Riesenburger |
| author_sort | Andy Y. Wang |
| collection | DOAJ |
| description | OBJECTIVES/GOALS: Wild-type transthyretin amyloid (ATTRwt) deposits have been found to deposit in the ligamentum flavum (LF) of spinal stenosis patients prior to systemic and cardiac amyloidosis, and is implicated in LF hypertrophy. Currently, no precise method of quantifying amyloid deposits exists. Here, we present our machine learning quantification method. METHODS/STUDY POPULATION: Images of ligamentum flavum specimens stained with Congo red are obtained from spinal stenosis patients undergoing laminectomies and confirmed to be positive for ATTRwt. Amyloid deposits in these specimens are classified and quantified by TWS through training the algorithm via user-directed annotations on images of LF. TWS can also be automated through exposure to a set of training images with user- directed annotations, and then application to a set of new images without additional annotations. Additional methods of color thresholding and manual segmentation are also used on these images for comparison to TWS. RESULTS/ANTICIPATED RESULTS: We develop the use of TWS in images of LF and demonstrate its potential for automated quantification. TWS is strongly correlated with manual segmentation in the training set of images with user-directed annotations (R = 0.98; p = 0.0033) as well as in the application set of images where TWS was automated (R = 0.94; p = 0.016). Color thresholding was weakly correlated with manual segmentation in the training set of images (R = 0.78; p = 0.12) and in the application set of images (R = 0.65; p = 0.23). DISCUSSION/SIGNIFICANCE: Our machine learning method correlates with the gold standard comparator of manual segmentation and outperforms color thresholding. This novel machine learning quantification method is a precise, objective, accessible, high throughput, and powerful tool that will hopefully pave the way towards future research and clinical applications. |
| first_indexed | 2024-04-10T04:30:40Z |
| format | Article |
| id | doaj.art-84bd4baad8b14567a9caf50362710663 |
| institution | Directory Open Access Journal |
| issn | 2059-8661 |
| language | English |
| last_indexed | 2024-04-10T04:30:40Z |
| publishDate | 2022-04-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Journal of Clinical and Translational Science |
| spelling | doaj.art-84bd4baad8b14567a9caf503627106632023-03-10T07:53:48ZengCambridge University PressJournal of Clinical and Translational Science2059-86612022-04-016636310.1017/cts.2022.193340 Machine Learning Segmentation of Amyloid Load in Ligamentum Flavum Specimens From Spinal Stenosis PatientsAndy Y. Wang0Vaishnavi Sharma1Harleen Saini2Joseph N. Tingen3Alexandra Flores4Diang Liu5Mina G. Safain6James Kryzanski7Ellen D. McPhail8Knarik Arkun9Ron I. Riesenburger10Tufts Medical CenterTufts Medical CenterTufts Medical CenterTufts Medical CenterTufts Medical CenterTufts Medical CenterTufts Medical CenterTufts Medical CenterMayo ClinicTufts Medical CenterTufts Medical CenterOBJECTIVES/GOALS: Wild-type transthyretin amyloid (ATTRwt) deposits have been found to deposit in the ligamentum flavum (LF) of spinal stenosis patients prior to systemic and cardiac amyloidosis, and is implicated in LF hypertrophy. Currently, no precise method of quantifying amyloid deposits exists. Here, we present our machine learning quantification method. METHODS/STUDY POPULATION: Images of ligamentum flavum specimens stained with Congo red are obtained from spinal stenosis patients undergoing laminectomies and confirmed to be positive for ATTRwt. Amyloid deposits in these specimens are classified and quantified by TWS through training the algorithm via user-directed annotations on images of LF. TWS can also be automated through exposure to a set of training images with user- directed annotations, and then application to a set of new images without additional annotations. Additional methods of color thresholding and manual segmentation are also used on these images for comparison to TWS. RESULTS/ANTICIPATED RESULTS: We develop the use of TWS in images of LF and demonstrate its potential for automated quantification. TWS is strongly correlated with manual segmentation in the training set of images with user-directed annotations (R = 0.98; p = 0.0033) as well as in the application set of images where TWS was automated (R = 0.94; p = 0.016). Color thresholding was weakly correlated with manual segmentation in the training set of images (R = 0.78; p = 0.12) and in the application set of images (R = 0.65; p = 0.23). DISCUSSION/SIGNIFICANCE: Our machine learning method correlates with the gold standard comparator of manual segmentation and outperforms color thresholding. This novel machine learning quantification method is a precise, objective, accessible, high throughput, and powerful tool that will hopefully pave the way towards future research and clinical applications.https://www.cambridge.org/core/product/identifier/S2059866122001935/type/journal_article |
| spellingShingle | Andy Y. Wang Vaishnavi Sharma Harleen Saini Joseph N. Tingen Alexandra Flores Diang Liu Mina G. Safain James Kryzanski Ellen D. McPhail Knarik Arkun Ron I. Riesenburger 340 Machine Learning Segmentation of Amyloid Load in Ligamentum Flavum Specimens From Spinal Stenosis Patients Journal of Clinical and Translational Science |
| title | 340 Machine Learning Segmentation of Amyloid Load in Ligamentum Flavum Specimens From Spinal Stenosis Patients |
| title_full | 340 Machine Learning Segmentation of Amyloid Load in Ligamentum Flavum Specimens From Spinal Stenosis Patients |
| title_fullStr | 340 Machine Learning Segmentation of Amyloid Load in Ligamentum Flavum Specimens From Spinal Stenosis Patients |
| title_full_unstemmed | 340 Machine Learning Segmentation of Amyloid Load in Ligamentum Flavum Specimens From Spinal Stenosis Patients |
| title_short | 340 Machine Learning Segmentation of Amyloid Load in Ligamentum Flavum Specimens From Spinal Stenosis Patients |
| title_sort | 340 machine learning segmentation of amyloid load in ligamentum flavum specimens from spinal stenosis patients |
| url | https://www.cambridge.org/core/product/identifier/S2059866122001935/type/journal_article |
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