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A Novel Digital Algorithm for Identifying Liver Steatosis Using Smartphone-Captured Images

Access to lifesaving liver transplantation is limited by a severe organ shortage. One factor contributing to the shortage is the high rate of discard in livers with histologic steatosis. Livers with <30% macrosteatosis are generally considered safe for transplant. However, histologic assessment o...

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Autores principales: Xu, Katherine, Raigani, Siavash, Shih, Angela, Baptista, Sofia G., Rosales, Ivy, Parry, Nicola M., Shroff, Stuti G., Misdraji, Joseph, Uygun, Korkut, Yeh, Heidi, Fairchild, Katherine, Anne Dageforde, Leigh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9355111/
https://www.ncbi.nlm.nih.gov/pubmed/35935028
http://dx.doi.org/10.1097/TXD.0000000000001361
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author Xu, Katherine
Raigani, Siavash
Shih, Angela
Baptista, Sofia G.
Rosales, Ivy
Parry, Nicola M.
Shroff, Stuti G.
Misdraji, Joseph
Uygun, Korkut
Yeh, Heidi
Fairchild, Katherine
Anne Dageforde, Leigh
author_facet Xu, Katherine
Raigani, Siavash
Shih, Angela
Baptista, Sofia G.
Rosales, Ivy
Parry, Nicola M.
Shroff, Stuti G.
Misdraji, Joseph
Uygun, Korkut
Yeh, Heidi
Fairchild, Katherine
Anne Dageforde, Leigh
author_sort Xu, Katherine
collection PubMed
description Access to lifesaving liver transplantation is limited by a severe organ shortage. One factor contributing to the shortage is the high rate of discard in livers with histologic steatosis. Livers with <30% macrosteatosis are generally considered safe for transplant. However, histologic assessment of steatosis by a pathologist remains subjective and is often limited by image quality. Here, we address this bottleneck by creating an automated digital algorithm for calculating histologic steatosis using only images of liver biopsy histology obtained with a smartphone. METHODS. Multiple images of frozen section liver histology slides were captured using a smartphone camera via the optical lens of a simple light microscope. Biopsy samples from 80 patients undergoing liver transplantation were included. An automated digital algorithm was designed to capture and count steatotic droplets in liver tissue while discounting areas of vascular lumen, white space, and processing artifacts. Pathologists of varying experience provided steatosis scores, and results were compared with the algorithm’s assessment. Interobserver agreement between pathologists was also assessed. RESULTS. Interobserver agreement between all pathologists was very low but increased with specialist training in liver pathology. A significant linear relationship was found between steatosis estimates of the algorithm compared with expert liver pathologists, though the latter had consistently higher estimates. CONCLUSIONS. This study demonstrates proof of the concept that smartphone-captured images can be used in conjunction with a digital algorithm to measure steatosis. Integration of this technology into the transplant workflow may significantly improve organ utilization rates.
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spelling pubmed-93551112022-08-05 A Novel Digital Algorithm for Identifying Liver Steatosis Using Smartphone-Captured Images Xu, Katherine Raigani, Siavash Shih, Angela Baptista, Sofia G. Rosales, Ivy Parry, Nicola M. Shroff, Stuti G. Misdraji, Joseph Uygun, Korkut Yeh, Heidi Fairchild, Katherine Anne Dageforde, Leigh Transplant Direct Liver Transplantation Access to lifesaving liver transplantation is limited by a severe organ shortage. One factor contributing to the shortage is the high rate of discard in livers with histologic steatosis. Livers with <30% macrosteatosis are generally considered safe for transplant. However, histologic assessment of steatosis by a pathologist remains subjective and is often limited by image quality. Here, we address this bottleneck by creating an automated digital algorithm for calculating histologic steatosis using only images of liver biopsy histology obtained with a smartphone. METHODS. Multiple images of frozen section liver histology slides were captured using a smartphone camera via the optical lens of a simple light microscope. Biopsy samples from 80 patients undergoing liver transplantation were included. An automated digital algorithm was designed to capture and count steatotic droplets in liver tissue while discounting areas of vascular lumen, white space, and processing artifacts. Pathologists of varying experience provided steatosis scores, and results were compared with the algorithm’s assessment. Interobserver agreement between pathologists was also assessed. RESULTS. Interobserver agreement between all pathologists was very low but increased with specialist training in liver pathology. A significant linear relationship was found between steatosis estimates of the algorithm compared with expert liver pathologists, though the latter had consistently higher estimates. CONCLUSIONS. This study demonstrates proof of the concept that smartphone-captured images can be used in conjunction with a digital algorithm to measure steatosis. Integration of this technology into the transplant workflow may significantly improve organ utilization rates. Lippincott Williams & Wilkins 2022-08-04 /pmc/articles/PMC9355111/ /pubmed/35935028 http://dx.doi.org/10.1097/TXD.0000000000001361 Text en Copyright © 2022 The Author(s). Transplantation Direct. Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY) (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Liver Transplantation
Xu, Katherine
Raigani, Siavash
Shih, Angela
Baptista, Sofia G.
Rosales, Ivy
Parry, Nicola M.
Shroff, Stuti G.
Misdraji, Joseph
Uygun, Korkut
Yeh, Heidi
Fairchild, Katherine
Anne Dageforde, Leigh
A Novel Digital Algorithm for Identifying Liver Steatosis Using Smartphone-Captured Images
title A Novel Digital Algorithm for Identifying Liver Steatosis Using Smartphone-Captured Images
title_full A Novel Digital Algorithm for Identifying Liver Steatosis Using Smartphone-Captured Images
title_fullStr A Novel Digital Algorithm for Identifying Liver Steatosis Using Smartphone-Captured Images
title_full_unstemmed A Novel Digital Algorithm for Identifying Liver Steatosis Using Smartphone-Captured Images
title_short A Novel Digital Algorithm for Identifying Liver Steatosis Using Smartphone-Captured Images
title_sort novel digital algorithm for identifying liver steatosis using smartphone-captured images
topic Liver Transplantation
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9355111/
https://www.ncbi.nlm.nih.gov/pubmed/35935028
http://dx.doi.org/10.1097/TXD.0000000000001361
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