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Deep learning for rapid and reproducible histology scoring of lung injury in a porcine model

Augusto Silva, Iran LU orcid ; Kazemi Rashed, Salma LU ; Hedlund, Ludwig ; Lidfeldt, August ; Gvazava, Nika LU ; Stegmayr, John LU ; Skoryk, Valeriia LU ; Aits, Sonja LU orcid and Wagner, Darcy LU orcid (2023)
Abstract
Acute respiratory distress syndrome (ARDS) is a life-threatening condition with mortality rates between 30-50%. Although in vitro models replicate some aspects of ARDS, small and large animal models remain the primary research tools due to the multifactorial nature of the disease. When using these animal models, histology serves as the gold standard method to confirm lung injury and exclude other diagnoses as high-resolution chest images are often not feasible. Semi-quantitative scoring performed by independent observers is the most common form of histologic analysis in pre-clinical animal models of ARDS. Despite progress in standardizing analysis procedures, objectively comparing histological injuries remains challenging, even for... (More)
Acute respiratory distress syndrome (ARDS) is a life-threatening condition with mortality rates between 30-50%. Although in vitro models replicate some aspects of ARDS, small and large animal models remain the primary research tools due to the multifactorial nature of the disease. When using these animal models, histology serves as the gold standard method to confirm lung injury and exclude other diagnoses as high-resolution chest images are often not feasible. Semi-quantitative scoring performed by independent observers is the most common form of histologic analysis in pre-clinical animal models of ARDS. Despite progress in standardizing analysis procedures, objectively comparing histological injuries remains challenging, even for highly-trained pathologists. Standardized scoring simplifies the task and allows better comparisons between research groups and across different injury models, but it is time-consuming, and interobserver variability remains a significant concern. Convolutional neural networks (CNNs), which have emerged as a key tool in image analysis, could automate this process, potentially enabling faster and more reproducible analysis. Here we explored the reproducibility of human standardized scoring for an animal model of ARDS and its suitability for training CNNs for automated scoring at the whole slide level. We found large variations between human scorers, even for pre-clinical experts and board-certified pathologies in evaluating ARDS animal models. We demonstrate that CNNs (VGG16, EfficientNetB4) are suitable for automated scoring and achieve up to 83% F1-score and 78% accuracy. Thus, CNNs for histopathological classification of acute lung injury could help reduce human variability and eliminate a time-consuming manual research task with acceptable performance. (Less)
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author
; ; ; ; ; ; ; and
organization
publishing date
type
Working paper/Preprint
publication status
published
subject
keywords
acute respiratory distress syndrome, lung, histology, deep learning, convolutional neural network, computer vision
publisher
bioRxiv
DOI
10.1101/2023.05.12.540340
project
Lund University AI Research
Deep Learning for Lung Image Analysis
Using artificial intelligence and advanced computational tools to identify biological pathways, disease mechanisms and therapeutic opportunities
Artificial intelligence for lung histology analysis
language
English
LU publication?
yes
id
47881d2f-1ac2-452c-b445-f7677c023ae0
date added to LUP
2023-05-15 14:06:36
date last changed
2024-02-14 14:16:50
@misc{47881d2f-1ac2-452c-b445-f7677c023ae0,
  abstract     = {{Acute respiratory distress syndrome (ARDS) is a life-threatening condition with mortality rates between 30-50%. Although in vitro models replicate some aspects of ARDS, small and large animal models remain the primary research tools due to the multifactorial nature of the disease. When using these animal models, histology serves as the gold standard method to confirm lung injury and exclude other diagnoses as high-resolution chest images are often not feasible. Semi-quantitative scoring performed by independent observers is the most common form of histologic analysis in pre-clinical animal models of ARDS. Despite progress in standardizing analysis procedures, objectively comparing histological injuries remains challenging, even for highly-trained pathologists. Standardized scoring simplifies the task and allows better comparisons between research groups and across different injury models, but it is time-consuming, and interobserver variability remains a significant concern. Convolutional neural networks (CNNs), which have emerged as a key tool in image analysis, could automate this process, potentially enabling faster and more reproducible analysis. Here we explored the reproducibility of human standardized scoring for an animal model of ARDS and its suitability for training CNNs for automated scoring at the whole slide level. We found large variations between human scorers, even for pre-clinical experts and board-certified pathologies in evaluating ARDS animal models. We demonstrate that CNNs (VGG16, EfficientNetB4) are suitable for automated scoring and achieve up to 83% F1-score and 78% accuracy. Thus, CNNs for histopathological classification of acute lung injury could help reduce human variability and eliminate a time-consuming manual research task with acceptable performance.}},
  author       = {{Augusto Silva, Iran and Kazemi Rashed, Salma and Hedlund, Ludwig and Lidfeldt, August and Gvazava, Nika and Stegmayr, John and Skoryk, Valeriia and Aits, Sonja and Wagner, Darcy}},
  keywords     = {{acute respiratory distress syndrome; lung; histology; deep learning; convolutional neural network; computer vision}},
  language     = {{eng}},
  month        = {{05}},
  note         = {{Preprint}},
  publisher    = {{bioRxiv}},
  title        = {{Deep learning for rapid and reproducible histology scoring of lung injury in a porcine model}},
  url          = {{http://dx.doi.org/10.1101/2023.05.12.540340}},
  doi          = {{10.1101/2023.05.12.540340}},
  year         = {{2023}},
}