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Diagnostic validation of vertebral heart score machine learning algorithm for canine lateral chest radiographs
J Solomon1, S Bender1, P Durgempudi1
1IDEXX Laboratories, Inc., Westbrook, ME, USA.
Insights
A new algorithm for the vertebral heart score (VHS) shows performance comparable to veterinary cardiologists in assessing canine heart size. This tool aids in identifying and staging heart disease, though further validation is recommended.
Area of Science:
- Veterinary cardiology
- Medical imaging analysis
- Artificial intelligence in diagnostics
Background:
- The vertebral heart score (VHS) is a crucial metric for evaluating canine cardiac health.
- Accurate VHS assessment is vital for diagnosing and staging heart disease, and for providing prognostic information.
- Manual VHS scoring can be subjective and time-consuming.
Purpose of the Study:
- To validate a novel algorithm for calculating the vertebral heart score (VHS) against manual scoring by board-certified veterinary cardiologists.
- To assess the algorithm's accuracy and reliability in predicting heart size relative to thoracic vertebrae.
Main Methods:
- A convolutional neural network (CNN) was developed for semantic segmentation of anatomical features to predict heart size and vertebral bodies.
- The CNN-generated predictions were used to calculate the VHS.
- An external validation set of 1200 canine lateral radiographs was used, with 400 images manually scored by three cardiologists using the Buchanan method.
Main Results:
- The algorithm's VHS predictions demonstrated a 95th percentile absolute difference of 1.05 vertebrae compared to cardiologist scores.
- A mean bias of -0.09 vertebrae was observed between the algorithm and cardiologist scores.
- The algorithm's performance was found to be well-calibrated across the predictive range.
Conclusions:
- The developed vertebral heart score algorithm performs comparably to board-certified veterinary cardiologists.
- The algorithm shows significant potential as an objective tool for canine heart size assessment.
- Further external validation in diverse clinical settings is recommended prior to widespread adoption.
Objectives:
The vertebral heart score is a measurement used to index heart size relative to thoracic vertebra. Vertebral heart score can be a useful tool for identifying and staging heart disease and providing prognostic information. The purpose of this study is to validate the use of a vertebral heart score algorithm compared to manual vertebral heart scoring by three board-certified veterinary cardiologists.
Materials And Methods:
A convolutional neural network centred around semantic segmentation of relevant anatomical features was developed to predict heart size and vertebral bodies. These predictions were used to calculate the vertebral heart score. An external validation study consisting of 1200 canine lateral radiographs was randomly selected to match the underlying distribution of vertebral heart scores. Three American College of Veterinary Internal Medicine board-certified cardiologists were enrolled to manually score 400 images each using the traditional Buchanan method. Post-scoring, the cardiologists evaluated the algorithm for misaligned anatomic landmarks and overall image quality.
Results:
The 95th percentile absolute difference between the cardiologist vertebral heart score and the algorithm vertebral heart score was 1.05 vertebrae (95% confidence interval: 0.97 to 1.20 vertebrae) with a mean bias of -0.09 vertebrae (95% confidence interval: -0.12 to -0.05 vertebrae). In addition, the model was observed to be well calibrated across the predictive range.
Clinical Significance:
We have found the performance of the vertebral heart score algorithm comparable to three board-certified cardiologists. While validation of this vertebral heart score algorithm has shown strong performance compared to veterinarians, further external validation in other clinical settings is warranted before use in those settings.
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