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Development of Artificial Intelligence Image Classification Models for Determination of Umbilical Cord Vascular Anomalies.
- Source :
- Journal of Ultrasound in Medicine; May2024, Vol. 43 Issue 5, p881-897, 17p
- Publication Year :
- 2024
-
Abstract
- Objective: The goal of this work was to develop robust techniques for the processing and identification of SUA using artificial intelligence (AI) image classification models. Methods: Ultrasound images obtained retrospectively were analyzed for blinding, text removal, AI training, and image prediction. After developing and testing text removal methods, a small n‐size study (40 images) using fastai/PyTorch to classify umbilical cord images. This data set was expanded to 286 lateral‐CFI images that were used to compare: different neural network performance, diagnostic value, and model predictions. Results: AI‐Optical Character Recognition method was superior in its ability to remove text from images. The small n‐size mixed single umbilical artery determination data set was tested with a pretrained ResNet34 neural network and obtained and error rate average of 0.083 (n = 3). The expanded data set was then tested with several AI models. The majority of the tested networks were able to obtain an average error rate of <0.15 with minimal modifications. The ResNet34‐default performed the best with: an image‐classification error rate of 0.0175, sensitivity of 1.00, specificity of 0.97, and ability to correctly infer classification. Conclusion: This work provides a robust framework for ultrasound image AI classifications. AI could successfully classify umbilical cord types of ultrasound image study with excellent diagnostic value. Together this study provides a reproducible framework to develop AI‐specific ultrasound classification of umbilical cord or other diagnoses to be used in conjunction with physicians for optimal patient care. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02784297
- Volume :
- 43
- Issue :
- 5
- Database :
- Complementary Index
- Journal :
- Journal of Ultrasound in Medicine
- Publication Type :
- Academic Journal
- Accession number :
- 176650255
- Full Text :
- https://doi.org/10.1002/jum.16418