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- Title
A Development and Validation of an AI Model for Cardiomegaly Detection in Chest X-rays.
- Authors
Lee, Kang-Hee; Choi, Jun-Woo; Park, Chun-Oh; Han, Dong-Hun; Kang, Min-Soo
- Abstract
In this study, the development of a deep learning approach for distinguishing cardiomegaly in chest X-ray images and its validation process are presented. Typically, radiologists diagnose cardiomegaly by examining X-ray images. However, their interpretations can vary owing to subjective judgments, and mild cardiomegaly can be missed. For this reason, there is ongoing research into the use of AI-based deep learning algorithms as an adjunct to X-ray interpretation. In this study, radiologists collected 10,000 public images, from which 718 useful images were selected to create a dataset. A DenseNet121 algorithm was then used to develop an AI model for cardiomegaly detection. The results demonstrate an accuracy of 0.95, a recall of 0.91, and an F1 score of 0.94. Additional validation was performed to ensure the accuracy of the cardiomegaly detection model. The validation methods included saliency maps and guided backpropagation, which indicated the significance of the model. In conclusion, this study demonstrates the potential to develop a high-quality AI model with fewer data than previous studies, suggesting its applicability in the medical field.
- Subjects
MACHINE learning; ARTIFICIAL intelligence; CARDIAC hypertrophy; X-ray imaging; X-ray detection; DEEP learning
- Publication
Applied Sciences (2076-3417), 2024, Vol 14, Issue 17, p7465
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app14177465