A validation of an entropy-based artificial intelligence for ultrasound data in breast tumors.
- Resource Type
- Article
- Authors
- Huang, Zhibin; Yang, Keen; Tian, Hongtian; Wu, Huaiyu; Tang, Shuzhen; Cui, Chen; Shi, Siyuan; Jiang, Yitao; Chen, Jing; Xu, Jinfeng; Dong, Fajin
- Source
- BMC Medical Informatics & Decision Making. 1/2/2024, Vol. 24 Issue 1, p1-8. 8p.
- Subject
- *ARTIFICIAL intelligence
*BREAST tumors
*BREAST ultrasound
*ENTROPY
*DEEP learning
*MEDICAL screening
*VIDEO processing
- Language
- ISSN
- 1472-6947
Key points: 1. The study explored the impact of ultrasound images with different frequencies on the diagnostic efficacy of artificial intelligence. 2. Ultrasound images obtained with different frequency probes exhibited variable levels of average two-dimensional image entropy, influencing the diagnostic performance of artificial intelligence models in nuanced ways. 3. Datasets with higher average two-dimensional image entropy were associated with superior artificial intelligence breast diagnostic efficacy. [ABSTRACT FROM AUTHOR]