Improving detection of impacted animal bones on lateral neck radiograph using a deep learning artificial intelligence algorithm

Improving detection of impacted animal bones on lateral neck radiograph using a deep learning artificial intelligence algorithm

Key points The AI-aided interpretation improved the accuracy for doctors of every specialty. The AI model accurately identified more cases than the radiologists’ reports. The AI model increased the detection rate and may decrease required CT imaging.

Bibliographic Details
Main Authors: Yueh-Sheng Chen, Sheng-Dean Luo, Chi-Hsun Lee, Jian-Feng Lin, Te-Yen Lin, Sheung-Fat Ko, Chiun-Chieh Yu, Pi-Ling Chiang, Cheng-Kang Wang, I.-Min Chiu, Yii-Ting Huang, Yi-Fan Tai, Po-Teng Chiang, Wei-Che Lin
Format: Article
Language:English
Published: SpringerOpen 2023-03-01
Series:Insights into Imaging
Subjects:
Artificial intelligence
Retrospective studies
Lateral neck radiograph
Animal bone impaction
Online Access:https://doi.org/10.1186/s13244-023-01385-x

Internet

https://doi.org/10.1186/s13244-023-01385-x

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