Clinical validation of AI assisted animal ultrasound models for diagnosis of early liver trauma

Clinical validation of AI assisted animal ultrasound models for diagnosis of early liver trauma

Abstract The study aimed to develop an AI-assisted ultrasound model for early liver trauma identification, using data from Bama miniature pigs and patients in Beijing, China. A deep learning model was created and fine-tuned with animal and clinical data, achieving high accuracy metrics. In internal...

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Bibliographic Details
Main Authors: Qing Song, Xuelei He, Yanjie Wang, Hanjing Gao, Li Tan, Jun Ma, Linli Kang, Peng Han, Yukun Luo, Kun Wang
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Liver contusions
Lacerations
Deep learning
Artificial intelligence
Animal models
Predictive value of tests
Online Access:https://doi.org/10.1038/s41598-025-91900-5
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Summary:Abstract The study aimed to develop an AI-assisted ultrasound model for early liver trauma identification, using data from Bama miniature pigs and patients in Beijing, China. A deep learning model was created and fine-tuned with animal and clinical data, achieving high accuracy metrics. In internal tests, the model outperformed both Junior and Senior sonographers. External tests showed the model’s effectiveness, with a Dice Similarity Coefficient of 0.74, True Positive Rate of 0.80, Positive Predictive Value of 0.74, and 95% Hausdorff distance of 14.84. The model’s performance was comparable to Junior sonographers and slightly lower than Senior sonographers. This AI model shows promise for liver injury detection, offering a valuable tool with diagnostic capabilities similar to those of less experienced human operators.
ISSN:2045-2322

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