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

Authors

Song Q,He X,Wang Y,Gao H,Tan L,Ma J,Kang L,Han P,Luo Y,Wang K

Affiliations (9)

  • Department of Ultrasound, First Medical Center of General Hospital of Chinese PLA, Beijing, 100853, China.
  • Department of Ultrasound, Seventh Medical Center, General Hospital of Chinese PLA, Beijing, 100700, China.
  • School of Information Sciences and Technology, Northwest University, Xi'an, 710127, Shaanxi Province, China.
  • CAS Key Laboratory of Molecular Imaging, State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
  • Department of Ultrasound, Shandong Province Maternal and Child Health Care Hospital, Jinan, 250014, China.
  • Department of Ultrasound, Second Medical Center, General Hospital of Chinese PLA, Beijing, 100700, China.
  • Beijing Da Wang Lu Emergency Hospital, Beijing, China.
  • Department of Ultrasound, First Medical Center of General Hospital of Chinese PLA, Beijing, 100853, China. [email protected].
  • CAS Key Laboratory of Molecular Imaging, State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. [email protected].

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.

Topics

LiverArtificial IntelligenceJournal ArticleValidation Study

Ready to Sharpen Your Edge?

Join hundreds of your peers who rely on RadAI Slice. Get the essential weekly briefing that empowers you to navigate the future of radiology.

We respect your privacy. Unsubscribe at any time.