ArticleJournal of hepato-biliary-pancreatic sciences2025
An artificial intelligence-based recognition model of colorectal liver metastases in intraoperative ultrasonography with improved accuracy through algorithm integration.
Article in Journal of hepato-biliary-pancreatic sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Review
- Risk Factors and Strategies for Failure to Rescue Following Hepatectomy: A Review.Journal of hepato-biliary-pancreatic sciences · 2025Review
- An artificial intelligence-based recognition model of colorectal liver metastases in intraoperative ultrasonography with improved accuracy through algorithm integration.Journal of hepato-biliary-pancreatic sciences · 2025Article
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Authors and funding
10 authors.
Funding
Abstract
background/purposeContrast-enhanced intraoperative ultrasonography (CE-IOUS) is crucial for detecting colorectal liver metastases (CLM) during surgery. Although artificial intelligence shows potential in diagnostic systems, its application in CE-IOUS is limited.
methodsThis study aimed to develop an automatic tumor detection model using Mask region-based convolutional neural network (Mask R-CNN) for CE-IOUS images. CE-IOUS videos of the CLM from 121 patients were collected, generating ground truth data. A total of 2659 images were obtained. Two models were developed: the basic recognition model (BRM), which was trained on CE-mode images, and the subtraction model (SM), which used images created by a subtraction algorithm that highlighted the differences in pixel values between the basic-mode and CE-mode images. The subtraction algorithm focuses on echogenicity differences. These two models were combined into a combination model (CM), which assessed outcomes using the prediction probabilities from both models.
resultsThe optimal epochs were determined by the maximum area under the curve (AUC), and the thresholds were calculated accordingly. BRM, SM, and CM achieved 89.4%, 86.6%, and 96.5% accuracy, respectively. CM outperformed the individual models, achieving an AUC of 0.99.
conclusionsA novel automated recognition model was developed for accurate CLM detection in CE-IOUS by integrating image- and algorithm-based models.
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