An artificial intelligence-based recognition model of colorectal liver metastases in intraoperative ultrasonography with improved accuracy through algorithm integration

Background/purpose: Contrast-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.

Methods: This 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.

Results: The 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.

Conclusions: A novel automated recognition model was developed for accurate CLM detection in CE-IOUS by integrating image- and algorithm-based models.