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

ABSTRACT:

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 bet

SUBMITTER: Takayama M 

PROVIDER: S-EPMC11780306 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature