This study aimed to explore the application effect of magnetic resonance imaging (MRI) based on deep learning in laparoscopic surgery for colorectal carcinoma (CRC). 40 patients with CRC who were diagnosed and required laparoscopic surgery were selected in the research. The MRI scan images of all patients were processed based on the convolutional neural network algorithm. The MRI images before and after treatment were set as the control group and the experimental group, respectively. The consistency of MRI results with laparoscopic and postoperative pathological biopsy results was observed. Through the comparative analysis of the research results, in terms of consistency with the surgical plane, the assessment results of the experimental group were more consistent than those of the control group and direct observation under laparoscopy, and the difference was statistically significant (). In terms of tumor T staging, the consistency between the experimental group and pathological biopsy results was superior to that of the control group, with considerable difference (). In conclusion, practically speaking, the application of MR images based on convolutional neural network algorithm in laparoscopic CRC surgery was better than conventional MRI technology. However, the research was a small-scale pathological study, which was not very representative.

中文翻译：

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磁共振成像下腹腔镜结直肠癌手术的深度学习

本研究旨在探讨基于深度学习的磁共振成像（MRI）在结直肠癌（CRC）腹腔镜手术中的应用效果。研究选择了 40 名确诊并需要进行腹腔镜手术的结直肠癌患者。所有患者的MRI扫描图像均基于卷积神经网络算法进行处理。治疗前后的MRI图像分别设为对照组和实验组。观察MRI结果与腹腔镜及术后病理活检结果的一致性。通过对研究结果的对比分析，在与手术平面的一致性方面，实验组的评估结果比对照组和腹腔镜下直接观察更一致，）。在肿瘤T分期方面，实验组与病理活检结果的一致性优于对照组，差异显着（）。综上所述，从实践上讲，基于卷积神经网络算法的MR图像在腹腔镜CRC手术中的应用优于常规MRI技术。但是，该研究是小规模的病理学研究，代表性不强。