右半结肠系膜血管解剖分型繁杂、变异发生率高，传统二维腹腔镜受平面视野限制，在术前血管预判、术中精细解剖、中央淋巴结完整清扫、围手术期风险量化评估方面存在固有短板。以人工智能（AI）三维重建、3D打印实体模型、荧光腹腔镜、手术机器人、混合现实（MR）实时叠加导航、深度学习术中识别为核心的数字化智能技术体系，可从术前规划、术中操作、术后质控全流程降低血管撕裂、大出血、淋巴结清扫不全等核心手术风险，显著提升D3根治术规范化程度。依托智能影像完成肿瘤T分期、Henle干分型、患者基础合并疾病三维综合评估后建立三级个体化手术分层方案，能够针对低、中、高危不同人群匹配适配的微创设备、清扫范围与消化道重建方式，实现差异化精准外科治疗。相较于传统Logistic回归模型，基于随机森林、支持向量机（SVM）等算法构建的机器学习预测模型对吻合口漏、手术部位感染、远期肿瘤复发的区分度与预测效能显著提升。数字化智能诊疗平台与个体化分层外科模式深度融合，重塑了传统右半结肠癌微创诊疗范式，可同步提升肿瘤根治彻底性、围手术期操作安全性与患者远期生存质量。但现阶段AI术中实时去遮挡识别算法、国产手术机器人长期多中心肿瘤学终点证据仍存在明显缺口，需开展更大规模、更长随访周期的前瞻性随机对照研究进一步验证其临床推广价值。

The mesenteric vessels of the right colon have complex classifications and high variation rates. Restricted by planar field of view, traditional two-dimensional laparoscopy has inherent shortcomings in preoperative vascular prediction, intraoperative fine dissection, complete central lymph node dissection and quantitative perioperative risk assessment. The digital intelligent technology system centered on AI 3D reconstruction, 3D printed physical models, fluorescent laparoscopy, surgical robots, MR real-time superimposed navigation and deep learning intraoperative recognition can reduce core surgical risks such as vascular laceration, massive hemorrhage and incomplete lymph node cleaning from the whole process of preoperative planning, intraoperative operation and postoperative quality control, and significantly improve the standardization of D3 radical resection. Based on comprehensive intelligent imaging evaluation of tumor T stage, Henle trunk classification and patients' underlying comorbidities, a three-level individualized surgical stratification scheme can match adapted minimally invasive equipment, dissection scope and digestive tract reconstruction methods for low, medium and high-risk populations to realize differentiated precise surgical treatment. Compared with traditional Logistic regression models, machine learning models constructed based on random forest and SVM algorithms have significantly higher discrimination and predictive efficiency for anastomotic leakage, surgical site infection and long-term tumor recurrence. The deep integration of digital intelligent diagnosis and treatment platform and individualized stratified surgical model has reshaped the traditional minimally invasive diagnosis and treatment paradigm of right colon cancer, which can simultaneously improve the radicality of tumor resection, intraoperative safety and patients' long-term quality of life. However, there are obvious gaps in the intraoperative real-time occlusion removal recognition algorithm of AI and long-term multicenter oncological endpoint evidence of domestic surgical robots at this stage. Larger-scale, longer follow-up prospective randomized controlled studies are needed to further verify its clinical promotion value.