【摘要】目的评估双吻合器在直肠癌保肛术中的应用价值，并探讨吻合口漏等并发症的防治措施。方法对81例采用双吻合器行直肠癌前切除术患者的临床资料进行回顾性分析。结果全组术中肿瘤切除后远端直肠的缝合、吻合过程顺利，手术时间120～190 min,平均160 min。术后发生吻合口漏3例（3.7%），吻合口狭窄1例（1.2%），无手术死亡。结论双吻合器技术可帮助外科医生顺利完成直肠癌前切除术中结直肠的吻合，并且安全、可靠。
ObjectiveTo summarize the recent research progress of artificial intelligence (AI) for perioperative management of colorectal cancer (CRC), and to explore its clinical application value and future development direction. MethodThe relevant research on AI in the perioperative management of CRC surgery from China National Knowledge Infrastructure, Wanfang, PubMed, and Google Scholar databases in the past 5 years was retrieved and reviewed. ResultsCurrently, AI had been applied throughout the entire process related to CRC surgery. Preoperatively, AI-assisted analysis of CT or MRI images facilitated precise tumor staging assessment, prediction of neoadjuvant therapy response, and surgical planning optimization. Intraoperatively, real-time endoscopic vision integrated with AI enabled tumor localization, tracking, and tissue identification accuracy, enhancing procedural safety. Postoperatively, AI-supported rehabilitation protocols optimized early mobilization, enabled continuous complication monitoring, and refined follow-up management, providing personalized intervention strategies for early clinical intervention to improve patient outcomes. ConclusionsCurrent research demonstrates promising outcomes of AI applications in CRC perioperative management, yet reveals a significant imbalance in research focus with predominant investigations concentrated on preoperative assistance. Notably, postoperative domains, including fall prevention, medication error detection, complication mitigation, adjuvant therapy decision support, psychosocial support, recurrence surveillance, and survival follow-up, exhibit marked deficiencies in AI exploration and clinical translation, constituting a critical weakness in establishing comprehensive intelligent support throughout the perioperative continuum. Future research must extend beyond addressing intraoperative AI challenges to prioritize AI-augmented prediction of short-/long-term complications, optimization of personalized rehabilitation pathways, precision adjuvant therapy decision support, intelligent follow-up systems, and applications enhancing postoperative quality of life and long-term survival outcomes.