Applying deep learning (DL) for annotating surgical instruments in robot-assisted minimally invasive surgeries (MIS) represents a significant advancement in surgical technology. This systematic review examines 48 studies that and advanced DL methods and architectures. These sophisticated DL models have shown notable improvements in the precision and efficiency of detecting and segmenting surgical tools. The enhanced capabilities of these models support various clinical applications, including real-time intraoperative guidance, comprehensive postoperative evaluations, and objective assessments of surgical skills. By accurately identifying and segmenting surgical instruments in video data, DL models provide detailed feedback to surgeons, thereby improving surgical outcomes and reducing complication risks. Furthermore, the application of DL in surgical education is transformative. The review underscores the significant impact of DL on improving the accuracy of skill assessments and the overall quality of surgical training programs. However, implementing DL in surgical tool detection and segmentation faces challenges, such as the need for large, accurately annotated datasets to train these models effectively. The manual annotation process is labor-intensive and time-consuming, posing a significant bottleneck. Future research should focus on automating the detection and segmentation process and enhancing the robustness of DL models against environmental variations. Expanding the application of DL models across various surgical specialties will be essential to fully realize this technology's potential. Integrating DL with other emerging technologies, such as augmented reality (AR), also offers promising opportunities to further enhance the precision and efficacy of surgical procedures.

翻译：在机器人辅助微创手术中应用深度学习进行手术器械标注代表了手术技术的重大进步。本系统综述分析了48项研究，探讨了先进的深度学习方法与架构。这些复杂的深度学习模型在手术工具检测与分割的精度和效率方面展现出显著提升。模型增强的能力支持多种临床应用，包括实时术中引导、全面术后评估以及手术技能的客观评价。通过准确识别和分割视频数据中的手术器械，深度学习模型为外科医生提供详细反馈，从而改善手术结果并降低并发症风险。此外，深度学习在手术教育中的应用具有变革性。本综述强调了深度学习在提升技能评估准确性和手术培训项目整体质量方面的显著影响。然而，在手术工具检测与分割中实施深度学习面临诸多挑战，例如需要大规模精确标注的数据集来有效训练这些模型。手动标注过程劳动密集且耗时，构成显著瓶颈。未来研究应聚焦于自动化检测与分割流程，并增强深度学习模型对环境变化的鲁棒性。拓展深度学习模型在不同外科专科的应用对于充分实现该技术潜力至关重要。将深度学习与增强现实等其他新兴技术相结合，也为进一步提升手术操作的精确性和有效性提供了前景广阔的发展机遇。