RESUMO
BACKGROUND: Laparoscopic cholecystectomy is a very frequent surgical procedure. However, in an ageing society, less surgical staff will need to perform surgery on patients. Collaborative surgical robots (cobots) could address surgical staff shortages and workload. To achieve context-awareness for surgeon-robot collaboration, the intraoperative action workflow recognition is a key challenge. METHODS: A surgical process model was developed for intraoperative surgical activities including actor, instrument, action and target in laparoscopic cholecystectomy (excluding camera guidance). These activities, as well as instrument presence and surgical phases were annotated in videos of laparoscopic cholecystectomy performed on human patients (n = 10) and on explanted porcine livers (n = 10). The machine learning algorithm Distilled-Swin was trained on our own annotated dataset and the CholecT45 dataset. The validation of the model was conducted using a fivefold cross-validation approach. RESULTS: In total, 22,351 activities were annotated with a cumulative duration of 24.9 h of video segments. The machine learning algorithm trained and validated on our own dataset scored a mean average precision (mAP) of 25.7% and a top K = 5 accuracy of 85.3%. With training and validation on our dataset and CholecT45, the algorithm scored a mAP of 37.9%. CONCLUSIONS: An activity model was developed and applied for the fine-granular annotation of laparoscopic cholecystectomies in two surgical settings. A machine recognition algorithm trained on our own annotated dataset and CholecT45 achieved a higher performance than training only on CholecT45 and can recognize frequently occurring activities well, but not infrequent activities. The analysis of an annotated dataset allowed for the quantification of the potential of collaborative surgical robots to address the workload of surgical staff. If collaborative surgical robots could grasp and hold tissue, up to 83.5% of the assistant's tissue interacting tasks (i.e. excluding camera guidance) could be performed by robots.
Assuntos
Colecistectomia Laparoscópica , Aprendizado de Máquina , Procedimentos Cirúrgicos Robóticos , Colecistectomia Laparoscópica/métodos , Procedimentos Cirúrgicos Robóticos/métodos , Humanos , Suínos , Animais , Algoritmos , Gravação em Vídeo , Fluxo de TrabalhoRESUMO
Background: Medical robotics has the potential to improve surgical and endoluminal procedures by enabling high-precision movements and superhuman perception. Objectives: To present historical, existing and future robotic assistants for surgery and to highlight their characteristics and advantages for keyhole surgery and endoscopy. Methods: In particular, historical medical robots and conventional telemanipulators are presented and compared with minimally invasive continuum robots and novel robotic concepts from practice and research. In addition, a perspective for future generations of surgical and endoluminal robots is offered. Conclusion: Robot-assisted medicine offers great added value for quality of intervention as well as safety for surgeons and patients. In the future, more surgical steps will be performed (semi-)autonomously and in cooperation with the experts.