International journal of computer assisted radiology and surgery · 2025
In-depth characterization of a laparoscopic radical prostatectomy procedure based on surgical process modeling.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Rodrigues Nuno S, Morais Pedro, Buschle Lukas R, Lima Estevão, Vilaça João L
Аннотация
PURPOSE: Minimally invasive surgical approaches are currently the standard of care for men with prostate cancer, presenting higher rates of erectile function preservation. With these laparoscopic techniques, there is an increasing amount of data and information available. Adaptive systems can play an important role, acting as an intelligent information filter, assuring that all the available information can become useful for the procedure and not overwhelming for the surgeon. Standardizing and structuring the surgical workflow are key requirements for such smart assistants to recognize the different surgical steps through context information about the environment. This work aims to do a detailed characterization of a laparoscopic radical prostatectomy procedure, focusing on the formalization of medical expert knowledge, via surgical process modeling.
METHODS: Data were acquired manually, via online and offline observation, and discussion with medical experts. A total of 14 procedures were observed. Both manual laparoscopic radical prostatectomy and robot-assisted laparoscopic prostatectomy were studied. The derived SPM focuses only on the intraoperatory part of the procedure, with constant feedback from the endoscopic camera. For surgery observation, a dedicated Excel template was developed.
RESULTS: The final model is represented in a descriptive and numerical format, combining task description with a workflow diagram arrangement for ease of interpretation. Practical applications of the generated surgical process model are exemplified with the creation of activation trees for surgical phase identification. Anatomical structures are reported for each phase, distinguishing between visible and inferable ones. Additionally, the surgeons involved are identified, surgical instruments, and actions performed in each phase. A total of 11 phases were identified and characterized. Average surgery duration is 87 min.
CONCLUSION: The generated surgical process model is a first step toward the development of a context-aware surgical assistant and can potentially be used as a roadmap by other research teams, operating room managers and surgical teams.
METHODS: Data were acquired manually, via online and offline observation, and discussion with medical experts. A total of 14 procedures were observed. Both manual laparoscopic radical prostatectomy and robot-assisted laparoscopic prostatectomy were studied. The derived SPM focuses only on the intraoperatory part of the procedure, with constant feedback from the endoscopic camera. For surgery observation, a dedicated Excel template was developed.
RESULTS: The final model is represented in a descriptive and numerical format, combining task description with a workflow diagram arrangement for ease of interpretation. Practical applications of the generated surgical process model are exemplified with the creation of activation trees for surgical phase identification. Anatomical structures are reported for each phase, distinguishing between visible and inferable ones. Additionally, the surgeons involved are identified, surgical instruments, and actions performed in each phase. A total of 11 phases were identified and characterized. Average surgery duration is 87 min.
CONCLUSION: The generated surgical process model is a first step toward the development of a context-aware surgical assistant and can potentially be used as a roadmap by other research teams, operating room managers and surgical teams.