Surgical phase modelling in minimal invasive surgery.
Surgical phase modelling in minimal invasive surgery.
复制标题
最小入侵手术中的手术期建模。
DOI：
10.1007/s00464-018-6417-4
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发表时间：
2019-05
期刊：
影响因子：
--
通讯作者：
van den Dobbelsteen JJ
中科院分区：
文献类型：
--
作者：
Meeuwsen FC;van Luyn F;Blikkendaal MD;Jansen FW;van den Dobbelsteen JJ
手术流程建模 (SPM) 提供了自动了解手术工作流程的可能性，并有可能改善手术室后勤和手术护理。大多数研究都集中在腹腔镜胆囊切除术的相位识别模型上，因为它的执行标准且频繁。为了证明 SPM 的广泛适用性，需要研究更多样化和更复杂的程序。本研究的目的是调查我们在腹腔镜子宫切除术 (LH) 中识别和提取手术阶段的准确性，以及手术时间的固有变异性。为了展示该方法的适用性，该模型用于自动预测手术结束时间。 40 个视频记录的 LH 数据集经过手动注释以供仪器使用，并分为 10 个手术阶段。仪器的使用为构建随机森林手术阶段识别模型提供了特征输入，该模型经过训练可以自动识别手术阶段。进行十倍交叉验证以优化预测整个手术过程中手术结束时间的模型。平均手术时间为 128±27 分钟。可以看到特定阶段内的巨大变化。总体而言，随机森林模型识别过程中当前阶段的准确度达到 77%。其中六个阶段的预测准确率超过其持续时间的 80%。在预测手术结束时间时，整个手术过程中的平均误差为 16±13 分钟。本研究展示了一种根据器械使用数据识别 40 例腹腔镜子宫切除术病例手术阶段的术中方法。该模型能够自动检测手术阶段，以准确预测手术结束时间。
Surgical Process Modelling (SPM) offers the possibility to automatically gain insight in the surgical workflow, with the potential to improve OR logistics and surgical care. Most studies have focussed on phase recognition modelling of the laparoscopic cholecystectomy, because of its standard and frequent execution. To demonstrate the broad applicability of SPM, more diverse and complex procedures need to be studied. The aim of this study is to investigate the accuracy in which we can recognise and extract surgical phases in laparoscopic hysterectomies (LHs) with inherent variability in procedure time. To show the applicability of the approach, the model was used to automatically predict surgical end-times. A dataset of 40 video-recorded LHs was manually annotated for instrument use and divided into ten surgical phases. The use of instruments provided the feature input for building a Random Forest surgical phase recognition model that was trained to automatically recognise surgical phases. Tenfold cross-validation was performed to optimise the model for predicting the surgical end-time throughout the procedure. Average surgery time is 128 ± 27 min. Large variability within specific phases is seen. Overall, the Random Forest model reaches an accuracy of 77% recognising the current phase in the procedure. Six of the phases are predicted accurately over 80% of their duration. When predicting the surgical end-time, on average an error of 16 ± 13 min is reached throughout the procedure. This study demonstrates an intra-operative approach to recognise surgical phases in 40 laparoscopic hysterectomy cases based on instrument usage data. The model is capable of automatic detection of surgical phases for generation of a solid prediction of the surgical end-time.
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DOI：
10.1007/s11548-015-1195-0
发表时间：
2015-06-01
影响因子：
3
作者：
Forestier, Germain;Riffaud, Laurent;Jannin, Pierre
通讯作者：
Jannin, Pierre
影响因子：
6.7
作者：
Zhou, JS;Dexter, F;Lubarsky, DA
通讯作者：
Lubarsky, DA
影响因子：
4.5
作者：
Bouarfa, L.;Jonker, P. P.;Dankelman, J.
通讯作者：
Dankelman, J.
影响因子：
2.2
作者：
Kranzfelder, Michael;Schneider, Armin;Feussner, Hubertus
通讯作者：
Feussner, Hubertus
DOI：
10.1007/s11548-015-1222-1
发表时间：
2015-09-01
影响因子：
3
作者：
Katic, Darko;Julliard, Chantal;Gibaud, Bernard
通讯作者：
Gibaud, Bernard