Real-time estimation of surgical procedure duration

Abstract

Efficiency in the Operating Room (OR) is a topic of growing interest. Planning of care is a crucial element to ensure optimal use of the ORs. Currently, OR scheduling is considered as a complex task based on predictions of surgery duration. The latter are often based on average times, but turn out to be inaccurate in practice because of various factors (such as complexity, patient's characteristics, unexpected events, etc). The aim of this study is to develop a prediction system that estimates in real-time the remaining duration of a surgical procedure. The prediction system was based on monitoring the progress of a procedure by recording the activation of a single piece of equipment in the OR, the electrosurgical device. Support Vector Machines was then used as a classifier to predict the remaining surgical procedure duration and thereby the optimal timing to start preparing the next patient for surgery. The classifier was trained with data on the activation of the electrosurgical device during 55 laparoscopic cholecystectomies.The performance tests showed a mean error rate about 0.2, which means that about 80% of the procedures were classified correctly. The real-time prediction system is a promising tool to improve OR planning and decrease unnecessary patients' waiting times.

Surgery duration: Optimized prediction and causality analysis

“…them, we adopted those that are independent of the surgery type. We moreover used additional features that were suggested by domain experts, i.e., experienced surgeons [3,8,21,23]. The full list of features is shown in Table 1.…”

“…Earlier studies developed regression ML models to predict DOS values. Most studies used linear regression algorithms for the development of the prediction models [21][22][23][24]. Some recent studies, e.g., that by Jiao et al (2020), employed ML algorithms, e.g., multilayer perceptron.…”

RSDNet: Learning to Predict Remaining Surgery Duration from Laparoscopic Videos Without Manual Annotations

“…However, a semi-automatic method is disadvantageous since it disrupts the surgical workflow, and in the other studies, the signals are typically obtained through manual annotation, rendering the methods impractical for intraoperative applications. In [4], a classification approach using the activation of the electrosurgical devices was proposed to answer: should the next patient be called? However, the pipeline is constrained to start the detection after the procedure has progressed for 15 minutes and assumes that the next patient should be prepared 25 minutes before the surgery ends.…”

“…In other words, this evaluation indicates how big the error is when the method predicts that the surgery will end in, for instance, 25 minutes. A reliable RSD prediction of 25 minutes is particularly important as it is the time required for preparing the next patient for both cholecystectomy [4] and bypass surgeries [20]. The performance of RSDNet and the Naïve approach are compared since these approaches do not rely on manual annotations, enabling them to be more easily scaled up to different surgery types.…”

“…For instance, it has been shown by [3] that general surgeons underestimated surgery durations by 31 minutes in average, while anesthesiologists underestimated the durations by 35 minutes (29% and 167% error with respect to predicted durations, respectively). Such underestimation will lead to longer waiting time for patients, e.g., a large variation of waiting time (47±17 mins) over 157 cholecystectomy patients was observed in [4], while it typically requires 25 minutes for patient preparation.…”