An evaluation of a simple model for predicting surgery duration using a set of surgical procedure parameters

Yuniartha, Deny Ratna; Masruroh, Nur Aini; Herliansyah, Muhammad Kusumawan

doi:10.1016/j.imu.2021.100633

Cited by 10 publications

(2 citation statements)

References 11 publications

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“…However, these methods are subjective, workflow interruptive and present limited reliability 4 . Besides, the mean prediction error can be up to 35 min, which in some surgeries is close to the average duration 5 ,. 6 …”

Section: Introductionmentioning

confidence: 60%

Prediction of remaining surgery duration in laparoscopic videos based on visual saliency and the transformer network

Loukas,

Seimenis,

Prevezanou

et al. 2024

Robotics Computer Surgery

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BackgroundReal‐time prediction of the remaining surgery duration (RSD) is important for optimal scheduling of resources in the operating room.MethodsWe focus on the intraoperative prediction of RSD from laparoscopic video. An extensive evaluation of seven common deep learning models, a proposed one based on the Transformer architecture (TransLocal) and four baseline approaches, is presented. The proposed pipeline includes a CNN‐LSTM for feature extraction from salient regions within short video segments and a Transformer with local attention mechanisms.ResultsUsing the Cholec80 dataset, TransLocal yielded the best performance (mean absolute error (MAE) = 7.1 min). For long and short surgeries, the MAE was 10.6 and 4.4 min, respectively. Thirty minutes before the end of surgery MAE = 6.2 min, 7.2 and 5.5 min for all long and short surgeries, respectively.ConclusionsThe proposed technique achieves state‐of‐the‐art results. In the future, we aim to incorporate intraoperative indicators and pre‐operative data.

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Section: Introductionmentioning

confidence: 60%

Prediction of remaining surgery duration in laparoscopic videos based on visual saliency and the transformer network

Loukas,

Seimenis,

Prevezanou

et al. 2024

Robotics Computer Surgery

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“…Bartek et al (2019) use machine learning approaches for point estimation of surgery duration. Deny Ratna et al (2021) alternatively estimate the surgery duration as categorical values represented by time bins. The estimated values from the two studies are intended for general usage, not restricted to the context of surgery scheduling.…”

Section: Estimating Surgery Durationmentioning

confidence: 99%

Feature‐driven robust surgery scheduling

Wang

Zhou

Tang

2023

Production and Operations Management

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Patient features such as gender, age, and underlying disease are crucial to improving the model fidelity of surgery duration. In this paper, we study a robust surgery scheduling problem augmented by patient feature segmentation. We focus on the surgery‐to‐operating room allocations for elective patients and future emergencies. Using feature data, we classify patients into different types using machine learning methods and characterize the uncertain surgery duration via a feature‐based cluster‐wise ambiguity set. We propose a feature‐driven adaptive robust optimization model that minimizes an overtime riskiness index, which helps mitigate both the magnitude and probability of working overtime. The model can be reformulated as a second‐order conic programming problem. From the reformulation, we find that minimizing the overtime riskiness index is equivalent to minimizing a Fano factor. This makes our robust optimization model easily interpretable to healthcare practitioners. To efficiently solve the problem, we develop a branch‐and‐cut algorithm and introduce symmetry‐breaking constraints. Numerical experiments demonstrate that our model outperforms benchmark models in a variety of performance metrics.

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Nonlinear Regression of Remaining Surgical Duration via Bayesian LSTM-Based Deep Negative Correlation Learning

Tao

Zheng

2022

Lecture Notes in Computer Science

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An evaluation of a simple model for predicting surgery duration using a set of surgical procedure parameters

Cited by 10 publications

References 11 publications

Prediction of remaining surgery duration in laparoscopic videos based on visual saliency and the transformer network

Prediction of remaining surgery duration in laparoscopic videos based on visual saliency and the transformer network

Feature‐driven robust surgery scheduling

Nonlinear Regression of Remaining Surgical Duration via Bayesian LSTM-Based Deep Negative Correlation Learning

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