Technological Trend Analysis for Surgical Operation Duration Estimation

Q: What is the clinical evidence from this study?

Study design: Systematic Review. Population: Surgical operation duration estimation. Intervention: Machine learning, deep learning, and statistical methods for surgical duration estimation. Primary outcome: Frequency of methodology usage and preferred evaluation metrics in surgical duration estimation literature.

Q: What does this research mean for the field?

Machine learning is the predominant methodology for surgical operation duration estimation, with Mean Absolute Error serving as the most common evaluation metric. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

Ziya Karakaya; Bahadır TATAR

doi:10.55549/epstem.1368277

October 16, 2023The Eurasia Proceedings of Science Technology Engineering and MathematicsOpen Access

Technological Trend Analysis for Surgical Operation Duration Estimation

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Key result

Machine learning dominates surgical duration estimation research, utilized in ~46% of reviewed studies.

Why the study?

Surgical procedures are complex and operative time is variable, but accurate operation duration estimation is needed to maximize operating room efficiency and optimize hospital resources.

Key Points

To examine recent technological trends and evaluate the performance of machine learning and statistical models for predicting surgical operation durations.
Reviewed literature investigating statistical distributions (Log-Normal and Empirical distributions) and algorithmic models for operative time forecasting.
Assessed predictive methods—including Linear Regression, Random Forest, AdaBoost, Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks—across datasets ranging from 14 to 302,300 surgical records.
Machine learning models regularly outperformed classical statistical methods, with tree-based regressors such as XGBoost achieving an R-Square of 0.770, RMSE of 92.95 minutes, and MAE of 44.31 minutes on a cohort of 3,189 surgeries.
Across large-scale evaluations, XGBoost delivered a Root Mean Square Error of 31.6 min and Mean Absolute Error of 18.71 min on 124,528 records, and achieved an accuracy of 92.0% with an AUC of 78.0% in an 864-case cohort.
Neural approaches and boosted regression trees demonstrated superior predictive capability compared to standard hospital heuristics, with a continuous ranked probability score of 18.1 minutes achieved by mixture density networks across 53,783 pediatric procedures.

Study Design

Type

Systematic Review

Structured PICO

P

Population

Systematic mapping study of 24 publications (selected from 351 candidates) evaluating machine learning, deep learning, and statistical models for surgical operation duration estimation.

I

Intervention

Machine learning (45.8% of studies, including XGBoost, Random Forest, Linear Regression), statistical methods (16.7%), and deep learning (12.5%, including ANN, CNN, MLP, LSTM) for predicting surgical operation duration

O

Outcome

Methodological frequency and evaluation metric preference (such as Mean Absolute Error, Root Mean Square Error, and R-Square) in surgical duration estimation research

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Implication

This systematic mapping study demonstrates that machine learning models—evaluated primarily by Mean Absolute Error and Root Mean Square Error—dominate recent research efforts to predict surgical operation duration and optimize operating room scheduling.

Abstract

Surgical procedures are complex in nature and operative time is subject to variability influenced by many factors. Accurate estimation of the surgical operation duration not only helps to maximize Operation rooms’ efficiency, but also helps to optimize hospital resources which are a crucial factor in planning surgical procedures. In this regard, Al techniques such as machine learning and deep learning promise to significantly improve the duration estimation by identifying hidden factors and make more accurate prediction. They achieve this success by identifying latent factors which are generally hard to be explored by human intelligence. Eventually, accuracy in time estimation added to a good scheduling optimization leads to make more efficient utilization of hospital resources by better aligning Operation Room, relevant equipment, and human resources. This study addresses the recent trends in research on surgical operations duration estimation, considering the relevant factors.

Discussion

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Cite This Study

Karakaya et al. (2023) conducted a systematic review in Surgical operation duration estimation. Machine learning, deep learning, and statistical methods for surgical duration estimation was evaluated on Frequency of methodology usage and preferred evaluation metrics in surgical duration estimation literature. Machine learning was the most frequently utilized methodology for surgical operation duration estimation (45.8% of 24 studies), with Mean Absolute Error being the most common evaluation metric.

synapsesocial.com/papers/6a7a4084fbd9051d3b0e2f5d https://doi.org/10.55549/epstem.1368277

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Technological Trend Analysis for Surgical Operation Duration Estimation

Key Points

Study Design

Structured PICO

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Technological Trend Analysis for Surgical Operation Duration Estimation

Key Points

Study Design

Structured PICO

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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