Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Real-time monitoring system for early stroke detection based on fog computing and enhanced deep learning techniques.

Scientific reports·2025

Same author

Machine learning framework for predicting the shear capacity of demountable bolted connectors in composite beams.

Scientific reports·2025

Same author

A new ensemble heart attack diagnosis (EHAD) model using artificial intelligence techniques.

Scientific reports·2025

Same author

An improved Sinh Cosh optimizer for optimizing energy management system in nano-grids.

Scientific reports·2025

Same author

Prediction of ultimate load capacity of demountable shear stud connectors using machine learning techniques.

Scientific reports·2025

Same author

An effective brain stroke diagnosis strategy based on feature extraction and hybrid classifier.

Scientific reports·2025

Same journal

Peripheral B-cell receptor repertoire predicts immune-related adverse events following immune checkpoint inhibitor therapy in advanced renal cell carcinoma.

Scientific reports·2026

Same journal

Effects of black soldier fly (Hermetia illucens L.) larvae zoocompost on the mineral element content of blue honeysuckle berries.

Scientific reports·2026

Same journal

Investigation on absorption refrigeration performance of R1243zf with imidazolium ionic liquid as the working pairs.

Scientific reports·2026

Same journal

DeepTriage-CN: integrating clinical text with vital signs for emergency department admission prediction in an aging population.

Scientific reports·2026

Same journal

Gold nanoparticles as dual-action antiviral agents: disruption of SARS-CoV-2 viral envelopes and RNA integrity.

Scientific reports·2026

Same journal

Comparison of capillary microsampling and venous blood for multi-pathogen serosurveillance.

Scientific reports·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 17, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Accurate surgery time prediction (ASTP) strategy based on artificial intelligence techniques.

Rana Mohamed El-Balka¹, Asmaa H Rabie^2,3, Ahmed I Saleh²

¹Computer and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt. RannaMohamedElbalka@std.mans.edu.eg.

Scientific Reports

|June 12, 2026

Summary

This summary is machine-generated.

Accurate surgical time prediction using the two-layered Accurate Surgical Time Prediction (ASTP) framework improves operating room scheduling. This interpretable AI approach efficiently identifies key features for precise surgery duration estimation.

Keywords:

Histogram Gradient Boosting Regression Long Short Term Memory Machine learning Operating room Prediction Time SHapley Additive exPlanations

More Related Videos

Pioneering Patient-Specific Approaches for Precision Surgery Using Imaging and Virtual Reality

Pioneering Patient-Specific Approaches for Precision Surgery Using Imaging and Virtual Reality

Published on: April 5, 2024

Automatic Surgery in Transcatheter Aortic Valve Replacement Using Augmented Reality

Automatic Surgery in Transcatheter Aortic Valve Replacement Using Augmented Reality

Published on: August 9, 2024

Related Experiment Videos

Last Updated: Jun 17, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Pioneering Patient-Specific Approaches for Precision Surgery Using Imaging and Virtual Reality

Pioneering Patient-Specific Approaches for Precision Surgery Using Imaging and Virtual Reality

Published on: April 5, 2024

Automatic Surgery in Transcatheter Aortic Valve Replacement Using Augmented Reality

Automatic Surgery in Transcatheter Aortic Valve Replacement Using Augmented Reality

Published on: August 9, 2024

Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Operations Research

Background:

Efficient operating room scheduling is critical for patient care and resource management.
Accurate prediction of surgical duration is a persistent challenge in healthcare operations.

Purpose of the Study:

To develop and evaluate a two-layered Accurate Surgical Time Prediction (ASTP) framework for estimating surgical duration.
To enhance operating room scheduling through interpretable and resource-efficient AI models.

Main Methods:

Utilized Long Short Term Memory (LSTM) with SHapley Additive exPlanations (SHAP) and Random Forest permutation importance for feature selection.
Employed Histogram Gradient Boosting Regression (HGBR) on optimized feature subsets (TOP-K) and compared with ANNs and recurrent models.
Validated the framework on real-world (Nile Hospital) and public (MOVER) operating room datasets.

Main Results:

HGBR achieved the best performance on the Nile Hospital dataset (MAE: 8.89 min, RMSE: 19.5 min, R-squared: 0.26) using only four features.
On the MOVER dataset, HGBR showed strong predictive behavior, with optimal subset performance at TOP-7.
The ASTP framework demonstrated resource efficiency and interpretability, outperforming other models with reduced feature complexity.

Conclusions:

The ASTP framework offers an effective, interpretable, and efficient solution for surgical time prediction.
This approach supports intelligent operating room scheduling and has potential for hospital decision-making integration.
Feature importance analysis combined with regression models significantly improves surgical duration prediction accuracy.