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Related Experiment Video

Updated: Sep 27, 2025

Surgical Robot-Assisted Transanal Specimen Extraction Radical Sigmoidectomy Without an Auxiliary Abdominal Incision

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Performance and Capability Assessment in Surgical Subtask Automation.

Tamás D Nagy^1,2,3, Tamás Haidegger^1,4

¹Antal Bejczy Center for Intelligent Robotics, EKIK, Óbuda University, Bécsi út 96/B, 1034 Budapest, Hungary.

Sensors (Basel, Switzerland)

|April 12, 2022

Summary

Standardizing performance evaluation for robot-assisted minimally invasive surgery (RAMIS) automation is crucial. This study proposes novel methodologies and models to address the lack of established metrics for surgical subtask automation.

Keywords:

RAMIS partial automation robot surgery benchmarking robot surgery validation

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Area of Science:

Robotics in Medicine
Surgical Technology
Automation and Control

Background:

Robot-assisted minimally invasive surgery (RAMIS) has advanced surgical practices over the past two decades.
Partial autonomy in RAMIS is considered the next frontier, aiming to reduce surgeon fatigue and cognitive load by automating repetitive subtasks.
Currently, a lack of standardized evaluation methods, metrics, and benchmarks hinders progress in surgical subtask automation.

Purpose of the Study:

To propose standard methodologies for performance evaluation in surgical subtask automation.
To address the void in research regarding standardized metrics for automated surgical procedures.
To facilitate objective comparison and advancement of autonomous surgical systems.

Main Methods:

Introduction of a novel characterization model for surgical automation.
Overview and analysis of existing metrics for performance evaluation and comparison.
Presentation of a workflow model to guide researchers in metric selection and application.
Identification of existing and potential benchmark systems for surgical automation.

Main Results:

A comprehensive characterization model for surgical automation is presented.
Existing performance evaluation metrics are analyzed, highlighting their limitations.
A workflow model is proposed to aid researchers in selecting appropriate metrics.
The necessity for standardized benchmarks in surgical automation is articulated.

Conclusions:

Standardized methodologies and benchmarks are essential for the advancement of surgical subtask automation in RAMIS.
The proposed characterization and workflow models provide a framework for consistent performance evaluation.
Addressing human-machine interface quality, robustness, and legal/ethical considerations is vital for the successful implementation of surgical autonomy.