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Artificial intelligence-based deep learning model for evaluating procedural consistency in microvascular anastomosis.

Jiuxu Chen^1,2, Thomas J On¹, Yuan Xu¹

¹1The Loyal and Edith Davis Neurosurgical Research Laboratory, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, Arizona.

Journal of Neurosurgery

|September 26, 2025

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This summary is machine-generated.

A deep learning model using long short-term memory (LSTM) accurately evaluates microanastomosis performance in neurosurgical training. This objective assessment of suturing skills offers a precise alternative to traditional subjective methods.

Area of Science:

Neurosurgery
Surgical Training
Artificial Intelligence

Background:

Objective assessment of microanastomosis is vital for neurosurgical training.
Current evaluation methods are subjective and time-consuming.
Deep learning offers a potential solution for precise performance analysis.

Purpose of the Study:

To develop and validate a deep learning model for objective microanastomosis performance evaluation.
To predict and compare suturing executions using long short-term memory (LSTM) architecture.
To provide a quantitative measure of surgical skill consistency and precision.

Main Methods:

Developed an LSTM-based neural network to model hand movements during microvascular anastomosis simulation.
Collected video data from expert neurosurgeons and a trainee.

Keywords:

artificial intelligence deep learning hand landmarks hand tracking microanastomosis microvascular anastomosis neurosurgical training vascular disorders

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Assessed model performance using Kullback-Leibler (KL) divergence and analyzed economy and flow of motion.

Main Results:

The LSTM model accurately predicted suturing movements with low KL divergence values for experts.
Trainee performance showed higher KL divergence, indicating less consistency.
Analysis of motion economy and flow metrics further validated the model's assessment capabilities.

Conclusions:

The LSTM-based model objectively assesses microanastomosis performance, capturing consistency and efficiency.
The model provides a validated, quantitative method for evaluating surgical skills.
Future work will involve broader application and refinement of performance metric interpretation.