# Machine Learning-Based Automated Assessment of Intracorporeal Suturing in Laparoscopic Fundoplication

arXiv: https://arxiv.org/abs/2412.16195 · HTML: https://mlsanity.com/paper/2412.16195/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 47 references that are in the map.
- First posted on arXiv 16 Dec 2024.

## Builds on (most cited first, 12 of 12)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [Depth Pro: Sharp Monocular Metric Depth in Less Than a Second](https://mlsanity.com/paper/2410.02073/) (2024)
- [EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos](https://mlsanity.com/paper/1602.03012/) (2016)
- [Video-based surgical skill assessment using 3D convolutional neural networks](https://mlsanity.com/paper/1903.02306/) (2019)
- [Using 3D Convolutional Neural Networks to Learn Spatiotemporal Features for Automatic Surgical Gesture Recognition in Video](https://mlsanity.com/paper/1907.11454/) (2019)
- [Automated Surgical Skill Assessment in RMIS Training](https://mlsanity.com/paper/1712.08604/) (2017)
- [Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networks](https://mlsanity.com/paper/1908.07319/) (2019)
- [Video-based Formative and Summative Assessment of Surgical Tasks using Deep Learning](https://mlsanity.com/paper/2203.09589/) (2022)
- [COMPASS: A Formal Framework and Aggregate Dataset for Generalized Surgical Procedure Modeling](https://mlsanity.com/paper/2209.06424/) (2022)
- [Machine Learning Approach for Skill Evaluation in Robotic-Assisted Surgery](https://mlsanity.com/paper/1611.05136/) (2016)

## Built on by (most cited first, 2 of 2)

- [Efficient Frame Extraction: A Novel Approach Through Frame Similarity and Surgical Tool Tracking for Video Segmentation](https://mlsanity.com/paper/2501.11153/) (2025)
- [Extended KAFR: A kinematic-adaptive paradigm for the efficient analysis of surgical video](https://mlsanity.com/paper/2608.01058/) (2026)

## Abstract (arXiv)

Automated assessment of surgical skills using artificial intelligence (AI) provides trainees with instantaneous feedback. After bimanual tool motions are captured, derived kinematic metrics are reliable predictors of performance in laparoscopic tasks. Implementing automated tool tracking requires time-intensive human annotation. We developed AI-based tool tracking using the Segment Anything Model (SAM) to eliminate the need for human annotators. Here, we describe a study evaluating the usefulness of our tool tracking model in automated assessment during a laparoscopic suturing task in the fundoplication procedure. An automated tool tracking model was applied to recorded videos of Nissen fundoplication on porcine bowel. Surgeons were grouped as novices (PGY1-2) and experts (PGY3-5, attendings). The beginning and end of each suturing step were segmented, and motions of the left and right tools were extracted. A low-pass filter with a 24 Hz cut-off frequency removed noise. Performance was assessed using supervised and unsupervised models, and an ablation study compared results. Kinematic features--RMS velocity, RMS acceleration, RMS jerk, total path length, and Bimanual Dexterity--were extracted and analyzed using Logistic Regression, Random Forest, Support Vector Classifier, and XGBoost. PCA was performed for feature reduction. For unsupervised learning, a Denoising Autoencoder (DAE) model with classifiers, such as a 1-D CNN and traditional models, was trained. Data were extracted for 28 participants (9 novices, 19 experts). Supervised learning with PCA and Random Forest achieved an accuracy of 0.795 and an F1 score of 0.778. The unsupervised 1-D CNN achieved superior results with an accuracy of 0.817 and an F1 score of 0.806, eliminating the need for kinematic feature computation. We demonstrated an AI model capable of automated performance classification, independent of human annotation.