# Preserving Privacy in Surgical Video Analysis Using Artificial Intelligence: A Deep Learning Classifier to Identify Out-of-Body Scenes in Endoscopic Videos

arXiv: https://arxiv.org/abs/2301.07053 · HTML: https://mlsanity.com/paper/2301.07053/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 17 Jan 2023.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos](https://mlsanity.com/paper/1602.03012/) (2016)
- [Recognition of Instrument-Tissue Interactions in Endoscopic Videos via Action Triplets](https://mlsanity.com/paper/2007.05405/) (2020)
- [Multi-Task Temporal Convolutional Networks for Joint Recognition of Surgical Phases and Steps in Gastric Bypass Procedures](https://mlsanity.com/paper/2102.12218/) (2021)
- [Weakly Supervised Convolutional LSTM Approach for Tool Tracking in Laparoscopic Videos](https://mlsanity.com/paper/1812.01366/) (2018)
- [RSDNet: Learning to Predict Remaining Surgery Duration from Laparoscopic Videos Without Manual Annotations](https://mlsanity.com/paper/1802.03243/) (2018)
- [Live Laparoscopic Video Retrieval with Compressed Uncertainty](https://mlsanity.com/paper/2203.04301/) (2022)

## Built on by (most cited first, 8 of 8)

- [Challenges in Multi-centric Generalization: Phase and Step Recognition in Roux-en-Y Gastric Bypass Surgery](https://mlsanity.com/paper/2312.11250/) (2023)
- [CholecTrack20: A Multi-Perspective Tracking Dataset for Surgical Tools](https://mlsanity.com/paper/2312.07352/) (2023)
- [A Generalist Model for Diverse Text-Guided Medical Image Synthesis](https://mlsanity.com/paper/2405.09806/) (2024)
- [Efficient Frame Extraction: A Novel Approach Through Frame Similarity and Surgical Tool Tracking for Video Segmentation](https://mlsanity.com/paper/2501.11153/) (2025)
- [Endoshare: A Publicly Available, Surgeons-Friendly Solution to De-Identify and Manage Surgical Videos](https://mlsanity.com/paper/2510.20087/) (2025)
- [Artificial Intelligence for the Assessment of Peritoneal Carcinosis during Diagnostic Laparoscopy for Advanced Ovarian Cancer](https://mlsanity.com/paper/2512.14797/) (2025)
- [AI for Quality Assurance in the Operating Room](https://mlsanity.com/paper/2606.30657/) (2026)
- [Extended KAFR: A kinematic-adaptive paradigm for the efficient analysis of surgical video](https://mlsanity.com/paper/2608.01058/) (2026)

## Abstract (arXiv)

Objective: To develop and validate a deep learning model for the identification of out-of-body images in endoscopic videos. Background: Surgical video analysis facilitates education and research. However, video recordings of endoscopic surgeries can contain privacy-sensitive information, especially if out-of-body scenes are recorded. Therefore, identification of out-of-body scenes in endoscopic videos is of major importance to preserve the privacy of patients and operating room staff. Methods: A deep learning model was trained and evaluated on an internal dataset of 12 different types of laparoscopic and robotic surgeries. External validation was performed on two independent multicentric test datasets of laparoscopic gastric bypass and cholecystectomy surgeries. All images extracted from the video datasets were annotated as inside or out-of-body. Model performance was evaluated compared to human ground truth annotations measuring the receiver operating characteristic area under the curve (ROC AUC). Results: The internal dataset consisting of 356,267 images from 48 videos and the two multicentric test datasets consisting of 54,385 and 58,349 images from 10 and 20 videos, respectively, were annotated. Compared to ground truth annotations, the model identified out-of-body images with 99.97% ROC AUC on the internal test dataset. Mean ± standard deviation ROC AUC on the multicentric gastric bypass dataset was 99.94±0.07% and 99.71±0.40% on the multicentric cholecystectomy dataset, respectively. Conclusion: The proposed deep learning model can reliably identify out-of-body images in endoscopic videos. The trained model is publicly shared. This facilitates privacy preservation in surgical video analysis.