TSP-OCS: A Time-Series Prediction for Optimal Camera Selection in Multi-Viewpoint Surgical Video Analysis

Xinyu Liu , Xiaoguang Lin , Xiang Liu

IEEE Journal of Biomedical and Health Informatics +

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November 24, 2025

View abstract on PubMed

Summary

This study introduces an automated multi-view camera selection system for open thyroidectomy surgery recordings. The AI framework enhances surgical video documentation and training by selecting the most informative camera angles, improving content comprehensibility.

Area Of Science

Medical Imaging
Computer Vision
Surgical Education

Background

Traditional single-camera surgical recordings suffer from occlusions and fixed angles, limiting educational and clinical evaluation value.
Open thyroidectomy documentation requires comprehensive visual data for effective training and assessment.

Purpose Of The Study

To develop an automated system for selecting optimal camera views during open thyroidectomy.
To enhance the comprehensibility and utility of surgical video recordings for training and evaluation.

Main Methods

Implemented a multi-viewpoint synchronized camera setup (six cameras) for recording open thyroidectomy.
Developed a supervised time-series prediction framework using visual and semantic features with TimeBlocks for camera view selection.
Constructed a dataset from five synchronized six-view thyroidectomy procedures.

Main Results

The proposed method demonstrated stable accuracy compared to existing baselines.
The system outperformed several mainstream time-series prediction models in the context of surgical video analysis.
Successfully automated the selection of informative camera views for critical surgical steps.

Conclusions

Multi-view camera selection using AI offers a valuable approach to improve surgical video documentation for thyroidectomy.
This technology has significant potential for enhancing surgical training and clinical evaluation.
The developed framework provides a foundation for future research in automated surgical video analysis.