ClipAssistNet: bringing real-time safety feedback to operating rooms

ClipAssistNet: bringing real-time safety feedback to operating rooms

Purpose Cholecystectomy is one of the most common laparoscopic procedures. A critical phase of laparoscopic cholecystectomy consists in clipping the cystic duct and artery before cutting them. Surgeons can improve the clipping safety by ensuring full visibility of the clipper, while enclosing the ar...

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Bibliographic Details
Published in:International journal for computer assisted radiology and surgery Vol. 17; no. 1; pp. 5 - 13
Main Authors: Aspart, Florian, Bolmgren, Jon L., Lavanchy, Joël L., Beldi, Guido, Woods, Michael S., Padoy, Nicolas, Hosgor, Enes
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01-01-2022
Springer Nature B.V
Springer Verlag
Subjects:
Annotations
Cholecystectomy
Cholecystectomy, Laparoscopic
Computer Imaging
Computer Science
Datasets
Feedback
Health Informatics
Human health and pathology
Humans
Image classification
Imaging
Laparoscopy
Life Sciences
Medicine
Medicine & Public Health
Neural networks
Operating Rooms
Original
Original Article
Pattern Recognition and Graphics
Radiology
Real time
Safety
Surgeons
Surgery
Surgical Instruments
Vision
Deep learning
Surgical intelligence
Surgical instrument visibility
Laparoscopic Cholecystectomy
Intraoperative safety feedback
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Summary:Purpose Cholecystectomy is one of the most common laparoscopic procedures. A critical phase of laparoscopic cholecystectomy consists in clipping the cystic duct and artery before cutting them. Surgeons can improve the clipping safety by ensuring full visibility of the clipper, while enclosing the artery or the duct with the clip applier jaws. This can prevent unintentional interaction with neighboring tissues or clip misplacement. In this article, we present a novel real-time feedback to ensure safe visibility of the instrument during this critical phase. This feedback incites surgeons to keep the tip of their clip applier visible while operating. Methods We present a new dataset of 300 laparoscopic cholecystectomy videos with frame-wise annotation of clipper tip visibility. We further present ClipAssistNet, a neural network-based image classifier which detects the clipper tip visibility in single frames. ClipAssistNet ensembles predictions from 5 neural networks trained on different subsets of the dataset. Results Our model learns to classify the clipper tip visibility by detecting its presence in the image. Measured on a separate test set, ClipAssistNet classifies the clipper tip visibility with an AUROC of 0.9107, and 66.15% specificity at 95% sensitivity. Additionally, it can perform real-time inference (16 FPS) on an embedded computing board; this enables its deployment in operating room settings. Conclusion This work presents a new application of computer-assisted surgery for laparoscopic cholecystectomy, namely real-time feedback on adequate visibility of the clip applier. We believe this feedback can increase surgeons’ attentiveness when departing from safe visibility during the critical clipping of the cystic duct and artery.
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ISSN:1861-6410
1861-6429
DOI:10.1007/s11548-021-02441-x