medical-7

Surgical Skills Evaluation

3260 papers • 126 benchmarks • 313 datasets

The task is to classify surgical skills using data that is recorded during the surgical intervention.

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Benchmarks

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Most implemented papers

Evaluating surgical skills from kinematic data using convolutional neural networks

Hassan Ismail Fawaz, G. Forestier, J. Weber, L. Idoumghar, Pierre-Alain Muller•Wed Jun 06 2018

A Convolutional Neural Network is designed to evaluate surgeon skills by extracting patterns in the surgeon motions performed in robotic surgery and this method achieved very competitive results with 100% accuracy on the suturing and needle passing tasks.

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Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networks

Hassan Ismail Fawaz, G. Forestier, J. Weber, L. Idoumghar, Pierre-Alain Muller•Mon Jul 29 2019

A convolutional neural network to classify surgical skills by extracting latent patterns in the trainees’ motions performed during robotic surgery is designed and this type of interpretable machine learning model could integrate within “Operation Room 2.0” and support novice surgeons in improving their skills to eventually become experts.

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Video-based surgical skill assessment using 3D convolutional neural networks

S. Speidel, Isabel Funke, J. Weitz, S. T. Mees•Tue Mar 05 2019

The results demonstrate the feasibility of deep learning-based assessment of technical skill from surgical video, and the 3D ConvNet is able to learn meaningful patterns directly from the data, alleviating the need for manual feature engineering.

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