{"@context":"https://schema.org","@type":"ScholarlyArticle","headline":"AIxSuture: vision-based assessment of open suturing skills","author":[{"@type":"Person","name":"Hanna Hoffmann"},{"@type":"Person","name":"Isabel Funke"},{"@type":"Person","name":"Philipp Peters"},{"@type":"Person","name":"Danush Kumar Venkatesh"},{"@type":"Person","name":"Jan Egger"},{"@type":"Person","name":"Dominik Rivoir"},{"@type":"Person","name":"Rainer Röhrig"},{"@type":"Person","name":"Frank Hölzle"},{"@type":"Person","name":"Sebastian Bodenstedt"},{"@type":"Person","name":"Marie-Christin Willemer"},{"@type":"Person","name":"Stefanie Speidel"},{"@type":"Person","name":"Behrus Puladi"}],"datePublished":"2024","isPartOf":{"@type":"Periodical","name":"International Journal of Computer Assisted Radiology and Surgery"},"identifier":"https://doi.org/https://doi.org/10.1007/s11548-024-03093-3","description":"Abstract\n Purpose\n Efficient and precise surgical skills are essential in ensuring positive patient outcomes. By continuously providing real-time, data driven, and objective evaluation of surgical performance, automated skill assessment has the potential to greatly improve surgical skill training. Whereas machine learning-based surgical skill assessment is gaining traction for minimally invasive techniques, this cannot be said for open surgery skills. Open surgery generally has more degrees of freedom when compared to minimally invasive surgery, making it more difficult to interpret. In this paper, we present novel approaches for skill assessment for open surgery skills.\n\n \n Methods\n We analyzed a novel video dataset for open suturing training. We provide a detailed analysis of the dataset and define evaluation guidelines, using state of the art deep learning models. Furthermore, we present novel benchmarking results for surgical skill assessment in open suturing. The models are trained to classify a video into three skill levels based on the global rating score. To obtain initial results for video-based surgical skill classification, we benchmarked a temporal segment network with both an I3D and a Video Swin backbone on this dataset.\n \n Results\n The dataset is composed of 314 videos of approximately five minutes each. Model benchmarking results are an accuracy and F1 score of up to 75 and 72%, respectively. This is similar to the performance achieved by the individual raters, regarding inter-rater agreement and rater variability. We present the first end-to-end trained approach for skill assessment for open surgery training.\n \n Conclusion\n We provide a thorough analysis of a new dataset as well as novel benchmarking results for surgical skill assessment. This opens the doors to new advances in skill assessment by enabling video-based skill assessment for classic surgical techniques with the potential to improve the surgical outcome of patients.\n ","url":"https://innscience.ca/en/community/literature/25fe45aa-72d8-4121-91db-558f8eefbb5b"}