Automated assessment of simulated laparoscopic surgical performance using 3DCNN
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Date
2024
Authors
Power, David
Ullah, Ihsan
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Institute of Electrical and Electronics Engineers (IEEE)
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Abstract
Artificial intelligence & Computer vision have the potential to improve surgical training, especially for minimally invasive surgery by analyzing intraoperative and simulation videos for training or performance improvement purposes. Among these, techniques based on deep learning have rapidly improved, from recognizing objects, instruments, and gestures, to remembering past surgical steps and phases of surgery. However, data scarcity is a problem, particularly in surgery, where complex datasets and human annotation are expensive and time-consuming, and in most cases rely on direct intervention of clinical expertise. Laproscopic surgical assessment of performance traditionally relies on direct observation or video analysis by human experts, a costly and time-consuming undertaking. A newly collected simulated laparoscopic surgical dataset (LSPD) is presented that will initiate the research in automating this problem and avoiding manual expert assessments. LSPD statistical analyses is given to show similarity and differences between different expertise level (on Stack, Bands, and Tower Skills). Finally, a convolutional neural network is used to predict the experience level of the surgeons, where the model achieved good distinguishing results. The proposed work offers the potential to automate performance assessment and self-learn important features that can discriminate between the performance of novice, trainee, and expert levels.
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Keywords
Performance assessment , Convolutional neural network , Computer vision , Minimally invasive surgery , Level of expertise , Video analysis , Surgical training , Level of performance , Median time , Binary classification , Test accuracy , Multi-label , Training group , Batch normalization layer , Dropout layer , Surgical instruments , Surgical skills , Surgical simulation , Time metrics , End of the video , Custom dataset
Citation
Power, D. and Ullah, I. (2024) ‘Automated assessment of simulated laparoscopic surgical performance using 3DCNN’, 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA, pp. 1–4. https://doi.org/10.1109/EMBC53108.2024.10782160
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© 2024, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.