Automated assessment of simulated laparoscopic surgical performance using 3DCNN

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dc.contributor.authorPower, Daviden
dc.contributor.authorUllah, Ihsanen
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2025-03-10T15:14:05Z
dc.date.available2025-03-10T15:14:05Z
dc.date.issued2024en
dc.description.abstractArtificial 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.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationPower, 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/EMBC53108.2024.10782160en
dc.identifier.doi10.1109/EMBC53108.2024.10782160.en
dc.identifier.eissn2694-0604en
dc.identifier.endpage4en
dc.identifier.issn2375-7477en
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/17159
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289_P2/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en
dc.rights© 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.en
dc.subjectPerformance assessmenten
dc.subjectConvolutional neural networken
dc.subjectComputer visionen
dc.subjectMinimally invasive surgeryen
dc.subjectLevel of expertiseen
dc.subjectVideo analysisen
dc.subjectSurgical trainingen
dc.subjectLevel of performanceen
dc.subjectMedian timeen
dc.subjectBinary classificationen
dc.subjectTest accuracyen
dc.subjectMulti-labelen
dc.subjectTraining groupen
dc.subjectBatch normalization layeren
dc.subjectDropout layeren
dc.subjectSurgical instrumentsen
dc.subjectSurgical skillsen
dc.subjectSurgical simulationen
dc.subjectTime metricsen
dc.subjectEnd of the videoen
dc.subjectCustom dataseten
dc.titleAutomated assessment of simulated laparoscopic surgical performance using 3DCNNen
dc.typeConference itemen
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