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dc.rights.licenseRestricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.
dc.contributorDe, Suvranu
dc.contributorIntes, Xavier
dc.contributorYan, Pingkun
dc.contributorZhang, Lucy T.
dc.contributorLiu, Li (Emily)
dc.contributor.authorGao, Yuanyuan
dc.date.accessioned2021-11-03T09:22:12Z
dc.date.available2021-11-03T09:22:12Z
dc.date.created2021-02-22T15:32:18Z
dc.date.issued2020-08
dc.identifier.urihttps://hdl.handle.net/20.500.13015/2620
dc.descriptionAugust 2020
dc.descriptionSchool of Engineering
dc.description.abstractSecondly, we explore the possibility to emulate the current standardized surgical skill metric employed in the field, namely the FLS score, by combining neuroimaging data acquired during the task execution and machine learning methodologies for potentially fast and bedside implementation. In this context, we have validated a deep neural network, Brain-NET, that accurately predicts performance scores from hemodynamic data from the brain obtained using functional near-infrared spectroscopy (fNIRS). Furthermore, we are also currently implementing deep learning approaches to improve and speed up the fNIRS data preprocessing workflow towards enabling real-time implementation.
dc.description.abstractFirstly, we propose a machine learning methodology that could predict the learning curve features from the beginning of the training, which indicates that the training protocol could be personalized. We further defined a single factor that can describe how the learning curve parameters are related to each other.
dc.description.abstractLastly, we investigate the potentials of transcranial electrical stimulation (tES) in enhancing bimanual surgical skills. We performed a series of studies to explore the short term and long term effects of tES on surgical motor skill performance. Transcranial random noise stimulation (tRNS) enhances surgical motor skill performance in the short term, while transcranial direct current stimulation (tDCS) improves long term skill acquisition in decreasing surgical error. The concurrently acquired fNIRS data, while medical students performed an FLS task, elucidated a correlation between cortical activations as reported by fNIRS with tES excitation and improved performance.
dc.description.abstractThe traditional surgical apprenticeship model of “see one, do one, teach one” that was standardized by Halsted more than a century ago is still in effect in residency programs around the world. Though, it has been greatly enhanced via the use of advanced technologies, such as training simulators as well as standardized tasks and assessment metrics. Notably, the Society of American Gastrointestinal and Endoscopic Surgeons (SAGES) and the American College of Surgeons (ACS) developed the Fundamentals of Laparoscopic Surgery (FLS) program. The FLS training and assessment portions of this program are prerequisites to board certification in general surgery and hence, impact every general surgery resident in the USA. Though, the current practice relies on scoring metrics that can suffer from subjectivity and inconsistencies while personnel intensive and time-consuming. Moreover, the current training protocols rely on repetitive execution of the tasks without remediation during the training leading to more practice than needed and a plateau that is less than the maximal potential level. In this project, we aim at enhancing the methodologies to assess surgical performance as well as investigating new approaches to facilitate surgical skill performance, acquisition, retention, and transfer beyond mere task repetition.
dc.language.isoENG
dc.publisherRensselaer Polytechnic Institute, Troy, NY
dc.relation.ispartofRensselaer Theses and Dissertations Online Collection
dc.subjectMechanical engineering
dc.titleImproving surgical motor skill assessment and acquisition via neuromodulation, neuroimaging, and machine learning
dc.typeElectronic thesis
dc.typeThesis
dc.digitool.pid180354
dc.digitool.pid180355
dc.digitool.pid180356
dc.rights.holderThis electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
dc.description.degreePhD
dc.relation.departmentDept. of Mechanical, Aerospace, and Nuclear Engineering


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