@proceedings {1826,
	title = {SOM and LVQ classification of endovascular surgeons using motion-based metrics},
	year = {2016},
	month = {01/2016},
	pages = {227-237},
	abstract = {<p><span style="color: rgb(51, 51, 51); font-family: Georgia, serif; font-size: 17px; letter-spacing: 0.102px; background-color: rgb(252, 252, 252);">An increase in the prevalence of endovascular surgery requires a growing number of proficient surgeons. Current endovascular surgeon evaluation techniques are subjective and time-consuming; as a result, there is a demand for an objective and automated evaluation procedure. Leveraging reliable movement metrics and tool-tip data acquisition, we here use neural network techniques such as LVQs and SOMs to identify the mapping between surgeons{\textquoteright} motion data and imposed rating scales. Using LVQs, only 50\&nbsp;\% testing accuracy was achieved. SOM visualization of this inadequate generalization, however, highlights limitations of the present rating scale and sheds light upon the differences between traditional skill groupings and neural network clusters. In particular, our SOM clustering both exhibits more truthful segmentation and demonstrates which metrics are most indicative of surgeon ability, providing an outline for more rigorous evaluation strategies.</span></p>},
	issn = {978-3-319-28517-7},
	doi = {https://doi.org/10.1007/978-3-319-28518-4_20},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-28518-4_20},
	attachments = {https://mahilab.rice.edu/sites/default/files/publications/WSOM_2016.pdf},
	author = {Kramer, B.D. and Dylan P. Losey and Marcia K. O{\textquoteright}Malley}
}
