Deep Learning for Personalized Preoperative Planning of Microsurgical Free Tissue Transfers

Authors

  • Eshika Saxena Harvard University, School of Engineering and Applied Sciences, Computer Science

DOI:

https://doi.org/10.1609/aaai.v36i11.21706

Keywords:

Machine Learning, Applications Of AI, Computer Vision

Abstract

Breast reconstruction surgery requires extensive planning, usually with a CT scan that helps surgeons identify which vessels are suitable for harvest. Currently, there is no quantitative method for preoperative planning. In this work, we successfully develop a Deep Learning algorithm to segment the vessels within the region of interest for breast reconstruction. Ultimately, this information will be used to determine the optimal reconstructive method (choice of vessels, extent of the free flap/harvested tissue) to reduce intra- and postoperative complication rates.

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Published

2022-06-28

How to Cite

Saxena, E. (2022). Deep Learning for Personalized Preoperative Planning of Microsurgical Free Tissue Transfers. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 13140-13141. https://doi.org/10.1609/aaai.v36i11.21706