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Deep Learning for Personalized Preoperative Planning of Microsurgical Free Tissue Transfers

Authors

Eshika Saxena

Harvard University, School of Engineering and Applied Sciences, Computer Science


Proceedings:

No. 11: IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 36

Track:

AAAI Undergraduate Consortium

Downloads:

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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.

DOI:

10.1609/aaai.v36i11.21706


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 36