Abstract #2604

Deep learning system for automated detection of posterior ligamentous complex injury in patients with thoracolumbar fracture on magnetic resonance imaging

Sang Won Jo¹, Eun Kyung Khil¹, Seun Ah Lee¹, Jihe Lim¹, Jae Hyeok Lee², and Yu Sung Yoon³

¹Radiology, Hallym University Dongtan Sacred Heart Hospital, Hwaseong, Korea, Republic of, ²Deepnoid, Seoul, Korea, Republic of, ³Radiology, Soonchunhyang University Bucheon Hospital, Bucheon, Korea, Republic of

Synopsis

Keywords: MSK, Machine Learning/Artificial Intelligence, thoracolumbar fractureThis study was aimed to develop a deep learning algorithm for automated detection and localization of posterior ligamentous complex (PLC) injury on magnetic resonance imaging and evaluate its diagnostic performance. The sensitivity, the specificity and AUC of inception-ResNet V2 architecture of second step were 88%, 82% and 0.928, for the internal test set and 86%, 74% and 0.916 for the external test set, respectively. A deep learning algorithm detected PLC injury in patients with thoracolumbar fracture with a high diagnostic performance which was validated using external data set.

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