Abstract #4049

Differentiation of Benign and Malignant Vertebral Fractures on Spine MRI Using ResNet Deep Learning Compared to Radiologists’ Reading

Lee-Ren Yeh¹, Yang Zhang², Jeon-Hor Chen², An-Chi Wang³, JieYu Yang³, Peter Chang², Daniel Chow², and Min-Ying Su²

¹Radiology, E-Da Hospital, Kaohsiung, Taiwan, ²University of California Irvine, Irvine, CA, United States, ³Radiology, Chi-Mei Medical Center, Tainan, Taiwan

This study compared the reading of three radiologists with different level of experience, and also investigated the potential of deep learning to differentiate between benign and malignant vertebral fractures based on T1W and T2W MRI. The results showed that deep learning using ResNet50 achieved a satisfactory diagnostic accuracy of 92%, although inferior to 98% made by a senior MSK radiologist and 96% made by a R4 resident, much higher compared to 66% made by a R1 resident. The inferior performance of ResNet50 might be partly explained by the very limited information when only considering a small bounding box.

This abstract and the presentation materials are available to members only; a login is required.

Join Here