Abstract #4738

Deep learning tools to aid the evaluation of isthmic spondylolysis

Vadim Malis¹, Suraj Achar², Dosik Hwang³, and Won C. Bae^1,4

¹Radiology, University of California, San Diego, La Jolla, CA, United States, ²Family Medicine, University of California, San Diego, La Jolla, CA, United States, ³Electrical and Electronics Engineering, Yonsei University, Seoul, Korea, Republic of, ⁴VA San Diego Healthcare System, San Diego, CA, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Bone, UTE, Deep Learning, Image Regression, Saliency MapIsthmic spondylolysis results in fracture of pars interarticularis of the lumbar spine in young athletes. UTE MRI provide good bone contrast, although CT is still the gold standard. To take UTE MRI further, we developed supervised deep-learning tools to generate CT-like images and saliency maps of fracture probability from UTE MRI and CT, using ex vivo preparation of cadaveric spines. The results demonstrate feasibility of CT-like images to provide easier interpretability for bone fractures, due to improved image contrast and CNR, and the saliency maps to aid in quick detection of pars fracture by providing visual cues to the reader.

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