Abstract #3812

Evaluating the performance of deep learning system for detecting focal liver lesions on contrast-enhanced MRI

Haoran Dai¹, Yuyao Xiao¹, Caixia Fu², Robert Grimm³, Heinrich von Busch⁴, Bram Stieltjes⁵, Moon Hyung Choi⁶, Chun Yang¹, and Mengsu Zeng^1,7

¹Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China, ²MR Application Development, Siemens Shenzhen Magnetic Resonance Ltd, Shenzhen, China, ³MR Predevelopment, Siemens Healthineers AG, Erlangen, Germany, ⁴Digital & Automation Innovation, Siemens Healthineers AG, Erlangen, Germany, ⁵Universitätsspital Basel, Basel, Switzerland, ⁶Eunpyeong St. Mary’s Hospital, Catholic University of Korea, Seoul, Korea, Republic of, ⁷Shanghai Institute of Medical Imaging, Shanghai, China

Synopsis

Keywords: AI/ML Software, Machine Learning/Artificial Intelligence, focal liver lesions, Magnetic resonance imaging

Motivation: The number of focal liver lesions (FLLs) detected by imaging has increased worldwide, highlighting the need to develop a robust, objective system for automatically detecting FLLs.

Goal(s): This study aimed to evaluate the application value of deep learning based artificial intelligence (AI) software in detecting FLLs.

Approach: We compared the performance and agreement of deep learning based AI software with those of radiologists in detecting and evaluating malignant lesions in enhanced MRI of patients with FLLs.

Results: AI displayed effective detection performance for malignant lesions down to <10 mm. The measured size of malignant tumors was consistent with the pathologic and manual sizes.

Impact: Our results indicated that the use of AI might promote the detection ability of sub-centimeter-sized liver malignant lesions, providing a reference for selecting clinical treatment schemes.

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