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

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.