Comparative evaluation of interpretation methods in surface-based age prediction for neonates
Xiaotong Wu,
Chenxin Xie,
Fangxiao Cheng,
Zhuoshuo Li,
Ruizhuo Li,
Duan Xu,
Hosung Kim,
Jianjia Zhang,
Hongsheng Liu,
Mengting Liu
Affiliations
Xiaotong Wu
School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, 518107, China
Chenxin Xie
Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China
Fangxiao Cheng
Institute of Medical Technology, Peking University Health Science Center, Beijing, 100191, China
Zhuoshuo Li
School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, 518107, China
Ruizhuo Li
Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China
Duan Xu
Department of Radiology & Biomedical Imaging, University of California, San Francisco, CA, USA
Hosung Kim
Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
Jianjia Zhang
School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, 518107, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing, China; Corresponding authors at: Associate Professor of Biomedical Engineering, Sun Yat-sen University, Shenzhen, 518107, China.
Hongsheng Liu
Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China; Co-corresponding author at: Director of Radiology Department, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China.
Mengting Liu
School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, 518107, China; Corresponding authors at: Associate Professor of Biomedical Engineering, Sun Yat-sen University, Shenzhen, 518107, China.
Significant changes in brain morphology occur during the third trimester of gestation. The capability of deep learning in leveraging these morphological features has enhanced the accuracy of brain age predictions for this critical period. Yet, the opaque nature of deep learning techniques, often described as ''black box'' approaches, limits their interpretability, posing challenges in clinical applications. Traditional interpretable methods developed for computer vision and natural language processing may not directly translate to the distinct demands of neuroimaging. In response, our research evaluates the effectiveness and adaptability of two interpretative methods—regional age prediction and the perturbation-based saliency map approach—for predicting the brain age of neonates. Analyzing 664 T1 MRI scans with the NEOCIVET pipeline to extract brain surface and cortical features, we assess how these methods illuminate key brain regions for age prediction, focusing on technical analysis with clinical insight. Through a comparative analysis of the saliency index (SI) with relative brain age (RBA) and the examination of structural covariance networks, we uncover the saliency index's enhanced ability to pinpoint regions vital for accurate indication of clinical factors. Our results highlight the advantages of perturbation techniques in addressing the complexities of medical data, steering clinical interventions for premature neonates towards more personalized and interpretable approaches. This study not only reveals the promise of these methods in complex medical scenarios but also offers a blueprint for implementing more interpretable and clinically relevant deep learning models in healthcare settings.
