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Edge-Guided Deep Learning Model to Predict Fetal Brain Age Using MRI. J Neuroimaging. 2025 Nov\u2013Dec;35(6):e70099. 10.1111\/jon.70099 [\n                      \n                      ]. The previous paper evaluated an edge-guided dual-path supervised deep learning model (EGAF-Net) that uses FFT-based high-pass filtering and cross-attention fusion of edge and salient features. In contrast, the current work proposes a conceptually different structure-aware self-supervised fusion network (SSFN-Net), featuring entropy- and Sobel-guided masking, WFNO-based frequency-domain enhancement, and a gated fusion mechanism that connects self-supervised structural features to a KAN-ResNet regression backbone, along with a new joint optimization strategy [\n                      \n                      ].","order":6,"name":"Ethics","label":"Study subjects or cohorts overlap","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"order":7,"name":"Ethics","label":"Methodology","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}}]}}