 The study aims to develop a more lenient and flexible classification for amyloid deposition in clinical practice, using a disease-specific deep learning method to identify amyloid equivocality. The results show that 139 individuals were identified as amyloid equivocal, displaying intermediate amyloid deposition between positive and negative groups. No difference in glucose metabolism or cognitive performance was observed between amyloid negativity and equivocality. A guide to assist in the interpretation of amyloid equivocality by visual reading with auxiliary criteria, including two cut points and deep learning methods was established. This article was authored by Shuhua Ren, Yong Sheng Pan, Zhang Peng Li, and others.