Motivation: Deep learning networks offers an opportunity for diffuse gliomas classification, which may be help for therapeutic decision making and selection of patient groups suitable for targeted genetic analysis. Goal(s): The purpose of this study is to develop an artificial intelligence method to reclassify adult-type diffuse gliomas based on the new WHO CNS tumor classification. Approach: An artificial intelligence decision tree diagnostic platform(DTDP) based on MRI and deep learning networks was developed by combined 6 individualized CNNs models in series and parallel Results: The DTDP performed well with accuracy of 86.67%. Impact: The DTDP achieved automatic classification and comprehensive diagnosis of adult‑type diffuse gliomas by combining genetic biomarkers and histological grading, and effectively helped neuroradiologists to reclassify adult-type diffuse gliomas.

An artificial intelligence decision tree diagnostic platform helps neuroradiologists reclassify adult-type diffuse gliomas