Early prediction of Alzheimer’s disease using artificial intelligence and cortical features on T1WI sequences
The early occurrence of AD could be accurately predicted using our morphology-network-clinical model and the multi-predictor nomogram. This could help doctors make early and personalized treatment decisions in clinical practice, which showed important clinical significance.
Authors
- Rao Song (ORCID: https://orcid.org/0000-0002-7909-3048)
- Xiaojia Wu (ORCID: https://orcid.org/0000-0001-6682-9426)
- Beisheng Yang
- Faqi Wu
- Dajing Guo (ORCID: https://orcid.org/0000-0001-8655-6621)
- Huan Liu (ORCID: https://orcid.org/0000-0002-6700-962X)
- Xia Wang (ORCID: https://orcid.org/0000-0002-6791-4072)
- Qingqing Zheng
- Rong Zeng (ORCID: https://orcid.org/0009-0006-1183-5304)
- Lin Tang (ORCID: https://orcid.org/0000-0001-6511-1607)
Institutions
- Chongqing University (CN)
- Dalian Medical University (CN)
- Xian Central Hospital (CN)
- Second Affiliated Hospital of Chongqing Medical University (CN)
- United Imaging Healthcare (China) (CN)
- Chongqing Cancer Hospital (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2025-03-12
- DOI
- https://doi.org/10.3389/fneur.2025.1552940
- Primary Topic
- Dementia and Cognitive Impairment Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00