Diagnostic performance of deep learning-assisted [18F]FDG PET imaging for Alzheimer's disease: a systematic review and meta-analysis.
Purpose: This study aims to calculate the diagnostic performance of deep learning (DL)-assisted 18F-fluorodeoxyglucose ([18F]FDG) PET imaging in Alzheimer's disease (AD). Methods: The Ovid MEDLINE, Ovid Embase, Web of Science Core Collection, Cochrane, and IEEE Xplore databases were searched for rel...
| Publicado en: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 52; no. 10; pp. 3600 - 3613 |
|---|---|
| Autores principales: | , , , , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Aug2025
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187119033&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187119033 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16197070 NPC jtl: European Journal of Nuclear Medicine & Molecular Imaging issn: 16197070 maglogo: N pubinfo: dt: Aug2025 vid: 52 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187119033 184079827 10.1007/s00259-025-07228-9 187119033 ppf: 3600 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Diagnostic performance of deep learning-assisted [18F]FDG PET imaging for Alzheimer's disease: a systematic review and meta-analysis. aug: au: Sun, Yuan Chen, Yuhan Dong, La Hu, Daoyan Zhang, Xiaohui Jin, Chentao Zhou, Rui Zhang, Jucheng Dou, Xiaofeng Wang, Jing Xue, Le Xiao, Meiling Zhong, Yan Tian, Mei Zhang, Hong affil: https://ror.org/059cjpv64 Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, 310009, Hangzhou, Zhejiang, China sug: ab: Purpose: This study aims to calculate the diagnostic performance of deep learning (DL)-assisted 18F-fluorodeoxyglucose ([18F]FDG) PET imaging in Alzheimer's disease (AD). Methods: The Ovid MEDLINE, Ovid Embase, Web of Science Core Collection, Cochrane, and IEEE Xplore databases were searched for related studies from inception to May 24, 2024. We included original studies that developed a DL algorithm for [18F]FDG PET imaging to assess diagnostic performance in classifying AD, mild cognitive impairment (MCI), and normal control (NC). A bivariate random-effects model was employed to assess the area under the curve (AUC). Results: We identified 36 studies that met the inclusion criteria. Of these, 35 studies distinguished AD from NC, with a pooled AUC of 0.98 (95% CI: 0.96–0.99). Thirteen studies distinguished AD from MCI, with a pooled AUC of 0.95 (95% CI: 0.92–0.96). Nineteen studies distinguished MCI from NC, with a pooled AUC of 0.94 (95% CI: 0.91–0.95). Additionally, we found large amounts of heterogeneity across studies which could be partially attributed to variations in DL methods and imaging modalities. Conclusion: This systematic review and meta-analysis shows that DL-assisted [18F]FDG PET imaging has high diagnostic performance in identifying AD. The significant heterogeneity among studies underscores the necessity for future research to incorporate external validation, utilize large sample size, and adhere to rigorous guideline to provide robust support for clinical decision-making. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|