Diagnostic performance of deep learning-assisted [F]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 F-fluorodeoxyglucose ([F]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 [F]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 [F]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.