Visual interpretation of [18F]Florbetaben PET supported by deep learning–based estimation of amyloid burden



Amyloid PET which has been widely used for noninvasive assessment of cortical amyloid burden is visually interpreted in the clinical setting. As a fast and easy-to-use visual interpretation support system, we analyze whether the deep learning–based end-to-end estimation of amyloid burden improves inter-reader agreement as well as the confidence of the visual reading.


A total of 121 clinical routines [18F]Florbetaben PET images were collected for the randomized blind-reader study. The amyloid PET images were visually interpreted by three experts independently blind to other information. The readers qualitatively interpreted images without quantification at the first reading session. After more than 2-week interval, the readers additionally interpreted images with the quantification results provided by the deep learning system. The qualitative assessment was based on a 3-point BAPL score (1: no amyloid load, 2: minor amyloid load, and 3: significant amyloid load). The confidence score for each session was evaluated by a 3-point score (0: ambiguous, 1: probably, and 2: definite to decide).


Inter-reader agreements for the visual reading based on a 3-point scale (BAPL score) calculated by Fleiss kappa coefficients were 0.46 and 0.76 for the visual reading without and with the deep learning system, respectively. For the two reading sessions, the confidence score of visual reading was improved at the visual reading session with the output (1.27 ± 0.078 for visual reading-only session vs. 1.66 ± 0.63 for a visual reading session with the deep learning system).


Our results highlight the impact of deep learning–based one-step amyloid burden estimation system on inter-reader agreement and confidence of reading when applied to clinical routine amyloid PET reading.

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Data availability

Data that support this study can be made available upon reasonable request.


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This research was financially supported by the National Research Foundation of Korea Grant funded by the Korea Government (No. NRF-2019K1A3A1A14065446 and NRF-2019R1F1A1061412).

Author information




H.C. designed the study. J.Y.K. performed image analysis. D.Y.L and K.S. contributed to collect and analyze clinical data. J.Y.K., D.O., and H.C. contributed to the image interpretation. J.Y.K. and D.O. contributed to the data collection and literature review. J.C.P., G.J.C., K.W.K., and D.S.L. contributed to data interpretation and analysis. J.Y.K. and H.C. wrote the manuscript mainly and all authors critically reviewed the manuscript.

Corresponding author

Correspondence to Hongyoon Choi.

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All procedures performed in studies involving human participants were following the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent to clinical testing and neuroimaging approved by the institutional review boards of participating institutions (SNUH IRB Registration Number 2004-047-1116).

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Kim, JY., Oh, D., Sung, K. et al. Visual interpretation of [18F]Florbetaben PET supported by deep learning–based estimation of amyloid burden. Eur J Nucl Med Mol Imaging 48, 1116–1123 (2021).

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  • Alzheimer’s disease
  • Amyloid PET
  • [18F]Florbetaben
  • PET
  • Visual quantification
  • Deep learning