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Visual interpretation of [18F]Florbetaben PET supported by deep learning-based estimation of amyloid burden
Ji-Young Kim, Dongkyu Oh, Kiyoung Sung, Hongyoon Choi, Jin Chul Paeng, Gi Jeong Cheon, Keon Wook Kang, Dong Young Lee et al.. European journal of nuclear medicine and molecular imaging, 2020-09-29
Topic: Plasma and spinal-fluid biomarkers
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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APA 7th
Kim, J.-Y., Oh, D., Sung, K., Choi, H., Paeng, J. C., Cheon, G. J., et al. (2021). Visual interpretation of [18F]Florbetaben PET supported by deep learning-based estimation of amyloid burden. European journal of nuclear medicine and molecular imaging. https://doi.org/10.1007/s00259-020-05044-x
Vancouver
Kim J.-Y, Oh D, Sung K, Choi H, Paeng JC, Cheon GJ, et al. Visual interpretation of [18F]Florbetaben PET supported by deep learning-based estimation of amyloid burden. European journal of nuclear medicine and molecular imaging. 2021. doi:10.1007/s00259-020-05044-x. PMID: 32990807.
BibTeX
@article{kim2021-32990807,
author = {Kim, Ji-Young and Oh, Dongkyu and Sung, Kiyoung and Choi, Hongyoon and Paeng, Jin Chul and Cheon, Gi Jeong and Kang, Keon Wook and Lee, Dong Young and others},
title = {{Visual interpretation of [18F]Florbetaben PET supported by deep learning-based estimation of amyloid burden}},
journal = {European journal of nuclear medicine and molecular imaging},
year = {2021},
doi = {10.1007/s00259-020-05044-x},
pmid = {32990807},
url = {https://doi.org/10.1007/s00259-020-05044-x}
}
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Key findings
- 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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Study details
- Participants121
- Evidence strengthHigh
How this study scored 57/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record, multi-centre collaboration and free to read, so those were left out: it earned 32 of the 56 points we could judge it on, which is 57 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (European journal of nuclear medicine and molecular imaging) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 5 / 5 | Rated “high” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 3 / 5 | 2021 — within 6 year(s) of the newest study here |
| Sample size | 2 / 4 | 121 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 32990807) |
| Free to read | — / 2 | Access not recorded — left out of this score |
| Points | 32 / 56 | Scored on 10 of 20 parts. We have no data for the rest, so they are left out: 32 of the 56 points we could judge it on is 57 out of 100. |
| Total | 57 / 100 | — |
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