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Fast AI Based Amyloid PET Reporting – can Nuclear Medicine Handle the Expected Workload in Alzheimers Diagnostics?
Benedikt Schemmer, Markus Essler. Nuklearmedizin - NuclearMedicine, 2025-03-01
Topic: Amyloid, tau and blood-test diagnosis
The deep-learning network was capable of learning the task of delineating pathologic and normal findings within minutes on a standard PC and performing the classification task with 100% accuracy in less than 1 millisecond per case. Schlussfolgerungen/ Given the extremely fast training, the low cost to setup and maintain AI is very easy to deploy in clinical practice. Reports are finished almost instantly as the patient leaves the scanner. This doesn’t replace a trained professional, but can filter out clear cut cases that don’t need in-depth analysis. We can easily take our key role as gatekeeper for therapy of Alzheimer’s and other neurodegenerative diseases with a little help from AI and modern PET scanners. The AI filters out easy cases and frees time for those cases that require expertise. Publication History Article published online: 12 March 2025 © 2025. Thieme. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Schemmer, B., & Essler, M. (2025). Fast AI Based Amyloid PET Reporting – can Nuclear Medicine Handle the Expected Workload in Alzheimers Diagnostics? Nuklearmedizin - NuclearMedicine. https://doi.org/10.1055/s-0045-1804387
Vancouver
Schemmer B, Essler M. Fast AI Based Amyloid PET Reporting – can Nuclear Medicine Handle the Expected Workload in Alzheimers Diagnostics? Nuklearmedizin - NuclearMedicine. 2025. doi:10.1055/s-0045-1804387.
BibTeX
@article{schemmer2025-2096c5f9,
author = {Schemmer, Benedikt and Essler, Markus},
title = {{Fast AI Based Amyloid PET Reporting – can Nuclear Medicine Handle the Expected Workload in Alzheimers Diagnostics?}},
journal = {Nuklearmedizin - NuclearMedicine},
year = {2025},
doi = {10.1055/s-0045-1804387},
url = {https://doi.org/10.1055/s-0045-1804387}
}
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
Key findings
- The deep-learning network was capable of learning the task of delineating pathologic and normal findings within minutes on a standard PC and performing the classification task with 100% accuracy in less than 1 millisecond per case.
- Schlussfolgerungen/ Given the extremely fast training, the low cost to setup and maintain AI is very easy to deploy in clinical practice.
- Reports are finished almost instantly as the patient leaves the scanner.
- This doesn’t replace a trained professional, but can filter out clear cut cases that don’t need in-depth analysis.
- We can easily take our key role as gatekeeper for therapy of Alzheimer’s and other neurodegenerative diseases with a little help from AI and modern PET scanners.
Why it matters
A deep-learning network was able to classify amyloid PET findings with 100% accuracy in under a millisecond per case after very fast training on a standard PC. The authors say this does not replace a trained professional but can filter out clear-cut cases and free up expert time, which could help nuclear medicine handle the expected workload in Alzheimer's diagnostics.
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Study details
- Participants20
- Evidence strengthLow
How this study scored 30/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Nuklearmedizin - NuclearMedicine) |
| Journal's citation record | 0 / 4 | Nuklearmedizin - NuclearMedicine: journal h-index 46 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 1 / 4 | 20 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 | 0 / 7 | Cited 0 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.0 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 0 / 6 | Cited 0.0× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 0 / 4 | Cited 0 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Markus Essler: h-index 56 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | University Hospital Bonn: institution h-index 533 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 30 / 100 | — |
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