Distinguishing Alzheimer’s Pathologies with Machine Learning-Aided Quantum Sensing
We present a machine learning-aided quantum sensing approach for distinguishing Alzheimer's pathologies. Using a versatile entangled photon source, we analyzed brain samples of different mouse models, achieving high classification accuracy validated through confocal microscopy.
Authors
- Maria Eleni Karakatsani (ORCID: https://orcid.org/0000-0002-8368-1360)
- M. V. Jabir
- Ruiqing Ni (ORCID: https://orcid.org/0000-0002-0793-2113)
- Lukas Glandorf (ORCID: https://orcid.org/0000-0003-4968-2906)
- Daniel Razansky (ORCID: https://orcid.org/0000-0001-8676-0964)
- Michael Reiss (ORCID: https://orcid.org/0000-0003-3583-3451)
- Shruti Sundar
Publication Details
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1364/cleo_at.2025.aa104_1
- Primary Topic
- Biofield Effects and Biophysics
- Type
- article
- Field-Weighted Citation Impact
- 0.00