Non-Perturbative Identification and Subtyping of Amyloidosis in Human Kidney Tissue with Raman Spectroscopy and Machine Learning

Jeong Hee Kim, Chi Zhang, Christopher John Sperati, Serena Maria Bagnasco, Ishan Barman

Biosensors 2023 article cited by 13

Source Open-access PDF More in Medicine & Health

importance 4/10 ai assisted Spectroscopy Techniques in Biomedical and Chemical Research renal pathology stage: research

Problem

Identifying and subtyping amyloidosis (e.g., immunoglobulin light chain vs amyloid A) in kidney tissue normally requires special stains, immunolabeling, electron microscopy, or mass spectrometry.

Why it matters

Correct amyloidosis typing determines clinical treatment, so faster label-free methods matter.

What the AI did

Raman spectroscopy data from unstained frozen kidney tissue was analyzed with t-SNE and DBSCAN to classify amyloid types, compared against a normal biopsy control.

Results

Raman data 'accurately classified' by amyloid type; no numeric accuracy stated

Impact

Demonstrated label-free Raman spectroscopy plus ML can distinguish amyloidosis types in biopsies.

Record

Venue	Biosensors
Published	2023
Work type	article
Authors	Jeong Hee Kim, Chi Zhang, Christopher John Sperati, Serena Maria Bagnasco, Ishan Barman
Citations	13 (OpenAlex, at import time)
References	48
Model or method	t-SNE; DBSCAN
Organization	not stated in source
Primary source	https://doi.org/10.3390/bios13040466
Open access	gold
Keywords	Raman spectroscopy, amyloidosis, t-SNE, DBSCAN, kidney biopsy

Importance rating

4 / 10

real but narrower advance

Correct amyloid typing guides treatment, but abstract only demonstrates a first ex vivo feasibility study on biopsies; no scale stated.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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