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New research · Ophthalmology
Scientific reports · 16h
AI / informaticsScientific reports · 2025

Non-invasive detection of choroidal melanoma via tear-derived protein corona on gold nanoparticles: a machine learning approach.

Hakimeh Rakhshandeh, Ahmad Nasiraei, Hamid Riazi-Esfahani … Rassoul Dinarvand
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OphthalmologyAI / informatics

Deep learning model detected choroidal melanoma from tear samples with near-perfect accuracy.

Non-invasive detection of choroidal melanoma via tear-derived protein corona on gold nanoparticles: a machine learning approach.

Hakimeh Rakhshandeh … Rassoul Dinarvand
Scientific reports · 2025
Purpose

This study investigates the feasibility of using tear sample analysis, based on protein corona formation on gold nanoparticles combined with electrospray ionization mass spectrometry (electrospray ionization mass spectrometry) and machine learning techniques, as a non-invasive approach for the detection of choroidal melanoma.

ROC AUC 0.997
Results
ROC AUC 0.997
correctly told cancer patients from healthy people almost every single time
n = 18 samples
More results

Additionally, Continuous Wavelet Transform (Continuous Wavelet Transform) with Mexican hat wavelet was applied to convert mass spectrometry data into 128 × 128 RGB images for deep learning analysis.

More results

While m/z parameters showed moderate differences that didn't reach statistical significance (p = 0.082), entropy-based features demonstrated strong discriminative power.

Among traditional machine learning models, Random Forest achieved the highest accuracy (0.959 ± 0.003) and ROC AUC (0.993 ± 0.000) with remarkable computational efficiency (3.90 s per fold).

“
Conclusion

While both traditional machine learning and deep learning approaches achieved exceptional performance, each offers distinct advantages in terms of computational efficiency and feature extraction capabilities.

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