Inflammatory processes affecting the uvea result in a temporary o permanent blurred vision and represent an important cause of visual impairment worldwide. It is often hard to make a precise diagnosis which is dependent on the clinical expertise, diagnostic tests, laboratory investigations in blood and sometimes in the aqueous humor (AH). With the aim of obtaining proof of principle Fourier Transformed Infrared (FT-IR) absorbance spectroscopy was applied to study the molecular composition of 72 AH samples collected in 26 patients with uveitis and in 44 controls. The unsupervised exploration of the internal structure of the dataset by principal component analysis reduced hundreds IR variables to those most representative allowing to obtain the predictive model that distinguished the AH spectra of patients with uveitis from controls. The same result was obtained by unsupervised agglomerative cluster analysis. After labeling the spectra with some clinical information it was observed that most severe uveitis with active processes were grouped separately from chronic and relapsing uveitis and controls. The consistence of prediction models is discussed in the light of supporting etiological diagnosis by machine learning processes. In conclusion, proof of principle has been obtained that the IR spectral pattern of AH may reflect particular uveal diseases.
Keywords: Aqueous humor; FT-IR spectroscopy; Hierarchical clustering; Infrared pattern; Principal component analysis; Uveitis.
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