Parlatan, UgurParlatan, SeymaSen, KubraKecoglu, IbrahimUlukan, Mustafa OzerKarakaya, AtalayErkanli, KorhanTurkoglu, HalilUgurlucan, MuratUnlu, Mehmet BurcinTanoren, Bukem2023-02-212023-02-212022-01-0110.1038/s41598-022-10380-zhttps://hdl.handle.net/11443/2344http://dx.doi.org/10.1038/s41598-022-10380-zAtrial fibrillation (AF) is diagnosed with the electrocardiogram, which is the gold standard in clinics. However, sufficient arrhythmia monitoring takes a long time, and many of the tests are made in only a few seconds, which can lead arrhythmia to be missed. Here, we propose a combined method to detect the effects of AF on atrial tissue. We characterize tissues obtained from patients with or without AF by scanning acoustic microscopy (SAM) and by Raman spectroscopy (RS) to construct a mechano-chemical profile. We classify the Raman spectral measurements of the tissue samples with an unsupervised clustering method, k-means and compare their chemical properties. Besides, we utilize scanning acoustic microscopy to compare and determine differences in acoustic impedance maps of the groups. We compared the clinical outcomes with our findings using a neural network classification for Raman measurements and ANOVA for SAM measurements. Consequently, we show that the stiffness profiles of the tissues, corresponding to the patients with chronic AF, without AF or who experienced postoperative AF, are in agreement with the lipid-collagen profiles obtained by the Raman spectral characterization.ArticleWOS:000783915800041