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We generated 105 spectroscopic scans, which comprised 4 NIR and 3 RS scans per sample to generate a \u201cmultimodal spectroscopic scan\u201d (MSS) for each heart, done across 15 patients, 5 each from the dilated cardiomyopathy (DCM), Ischaemic Heart Disease (IHD) and Normal pathologies. Each of the MSS scans was undertaken in 3\u00a0s. Data were entered into machine learning (ML) algorithms to assess accuracy of MSS in diagnosing tissue type. The median age was 50\u00a0years (IQR 49\u201352) for IHD, 47 (IQR 45\u201350) for DCM and 36 (IQR 33\u201352) for healthy patients (p<\/jats:italic>\u2009=\u20090.35), 60% of which were male. MSS identified key differences in IHD, DCM and normal heart samples in regions typically associated with fibrosis and collagen (NIR wavenumbers: 1433, 1509, 1581, 1689 and 1725\u00a0nm; RS wavelengths: 1658, 1450 and 1330\u00a0cm\u22121<\/jats:sup>). In principal component (PC) analyses, these differences explained 99.2% of the variation in 4 PCs for NIR, 81.6% in 10 PCs for Raman, and 99.0% in 26 PCs for multimodal spectroscopic signatures. Using a stack machine learning algorithm with combined NIR and Raman data, our model had a precision of 96.9%, recall of 96.6%, specificity of 98.2% and Area Under Curve (AUC) of 0.989 (Table 1). NIR and Raman modalities alone had similar levels of precision at 94.4% and 89.8% respectively (Table 1). MSS combined with ML showed accuracy of 90% for detecting dilated cardiomyopathy, 100% for ischaemic heart disease and 100% for diagnosing healthy tissue. Multimodal spectroscopic signatures, based on NIR and Raman spectroscopy, could provide cardiac tissue scans in 3-s to aid accurate diagnoses of fibrosis in IHD, DCM and normal hearts.Table 1<\/jats:label>\n Machine learning performance metrics for validation data sets of (a) Near-Infrared (NIR), (b) Raman and (c and d) multimodal data using logistic regression (LR), stochastic gradient descent (SGD) and support vector machines (SVM), with combined \u201cstack\u201d (LR\u2009+\u2009SGD\u2009+\u2009SVM)<\/jats:p>\n <\/jats:caption>\n \n \n AUC<\/jats:th>\n Precision<\/jats:th>\n Recall<\/jats:th>\n Specificity<\/jats:th>\n <\/jats:tr>\n <\/jats:thead>\n \n (a) NIR model<\/jats:td>\n <\/jats:tr>\n \n \u00a0Logistic regression<\/jats:td>\n 0.980<\/jats:td>\n 0.944<\/jats:td>\n 0.933<\/jats:td>\n 0.967<\/jats:td>\n <\/jats:tr>\n \n \u00a0SGD<\/jats:td>\n 0.550<\/jats:td>\n 0.281<\/jats:td>\n 0.400<\/jats:td>\n 0.700<\/jats:td>\n <\/jats:tr>\n \n \u00a0SVM<\/jats:td>\n 0.840<\/jats:td>\n 0.806<\/jats:td>\n 0.800<\/jats:td>\n 0.900<\/jats:td>\n <\/jats:tr>\n \n \u00a0Stack<\/jats:td>\n 0.933<\/jats:bold><\/jats:td>\n 0.794<\/jats:bold><\/jats:td>\n 0.800<\/jats:bold><\/jats:td>\n 0.900<\/jats:bold><\/jats:td>\n <\/jats:tr>\n \n (b) Raman model<\/jats:td>\n <\/jats:tr>\n \n \u00a0Logistic regression<\/jats:td>\n 0.985<\/jats:td>\n 0.940<\/jats:td>\n 0.929<\/jats:td>\n 0.960<\/jats:td>\n <\/jats:tr>\n \n \u00a0SGD<\/jats:td>\n 0.892<\/jats:td>\n 0.869<\/jats:td>\n 0.857<\/jats:td>\n 0.932<\/jats:td>\n <\/jats:tr>\n \n \u00a0SVM<\/jats:td>\n 0.992<\/jats:td>\n 0.940<\/jats:td>\n 0.929<\/jats:td>\n 0.960<\/jats:td>\n <\/jats:tr>\n \n \u00a0Stack<\/jats:td>\n 0.954<\/jats:bold><\/jats:td>\n 0.869<\/jats:bold><\/jats:td>\n 0.857<\/jats:bold><\/jats:td>\n 0.932<\/jats:bold><\/jats:td>\n <\/jats:tr>\n \n (c) MSS: multimodal (NIR\u2009+\u2009Raman) to detect DCM vs. IHD vs. normal patients<\/jats:td>\n <\/jats:tr>\n \n \u00a0Logistic regression<\/jats:td>\n 0.975<\/jats:td>\n 0.841<\/jats:td>\n 0.828<\/jats:td>\n 0.917<\/jats:td>\n <\/jats:tr>\n \n \u00a0SGD<\/jats:td>\n 0.847<\/jats:td>\n 0.803<\/jats:td>\n 0.793<\/jats:td>\n 0.899<\/jats:td>\n <\/jats:tr>\n \n \u00a0SVM<\/jats:td>\n 0.971<\/jats:td>\n 0.853<\/jats:td>\n 0.828<\/jats:td>\n 0.917<\/jats:td>\n <\/jats:tr>\n \n \u00a0Stack<\/jats:td>\n 0.961<\/jats:bold><\/jats:td>\n 0.853<\/jats:bold><\/jats:td>\n 0.828<\/jats:bold><\/jats:td>\n 0.917<\/jats:bold><\/jats:td>\n <\/jats:tr>\n \n (d) MSS: multimodal (NIR\u2009+\u2009Raman) to detect pathological vs. normal patients<\/jats:td>\n <\/jats:tr>\n \n \u00a0Logistic regression<\/jats:td>\n 0.961<\/jats:td>\n 0.969<\/jats:td>\n 0.966<\/jats:td>\n 0.984<\/jats:td>\n <\/jats:tr>\n \n \u00a0SGD<\/jats:td>\n 0.944<\/jats:td>\n 0.967<\/jats:td>\n 0.966<\/jats:td>\n 0.923<\/jats:td>\n <\/jats:tr>\n \n \u00a0SVM<\/jats:td>\n 1.000<\/jats:td>\n 1.000<\/jats:td>\n 1.000<\/jats:td>\n 1.000<\/jats:td>\n <\/jats:tr>\n \n \u00a0Stack<\/jats:td>\n 1.000<\/jats:bold><\/jats:td>\n 0.944<\/jats:bold><\/jats:td>\n 0.931<\/jats:bold><\/jats:td>\n 0.969<\/jats:bold><\/jats:td>\n <\/jats:tr>\n <\/jats:tbody><\/jats:table>\n Bold values indicate values obtained from the stack algorithm and used for analyses<\/jats:p>\n <\/jats:table-wrap-foot><\/jats:table-wrap><\/jats:p>","DOI":"10.1007\/s00380-023-02292-3","type":"journal-article","created":{"date-parts":[[2023,8,22]],"date-time":"2023-08-22T17:02:47Z","timestamp":1692723767000},"page":"1476-1485","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Towards a point-of-care multimodal spectroscopy instrument for the evaluation of human cardiac tissue"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5008-4113","authenticated-orcid":false,"given":"Varun 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