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This study aims to develop a new computational pathology approach thatautomates the identification and quantification of myocardial inflammatoryinfiltration in digital HE-stained images to provide a quantitativehistological diagnosis of myocarditis.898 HE-stained whole slide images (WSIs)of myocardium from 154 heart transplant patients diagnosed with myocarditis ordilated cardiomyopathy (DCM) were included in this study. An automated DL-basedcomputational pathology approach was developed to identify nuclei and detectmyocardial inflammatory infiltration, enabling the quantification of thelymphocyte nuclear density (LND) on myocardial WSIs. A cutoff value based onthe quantification of LND was proposed to determine if the myocardialinflammatory infiltration was present. The performance of our approach wasevaluated with a five-fold cross-validation experiment, tested with an internaltest set from the myocarditis group, and confirmed by an external test from adouble-blind trial group. An LND of 1.02/mm2 could distinguish WSIs withmyocarditis from those without. The accuracy, sensitivity, specificity, andarea under the receiver operating characteristic curve (AUC) in the five-foldcross-validation experiment were 0.899 plus or minus 0.035, 0.971 plus or minus0.017, 0.728 plus or minus 0.073 and 0.849 plus or minus 0.044, respectively.For the internal test set, the accuracy, sensitivity, specificity, and AUC were0.887, 0.971, 0.737, and 0.854, respectively. The accuracy, sensitivity,specificity, and AUC for the external test set reached 0.853, 0.846, 0.858, and0.852, respectively. Our new approach provides accurate and reliablequantification of the LND of myocardial WSIs, facilitating automatedquantitative diagnosis of myocarditis with HE-stained images.

Automated identification and quantification of myocardial inflammatory infiltration in digital histological images to diagnose myocarditis