Semi-quantitative scoring criteria based on multiple staining methods combined with machine learning to evaluate residual nuclei in decellularized matrix.

Abstract

The detection of residual nuclei in decellularized extracellular matrix (dECM) biomaterials is critical for ensuring their quality and biocompatibility. However, current evaluation methods have limitations in addressing impurity interference and providing intelligent analysis. In this study, we utilized four staining techniques-hematoxylin-eosin staining, acetocarmine staining, the Feulgen reaction and 4',6-diamidino-2-phenylindole staining-to detect residual nuclei in dECM biomaterials. Each staining method was quantitatively evaluated across multiple parameters, including area, perimeter and grayscale values, to establish a semi-quantitative scoring system for residual nuclei. These quantitative data were further employed as learning indicators in machine learning models designed to automatically identify residual nuclei. The experimental results demonstrated that no single staining method alone could accurately differentiate between nuclei and impurities. In this study, a semi-quantitative scoring table was developed. With this table, the accuracy of determining whether a single suspicious point is a cell nucleus has reached over 98%. By combining four staining methods, false positives caused by impurity contamination were eliminated. The automatic recognition model trained based on nuclear parameter features reached the optimal index of the model after several iterations of training in 172 epochs. The trained artificial intelligence model achieved a recognition accuracy of over 90% for detecting residual nuclei. The use of multidimensional parameters, integrated with machine learning, significantly improved the accuracy of identifying nuclear residues in dECM slices. This approach provides a more reliable and objective method for evaluating dECM biomaterials, while also increasing detection efficiency.