AUTHOR=Pantic Igor V. , Cumic Jelena , Valjarevic Svetlana , Shakeel Adeeba , Wang Xinyu , Vurivi Hema , Daoud Sayel , Chan Vincent , Petroianu Georg A. , Shibru Meklit G. , Ali Zehara M. , Nesic Dejan , Salih Ahmed E. , Butt Haider , Corridon Peter R. TITLE=Computational approaches for evaluating morphological changes in the corneal stroma associated with decellularization JOURNAL=Frontiers in Bioengineering and Biotechnology VOLUME=11 YEAR=2023 URL=https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2023.1105377 DOI=10.3389/fbioe.2023.1105377 ISSN=2296-4185 ABSTRACT=

Decellularized corneas offer a promising and sustainable source of replacement grafts, mimicking native tissue and reducing the risk of immune rejection post-transplantation. Despite great success in achieving acellular scaffolds, little consensus exists regarding the quality of the decellularized extracellular matrix. Metrics used to evaluate extracellular matrix performance are study-specific, subjective, and semi-quantitative. Thus, this work focused on developing a computational method to examine the effectiveness of corneal decellularization. We combined conventional semi-quantitative histological assessments and automated scaffold evaluations based on textual image analyses to assess decellularization efficiency. Our study highlights that it is possible to develop contemporary machine learning (ML) models based on random forests and support vector machine algorithms, which can identify regions of interest in acellularized corneal stromal tissue with relatively high accuracy. These results provide a platform for developing machine learning biosensing systems for evaluating subtle morphological changes in decellularized scaffolds, which are crucial for assessing their functionality.