Abstract: „Reliable assessment of embryo viability remains a critical yet challenging component of assisted reproductive technologies (ART). Traditional methods, including morphological grading and preimplantation genetic testing for aneuploidy (PGT-A), are either subjective, invasive, or resource intensive. Consequently, non-invasive approaches, including analysis of spent culture medium (SCM) and digital image-based analytical methods, emerged as promising alternatives to support clinical decision-making, though their reliability varies. SCM analyses can detect embryonic DNA and metabolites, but their outcomes are constrained by potential contamination with non-embryonic material and inconsistent amplification efficiency. In contrast, image-based assessment combined with machine learning (ML) enables quantitative, repeatable, and transparent evaluation of embryo architecture. Segmental image analysis of grayscale bitmaps allows for extraction of interpretable phototextural metrics, which have been correlated with developmental competence and ploidy status of embryos. Recent applications of ML-assisted techniques demonstrate comparable or superior predictive performance relative to PGT-A and expert embryologists, highlighting their potential as cost-effective and ethically favorable alternatives. Continued refinement of image acquisition minimizing artifacts, optimizing quality and resolution, and focusing on key regions such as the trophectoderm and inner cell mass, may further enhance their diagnostic sensitivity and specificity. Overall, computerized assessment of embryo microphotographs represents a scalable, non-invasive strategy that can complement or even partially replace invasive testing. By leveraging structured, interpretable image data, algorithms such as r-Algo 2.0 (discriminative image-analysis software) exemplify the emerging role of computational tools in advancing diagnostic precision and objectivity in ART. Highlights: Current methods of embryo viability assessment lack clinical reliability Non-invasive image analysis supports accurate embryo quality assessment Transparent algorithms enhance repeatability of image-analysis techniques Image-based embryo assessments may complement or reduce the need for PGT-A Imaging refinements can further enhance its diagnostic sensitivity and specificity Summary Sentence: Computerized image analysis and machine learning offer a non-invasive, scalable, and reliable alternative to currently used embryo assessment methods, with comparable or improved predictive performance, enhancing objectivity and clinical decisionmaking in ART.”(…)