Oocyte quality is a critical determinant of success in assisted reproductive technologies (ART), including in vitro fertilization (IVF), embryo development, and implantation. Current approaches to evaluating oocyte quality rely on morphological characteristics observed under a microscope, such as cytoplasmic texture, zona pellucida thickness, polar body appearance, and overall shape and size [1,2]. Although these morphological markers are widely used to assess maturity and developmental potential, they are limited by inter-embryologist variability, absence of standardized grading systems, and poor reproducibility [3,4]. To address these limitations, alternative indicators—such as meiotic spindle integrity, mitochondrial activity, cumulus-oocyte complex (COC) density, maternal RNA and protein content, apoptosis in surrounding cells, and metabolic enzyme activity—have been investigated to better characterize oocyte competence [5-7]. However, the clinical application of these markers is constrained by long processing times and the need for specialized techniques. Establishing reliable and standardized criteria for oocyte quality assessment is therefore essential, given the profound influence of oocyte quality on pregnancy outcomes.

Recent advances in imaging technology and artificial intelligence (AI) have enabled more objective and non-invasive assessments of oocyte quality [8,9]. Morphological features such as cytoplasmic granularity, zona pellucida integrity, and polar body structure remain central to evaluation, and several AI-based strategies have targeted specific oocyte regions. Targosz et al. [10] used deep neural networks to segment human oocytes into cytoplasm, polar bodies, and the perivitelline space, laying the groundwork for quantitative morphology analysis, although clinical outcomes remain to be validated. Barucic et al. [11] classified oocytes into viable and non-viable groups based on segmented components, but the study was limited by a small sample size and subjectively defined viability. Fjeldstad et al. [4] developed machine learning algorithms that predicted blastocyst development directly from oocyte images, without segmentation. Despite these advances, concerns remain about the black-box nature of AI and its lack of interpretability. More recently, AI approaches have been applied in ART for donor oocyte quality assessment, demonstrating improved prediction of blastocyst formation and suggesting potential for treatment personalization [12]. Likewise, AI-driven analyzes of oocytes during IVF have introduced new perspectives on objective morphology evaluation [13]. A recent systematic review further synthesized evidence on AI applications in ART, reinforcing its expanding role in improving reproductive outcomes [14]. Collectively, these findings highlight the potential of AI to establish standardized and interpretable methods for objective oocyte assessment.

AI-driven clustering techniques support biomedical research by enabling the systematic classification and analysis of complex biological data. In medical imaging, AI has been used to analyze heterogeneous healthcare data and aid in disease diagnosis [15]. Radiomics, a quantitative imaging approach, extracts features related to intensity, shape, and texture from biological or medical images. These features capture subtle patterns that may not be apparent to human observers and can be leveraged to identify phenotypic differences or predict clinical outcomes. In oncology, radiomics has been applied to extract imaging biomarkers to assess tumor heterogeneity, contributing to outcome prediction and personalized treatment strategies [16]. When clustering is performed on radiomics-derived features, the resulting groups provide quantitatively validated diagnostic insights [17,18]. Such outcomes are instrumental in advancing personalized medicine by offering objective and reproducible assessments of disease characteristics, thereby improving both precision and diagnostic accuracy.

A key component of oocyte quality assessment is cytoplasmic evaluation, as cytoplasmic characteristics directly influence early embryonic development. Previous studies have linked features such as granularity, coloration, and organelle clustering to oocyte competence and developmental potential [19-21]. However, quantifying cytoplasmic properties remains challenging due to subjectivity and the absence of standardized evaluation tools. Standardized AI-based approaches could address these limitations by providing reproducible and consistent metrics for assessing cytoplasmic quality.

To the best of our knowledge, this is the first study to associate cytoplasmic characteristics with blastocyst development using AI. We performed a comparative analysis of blastocyst development rates by cluster, based on quantitative features extracted from mouse oocyte cytoplasm. The primary aim was to establish an objective and quantitative method for assessing oocytes by classifying them according to cytoplasmic radiomic features correlated with blastocyst formation using AI-driven analyzes.

This study is the first to demonstrate the relationship between cytoplasmic morphological and intensity features of oocytes and blastocyst development, providing a novel framework for assessing oocyte quality. These findings may help identify cytoplasmic characteristics that can guide individualized treatment strategies to improve clinical outcomes.

This study demonstrates that, in addition to conventional morphological assessment [30-35], cytoplasmic intensity features are essential for predicting blastocyst formation, potentially enhancing the current methodologies used in ART. Incorporating radiomics analysis into ART offers a quantitative and objective method for evaluating oocyte quality, complementing traditional morphology-based evaluations and enhancing predictive accuracy [36-38].

The high variability in intensity observed in cluster 1 oocytes may reflect cytoskeletal dysfunction, which disrupts organelle distribution and reduces developmental potential [39]. Prior studies indicate that uniform cytoplasmic structure is crucial for proper organelle positioning, whereas irregularities in granularity and cytoplasmic texture suggest impaired maturation and reduced embryonic viability [40-42]. In line with this, increased cytoplasmic granularity, uneven brightness, and organelle clustering have been associated with reduced oocyte quality and developmental competence [39,43,44]. Previous ultrastructural studies identified refractile bodies—electron-dense inclusions containing fibrous material, granular vesicles, and lipid deposits—as contributors to increased cytoplasmic granularity and uneven brightness [39]. These structures are linked to oxidative stress, cellular aging, and cytoskeletal dysfunction, which lead to abnormal clustering of organelles, particularly mitochondria and smooth endoplasmic reticulum. Such clustering may impair metabolic activity and reduce developmental potential. Cytoplasmic abnormalities of this type have also been associated with mitochondrial dysfunction and elevated reactive oxygen species, which disrupt adenosine triphosphate production and meiotic spindle organization [45], ultimately compromising oocyte quality and early embryonic development [46]. Reviews emphasize the critical role of mitochondrial processes in oocyte maturation and embryo competence [47] and highlight how oxidative imbalance accelerates reproductive aging [48]. Together, mitochondrial and oxidative stress pathways compromise the structural and functional integrity of the cytoplasm, destabilize the cytoskeleton, and undermine fertilization potential.

In contrast, cluster 2 contained the most spherical and compact oocytes, features that may promote intracellular stability and efficient metabolic activity. The distinct separation of cluster 2 in PCA visualization suggests that this group represents optimal cytoplasmic and morphological characteristics, consistent with superior developmental potential.

The structural irregularities distinguishing clusters 2 and 3 may indicate mechanical or cytoskeletal instability that affects fertilization and cleavage. These findings suggest that cytoplasmic uniformity alone is insufficient for optimal embryonic development if structural abnormalities are present. Cluster 2 demonstrated superior developmental potential compared with cluster 3, underscoring the combined importance of morphological symmetry, high brightness, and low-intensity variability as indicators of optimal oocyte quality in ART.

Our subgroup analysis further revealed that blastocyst formation rates in cluster 2 did not differ significantly between young and aged oocytes, suggesting that some aged oocytes retain cytoplasmic and morphological properties similar to younger oocytes. This supports the concept of heterogeneity in oocyte aging, where not all aged oocytes experience equivalent functional decline. Conversely, cluster 1 oocytes from older mice exhibited the lowest blastocyst rates, reinforcing the link between cytoplasmic instability, structural abnormalities, and developmental failure. In cluster 3, aged oocytes also showed lower blastocyst rates than young oocytes, indicating that although the general characteristics of this cluster were favorable, structural instability had a disproportionately negative impact on aged oocytes, similar to cluster 1.

The significant overrepresentation of aged oocytes in cluster 1 suggests that aging is strongly associated with morphological irregularities and cytoplasmic heterogeneity. This trend was also evident in the ratios of aged to young oocytes across clusters. Because cluster 1 was predominantly composed of aged oocytes, these findings further reinforce the impact of maternal age on oocyte morphology and cytoplasmic characteristics. Nevertheless, the presence of some young oocytes in cluster 1 indicates that age alone does not determine developmental competence; instead, a combination of structural and cytoplasmic factors plays a critical role. Moreover, aged oocytes consistently showed reduced developmental potential, even when their morphology appeared similar to that of younger oocytes. This is consistent with prior studies reporting diminished developmental potential in aged oocytes [49]. Together, these observations suggest that oocyte quality should be assessed not solely by age, but through an integrated evaluation of morphological and cytoplasmic parameters.

Although our findings underscore the importance of cytoplasmic features in predicting oocyte competence, prior studies have primarily relied on age and conventional morphological markers, often neglecting pre-fertilization cytoplasmic properties. Time-lapse imaging and machine learning have been applied to distinguish embryos derived from young versus aged oocytes; however, these efforts have focused on post-fertilization morphokinetics rather than pre-fertilization cytoplasmic characteristics [50]. Our study addresses this gap by directly analyzing cytoplasmic morphology and intensity features, thereby providing a quantitative and predictive framework for oocyte quality assessment. Previous research on oocyte maturation kinetics found no significant differences between young and aged oocytes [51]. In contrast, the present findings demonstrate that clustering based on cytoplasmic morphology and intensity correlates strongly with blastocyst formation rates, offering greater predictive value than age alone. This highlights the central role of cytoplasmic and morphological features in determining oocyte developmental potential. While maternal age remains an established factor in the deterioration of oocyte quality, some aged oocytes retain high competence if they possess favorable cytoplasmic characteristics. Identifying such oocytes in advance is crucial, as interventions for enhancing blastocyst formation may differ according to developmental capacity.

This study introduces a novel framework for identifying oocytes with high developmental potential, which may enhance oocyte selection, improve pregnancy rates, and reduce the emotional and financial burden of infertility treatment. The incorporation of data-driven approaches into subjective evaluations can improve the consistency, reliability, and overall success of ART procedures [3,9]. Furthermore, analyzing the morphological and intensity features of high-quality oocytes provides valuable insight into intrinsic and extrinsic factors influencing oocyte quality, including maternal age, ovarian stimulation protocols, and culture conditions [2].

Compared with other AI-based approaches such as deep neural networks or traditional machine learning models, the radiomics–GMM framework offers distinct advantages. Radiomics generates handcrafted, interpretable features (e.g., shape, intensity, texture) that can be directly linked to biological or clinical attributes. The GMM provides unsupervised clustering without requiring large annotated datasets, making it especially suitable for exploratory studies with limited sample sizes. By contrast, deep learning models automatically learn hierarchical representations from raw images and may achieve superior predictive performance when applied to large, diverse datasets. Traditional supervised machine learning methods, including support vector machines or random forests, can also provide robust classification but depend on predefined labels. Thus, while the radiomics–GMM approach offers interpretability and feasibility in smaller datasets, its predictive power and generalizability may be more limited compared with data-driven deep learning methods. Future research should explore hybrid strategies that integrate radiomics-derived features with advanced AI models, thereby combining interpretability with enhanced predictive performance.

This study has several limitations. First, the experiments were performed using mouse oocytes; thus, the direct applicability of our findings to human oocytes remains to be validated. Whether the cytoplasmic morphology and intensity-based classification system established in mice demonstrates similar correlations with blastocyst development in humans is still unclear. Second, this study was conducted at a single institution under specific experimental conditions. Therefore, multicenter studies and meta-analyzes are required to confirm the reproducibility and generalizability of these results. Future research should incorporate larger, clinically relevant datasets across multiple laboratories and diverse patient populations to further validate this classification approach. Third, there was an imbalance in cluster sizes, particularly the relatively small number of oocytes in cluster 1 compared with clusters 2 and 3. This uneven distribution may affect the robustness of statistical comparisons, and thus, findings related to cluster 1 should be interpreted with caution. In this study, Student’s t-tests were used to compare characteristics among clusters that had already been defined by the GMM. The aim was not to use individual features as criteria for classification, but rather to determine whether predefined clusters exhibited distinct feature distributions. Because only pairwise comparisons were conducted, the number of statistical tests was limited, and analyzes avoided excessive multiple testing. Nonetheless, even a modest number of comparisons increases the risk of type I error, and this potential limitation should be considered when interpreting the results. Fourth, radiomic features are sensitive to image acquisition conditions, including equipment, imaging protocols, and resolution. Because our dataset was obtained from a single center, there remains a potential risk of bias from site-specific imaging parameters. Future studies using multicenter datasets with standardized imaging protocols will be essential to establish reproducibility and ensure generalizability.

In conclusion, this is the first study to quantitatively analyze oocyte cytoplasmic morphology and intensity features and to demonstrate their association with blastocyst formation. Unlike prior studies that primarily emphasized general morphological traits, our approach integrated both structural and intensity-based cytoplasmic parameters. These findings suggest that cytoplasmic features can serve as valuable predictors of developmental potential. Future studies should validate these results in human oocytes and further investigate the biological mechanisms influencing cytoplasmic morphology and intensity, with the ultimate goal of improving IVF success rates.