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Experimental results on a collection of microscope images of oocytes are reported to show the effectiveness of the proposed approach. This approach is made by fuzzy clustering. Finally, the extracted features are used to cluster oocytes according to different levels of granularity. To this aim, we evaluate some statistics in the Haar wavelet transform domain. In the second phase, regions that result from segmentation are processed through a multiresolution texture analysis to extract a set of features that describe different levels of cytoplasm granularity. In the segmentation phase, a region of interest inside the cytoplasm is extracted through morphological operators and the Hough transform. The proposed approach includes three main phases: 1) segmentation 2) feature extraction and 3) clustering. The purpose of this paper is to develop a diagnostic tool that can analyze light microscope images of human oocytes and derive a description of the oocyte cytoplasm that is useful for quality assessment in assisted insemination. The purpose of this commentary is to highlight the evolution and impact AI has had in other fields relevant to the fertility sector and describe a vision for applications within ART that could improve outcomes, reduce costs, and positively impact clinical care. Thoughtful scrutiny is essential lest we find ourselves in a position of trying to modulate and modify after entry of these tools into our clinics and patient care. We owe it to ourselves to begin to examine these AI-driven analytics and develop a very clear idea about where we can and should go before we roll these tools into clinical care.
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No prospective studies have shown clear cut benefit or cost reductions over current practices, but we are very early in the process of developing and evaluating these tools. To date, the impact of AI on ART outcomes is inconclusive.
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In their fully integrated format, these tools will be part of a digital fertility ecosystem of analytics embedded within an EMR. We can look forward to a time when AI will be the third part of a provider’s tool box to complement expertise and medical literature to enable ever more accurate predictions and outcomes in ART. These tools have the potential to improve outcomes and transition decision-making from one based on traditional provider centric assessments toward a hybrid triad of expertise, evidence, and algorithmic data analytics using AI. Online tools to integrate artificial intelligence into the decision-making process across all aspects of ART are rapidly emerging. Decision-making in fertility care is on the cusp of a significant frameshift.