Comparing performance between clinics of an embryo evaluation algorithm based on time-lapse images and machine learning.

PURPOSE: This article aims to assess how differences in maternal age distributions between IVF clinics affect the performance of an artificial intelligence model for embryo viability prediction and proposes a method to account for such differences. METHODS: Using retrospectively collected data from 4805 fresh and frozen single blastocyst transfers of embryos incubated for 5 to 6 days, the discriminative performance was assessed based on fetal heartbeat outcomes. The data was collected from 4 clinics, and the discrimination was measured in terms of the area under ROC curves (AUC) for each clinic. To account for the different age distributions between clinics, a method for age-standardizing the AUCs was developed in which the clinic-specific AUCs were standardized using weights for each embryo according to the relative frequency of the maternal age in the relevant clinic compared to the age distribution in a common reference population. RESULTS: There was substantial variation in the clinic-specific AUCs with estimates ranging from 0.58 to 0.69 before standardization. The age-standardization of the AUCs reduced the between-clinic variance by 16%. Most notably, three of the clinics had quite similar AUCs after standardization, while the last clinic had a markedly lower AUC both with and without standardization. CONCLUSION: The method of using age-standardization of the AUCs that is proposed in this article mitigates some of the variability between clinics. This enables a comparison of clinic-specific AUCs where the difference in age distributions is accounted for.

Differential Expression Levels of Integrin α6 Enable the Selective Identification and Isolation of Atrial and Ventricular Cardiomyocytes.

RATIONALE: Central questions such as cardiomyocyte subtype emergence during cardiogenesis or the availability of cardiomyocyte subtypes for cell replacement therapy require selective identification and purification of atrial and ventricular cardiomyocytes. However, current methodologies do not allow for a transgene-free selective isolation of atrial or ventricular cardiomyocytes due to the lack of subtype specific cell surface markers. METHODS AND RESULTS: In order to develop cell surface marker-based isolation procedures for cardiomyocyte subtypes, we performed an antibody-based screening on embryonic mouse hearts. Our data indicate that atrial and ventricular cardiomyocytes are characterized by differential expression of integrin α6 (ITGA6) throughout development and in the adult heart. We discovered that the expression level of this surface marker correlates with the intracellular subtype-specific expression of MLC-2a and MLC-2v on the single cell level and thereby enables the discrimination of cardiomyocyte subtypes by flow cytometry. Based on the differential expression of ITGA6 in atria and ventricles during cardiogenesis, we developed purification protocols for atrial and ventricular cardiomyocytes from mouse hearts. Atrial and ventricular identities of sorted cells were confirmed by expression profiling and patch clamp analysis. CONCLUSION: Here, we introduce a non-genetic, antibody-based approach to specifically isolate highly pure and viable atrial and ventricular cardiomyocytes from mouse hearts of various developmental stages. This will facilitate in-depth characterization of the individual cellular subsets and support translational research applications.