A novel machine-learning framework based on early embryo morphokinetics identifies a feature signature associated with blastocyst development.

BACKGROUND: Artificial Intelligence entails the application of computer algorithms to the huge and heterogeneous amount of morphodynamic data produced by Time-Lapse Technology. In this context, Machine Learning (ML) methods were developed in order to assist embryologists with automatized and objective predictive models able to standardize human embryo assessment. In this study, we aimed at developing a novel ML-based strategy to identify relevant patterns associated with the prediction of blastocyst development stage on day 5. METHODS: We retrospectively analysed the morphokinetics of 575 embryos obtained from 80 women who underwent IVF at our Unit. Embryo morphokinetics was registered using the Geri plus® time-lapse system. Overall, 30 clinical, morphological and morphokinetic variables related to women and embryos were recorded and combined. Some embryos reached the expanded blastocyst stage on day 5 (BL Group, n = 210), some others did not (nBL Group, n = 365). RESULTS: The novel EmbryoMLSelection framework was developed following four-steps: Feature Selection, Rules Extraction, Rules Selection and Rules Evaluation. Six rules composed by a combination of 8 variables were finally selected, and provided a predictive power described by an AUC of 0.84 and an accuracy of 81%. CONCLUSIONS: We provided herein a new feature-signature able to identify with an high performance embryos with the best developmental competence to reach the expanded blastocyst stage on day 5. Clear and clinically relevant cut-offs were identified for each considered variable, providing an objective tool for early embryo developmental assessment.

Bilateral posterior maxillary segmental osteotomy to rehabilitate edentulous mandibular area: case report.

The purpose of this work was to describe a clinical case with reduced vertical height in both the posterior sectors, due to maxillary dento-alveolar extrusion in mandibular edentulous space, as a result of some extractions which have not been promptly replaced by a prosthetic rehabilitation, eventually resolved with a bilateral posterior segmental maxillary osteotomy (PMSO). Our surgical technique was practised under general anesthesia according to Kufner's version of Schuchardt's original description. In the light of the present outcomes, in severe clinical cases of dento-alveolar extrusion, the PMSO can be considered the optimal solution, because of the quality and the stability of the final result, the short therapeutic times, the limited morbidity and the modest compliance asked to the patient.