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BACKGROUND: The most common Assisted Reproductive Technology is In-Vitro Fertilization (IVF). During IVF, embryologists commonly perform a morphological assessment to evaluate embryo quality and choose the best embryo for transferring to the uterus. However, embryo selection through morphological assessment is subjective, so various embryologists obtain different conclusions. Furthermore, humans can consider only a limited number of visual parameters resulting in a poor IVF success rate. Artificial intelligence (AI) for embryo selection is objective and can include many parameters, leading to better IVF outcomes. OBJECTIVES: This study sought to use AI to (1) predict pregnancy results based on embryo images, (2) assess using more than one image of the embryo in the prediction of pregnancy but based on the current process in IVF labs, and (3) compare results of AI-Based methods and embryologist experts in predicting pregnancy. METHODS: A data set including 252 Time-lapse Videos of embryos related to IVF performed between 2017 and 2020 was collected. Frames related to 19 ± 1, 43 ± 1, and 67 ± 1 h post-insemination were extracted. Well-Known CNN architectures with transfer learning have been applied to these images. The results have been compared with an algorithm that only uses the final image of embryos. Furthermore, the results have been compared with five experienced embryologists. RESULTS: To predict the pregnancy outcome, we applied five well-known CNN architectures (AlexNet, ResNet18, ResNet34, Inception V3, and DenseNet121). DeepEmbryo, using three images, predicts pregnancy better than the algorithm that only uses one final image. It also can predict pregnancy better than all embryologists. Different well-known architectures can successfully predict pregnancy chances with up to 75.0% accuracy using Transfer Learning. CONCLUSION: We have developed DeepEmbryo, an AI-based tool that uses three static images to predict pregnancy. Additionally, DeepEmbryo uses imag

The objective was to investigate the embryo morphokinitics using a time-lapse monitoring (TLM) system and assessment of clinical outcomes following intracytoplasmic sperm injection (ICSI) with zona pellucida (ZP)-bound sperm selection and conventional methods. A total of 371 metaphase II (MII) oocytes from 50 ICSI cycles were studied. Sibling oocytes were randomly divided into control (n = 199) and ZP-bound group (n = 172). All resulting zygotes were cultured and monitored in the TLM system up to Day 3 after ICSI. Fertilization rate, early embryo development, and clinical outcomes were evaluated. No significant differences were found in fertilization rate, time-lapse qualitative and quantitative measures, pronuclear fading time (PNF) t2, t3, t4, t5, t6, and t7 (times of cleavage to 2, 3, 4, 5, 6, and 7 cells), respectively. However, the t8 (time of cleavage to eight cells) and cc3 (duration of third cell cycle) revealed a significant difference between control and ZP-bound groups (p < .05). A significant difference between the two groups (p < .05) in the rates of Grade A embryos (according to Basile algorithm), chemical pregnancy, clinical pregnancy, and implantation was observed. Sperm selection using biological materials, such as ZP, improved both embryo quality and pregnancy outcomes, despite not affecting the early embryo development and morphokinetic parameters up to t8. This prospective randomized sibling oocyte trial was registered in October 2020 to January 2022 (IRCT20200705048021N1).

Distinguishing differences between areas represented with point cloud data is generally approached by choosing a optimal viewpoint. The most informative view of a scene ultimately enables to have the optimal coverage over distinct points both locally and globally while accounting for the distance to the foci of attention. Measures of surface saliency, related to curvature inconsistency, extenuate differences in shape and are coupled with viewpoint selection approaches. As there is no analytical solution for optimal viewpoint selection, candidate viewpoints are generally discretely sampled and evaluated for information and require (near) exhaustive combinatorial searches. We present a consolidated optimization framework for optimal viewpoint selection with a continuous cost function and analytically derived Jacobian that incorporates view angle, vertex normals and measures of task related surface information relative to viewpoint. We provide a mechanism in the cost function to incorporate sensor attributes such as operating range, field of view and angular resolution. The framework is evaluated as competing favorably with the state-of-the-art approaches to viewpoint selection while significantly reducing the number of viewpoints to be evaluated in the process.

© 2017 IEEE. This paper presents a node-level self-organizing interference avoidance scheme (SIAC) between multiple coexisting wireless body area networks (WBANs) that incorporates self-organization and smart spectrum allocation. It follows a biologically inspired approach based on the theory of pulse-coupled oscillators for self-organization. The proposed scheme makes three major contributions as compared to the current literature. Firstly, it considers node-level interference for internetwork interference mitigation rather than considering each WBAN as a whole. Secondly, it allocates synchronous and parallel transmission intervals for interference avoidance in an optimal manner and dynamically adapts to changes in their coexistence. Finally, it achieves collision-free, self-organized communication with only information of the firing signal of each WBAN and does not require a global coordinator to manage its communications. It operates on a nodes traffic priority, signal strength, and density of sensors in a WBAN. Simulation results show that our proposal achieves a fast convergence time despite the little information it receives. Moreover, SIAC is shown to be robust to variations in signal strength, number of coexisting WBANs and number of sensor nodes within each WBAN.

3d reconstruction of deformable objects in dynamic scenes forms the fundamental basis of many robotic applications. Existing mesh-based approaches compromise registration accuracy, and lose important details due to interpolation and smoothing. Additionally, existing non-rigid registration techniques struggle with unindexed points and disconnected manifolds. We propose a novel non-rigid registration framework for raw, unstructured, deformable point clouds purely based on geometric features. The global non-rigid deformation of an object is formulated as an aggregation of locally rigid transformations. The concept of locality is embodied in soft patches described by geometrical properties based on SHOT descriptor and its neighborhood. By considering the confidence score of pairwise association between soft patches of two scans (not necessarily consecutive), a computed similarity matrix serves as the seed to grow a correspondence graph which leverages rigidity terms defined in As-Rigid-As-Possible for pruning and optimization. Experiments on simulated and publicly available datasets demonstrate the capability of the proposed approach to cope with large deformations blended with numerous missing parts in the scan process.