Objective: To describe novel embryo features capable of predicting implantation potential as input data for an artificial neural network (ANN) model.
Design: Retrospective cohort study.
Setting: University-affiliated private IVF center.
Patient(s): This study included 637 patients from the oocyte donation program who underwent single-blastocyst transfer during two consecutive years.
Intervention(s): None.
Main Outcome Measure(s): The research was divided into two phases. Phase 1 consisted of the description and analysis of the following embryo features in implanted and nonimplanted embryos: distance and speed of pronuclear migration, blastocyst expanded diameter, inner cell mass area, and trophectoderm cell cycle length. Phase 2 consisted of the development of an ANN algorithm for implantation prediction. Results were obtained for four models fed with different input data. The predictive power was measured with the use of the area under the receiver operating characteristic curve (AUC).
Result(s): Out of the five novel described parameters, blastocyst expanded diameter and trophectoderm cell cycle length had statistically different values in implanted and nonimplanted embryos. After the ANN models were trained and validated using fivefold cross-validation, they were capable of predicting implantation on testing data with AUCs of 0.64 for ANN1 (conventional morphokinetics), 0.73 for ANN2 (novel morphodynamics), 0.77 for ANN3 (conventional morphokinetics + novel morphodynamics), and 0.68 for ANN4 (discriminatory variables from statistical test).
Conclusion(s): The novel proposed embryo features affect the implantation potential, and their combination with conventional morphokinetic parameters is effective as input data for a predictive model based on artificial intelligence.
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http://dx.doi.org/10.1016/j.fertnstert.2020.08.023 | DOI Listing |
Interdiscip Sci
December 2024
School of Computer Science and Artificial Intelligence, Aliyun School of Big Data, School of Software, Changzhou University, Changzhou, 213164, China.
Cell-Penetrating Peptides (CPPs) are a crucial carrier for drug delivery. Since the process of synthesizing new CPPs in the laboratory is both time- and resource-consuming, computational methods to predict potential CPPs can be used to find CPPs to enhance the development of CPPs in therapy. In this study, EnDM-CPP is proposed, which combines machine learning algorithms (SVM and CatBoost) with convolutional neural networks (CNN and TextCNN).
View Article and Find Full Text PDFMed Phys
December 2024
Jiangsu Key Laboratory for Biomaterials and Devices, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China.
Background: Medical imaging plays a pivotal role in the real-time monitoring of patients during the diagnostic and therapeutic processes. However, in clinical scenarios, the acquisition of multi-modal imaging protocols is often impeded by a number of factors, including time and economic costs, the cooperation willingness of patients, imaging quality, and even safety concerns.
Purpose: We proposed a learning-based medical image synthesis method to simplify the acquisition of multi-contrast MRI.
Med Phys
December 2024
Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Assam, India.
Background: Measurement noise often leads to inaccurate shear wave phase velocity estimation in ultrasound shear wave elastography. Filtering techniques are commonly used for denoising the shear wavefields. However, these filters are often not sufficient, especially in fatty tissues where the signal-to-noise ratio (SNR) can be very low.
View Article and Find Full Text PDFInt J Speech Lang Pathol
December 2024
Faculty of Rehabilitation Medicine, College of Health Sciences, University of Alberta, Edmonton, Canada.
Purpose: Assessment allows speech-language pathologists to identify clients' strengths and needs while laying the foundation for the therapeutic relationship. However, the extent to which parents' experiences with assessment has been explored in the literature is unclear. The purposes of this review were to: a) Identify and summarise the available literature on parents' experiences with speech-language assessment for their preschool-aged children, and b) identify gaps in the literature.
View Article and Find Full Text PDFUtilizing data from the Vitamin C, Thiamine, and Steroids in Sepsis (VICTAS) Trial, this hub model was developed to limit seventeen Renin-Angiotensin-Aldosterone System (RAAS) components as three entrance and four exits, to facilitate the calculation of a model as one egress unknown, the angiotensin type 1 (AT1) receptor. Following previous evidence relating renin levels to mortality, this study found controls were more like sepsis patients with levels < renin quartile 1 (Q1) for calculated AT1, while more like sepsis patients with renin levels > quartile 3 (Q4) for measured aldosterone levels. Additionally differential discrete correlate summation (DCS) analysis indicates AT1, aldosterone and renin as major hub nodes in this independent DCS model of metabolic data inputs.
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