Machine Learning Approaches to predict Intra-Uterine Insemination Success Rate- Application of Artificial Intelligence in Infertility

Introduction: Assisted Reproductive Technology (ART) has been widely utilized for infertility management. Despite its low success rate, Intra-Uterine Insemination (IUI) is one of the first alternatives and most important approaches regarding many cases of infertility treatment. Given the numerous influencing factors and limitations associated with time and resources, the development of a reliable model to predict the success rate of ART methods can significantly contribute to decision-making processes. Materials and methods: We reviewed the demographic, clinical, and laboratory data regarding 157 IUI treatment cycles among 124 women using their partner’s sperm from May2017 to June2019. Primary outcome measures were clinical pregnancy and live birth. Some prediction models were constructed and compared to the logistic regression analysis. Results: Woman’s mean age was 30.1 ± 5.2 years and the infertility had a female cause in 24.3% of the cases, male cause in 32.6% of cases, and combined causes in 32.6% of the cases. Concerning the first IUI cycle, the clinical pregnancy rate per cycle was 16.9% (N= 21). Data were prepared according to cross-industry standard process for data mining (CRISP-DM) methodology, and the following models were fitted to the data: J48 Decision Tree, Perceptron Multilayer (MLP) Neural Network, Support Vector Machine (SVM) with radial basis function (RBF) kernel, K-Nearest Neighbors (KNN) with one neighborhood, and Bayesian Network. J48 Decision Tree, with a sensitivity of 95% and specificity of 98%, had the most optimal performance, and the KNN model was the weakest one. Conclusion: To predict the results of IUI as a simple and less invasive therapy for infertile couples, some models were applied based on artificial intelligence and J48 Decision Tree was recommended

Semen parameters can be predicted from environmental factors and lifestyle using artificial intelligence methods

Fertility rates have dramatically decreased in the last two decades, especially in men. It has been described that environmental factors as well as life habits may affect semen quality. In this paper we use artificial intelligence techniques in order to predict semen characteristics resulting from environmental factors, life habits, and health status, with these techniques constituting a possible decision support system that can help in the study of male fertility potential. A total of 123 young, healthy volunteers provided a semen sample that was analyzed according to the World Health Organization 2010 criteria. They also were asked to complete a validated questionnaire about life habits and health status. Sperm concentration and percentage of motile sperm were related to sociodemographic data, environmental factors, health status, and life habits in order to determine the predictive accuracy of a multilayer perceptron network, a type of artificial neural network. In conclusion, we have developed an artificial neural network that can predict the results of the semen analysis based on the data collected by the questionnaire. The semen parameter that is best predicted using this methodology is the sperm concentration. Although the accuracy for motility is slightly lower than that for concentration, it is possible to predict it with a significant degree of accuracy. This methodology can be a useful tool in early diagnosis of patients with seminal disorders or in the selection of candidates to become semen donors.This study was partially funded by Vicerrectorado de Investigación, University of Alicante, Alicante, Spain (Vigrob-137)

Seleção de embriões pela análise de imagens: uma abordagem Deep Learning

Infertility affects about 186 million people worldwide and 9-10% of couples in Portugal, causing financial, social and medical problems. Evaluation of embryo quality based morphological features is the standard in vitro fertilization (IVF) clinics around the world. This process is subjective and time-consuming, and results in discrepant classifications among embryologists and clinics, leading to fail in predict accurately embryo implantation and live birth potential. Although assisted reproductive technologies (ART) such as IVF coupled with time lapse elimination of periodic transfer to microscopy assessment and stable embryo culture conditions for embryos development, has alleviated the infertility problem, there are significant limitations even considering morphokinetic analysis. Likewise, many patients require multiple IVF cycles to achieve pregnancy, making the selection of single embryo for transfer a critical challenge. Here, we demonstrate the reliability of machine learning, especially deep learning based on TensorFlow open source and Keras libraries for embryo raw TLI images features extraction and classification in clinical practice. Equally, we present a follow up pipeline for clinicians and researchers, with no expertise in machine learning, to easily, rapid and accurately utilize deep learning as a clinical decision support tool in embryos viability studies, as well in other medical field where the analysis of images is preeminentA infertilidade afeta cerca de 186 milhões de pessoas em todo o mundo e 9-10% dos casais em Portugal, causando problemas financeiros, sociais e de saúde. Constitui procedimento padrão a avaliação da qualidade dos embriões baseadas em características morfológicas. No entanto, tais avaliações são subjetivas e demoradas e resultam em classificações discrepantes entre embriologistas e clínicas causando problemas na avaliação do potencial do embrião. Embora as tecnologias de reprodução medicamente assistida, como a fertilização in vitro, acoplada à tecnologia time-lapse, tenham diminuído o problema da infertilidade, existem limitações significativas, mesmo considerando a análise morfocinética. Outrossim, muitas pacientes necessitam de múltiplos ciclos de fertilização para alcançar a gravidez, tornando a seleção do embrião com maior potencial de implantação e geração de nados vivos um desafio crítico. No presente projeto demonstramos a prova do conceito da confiabilidade de Machine Learning (aprendizagem automática), especialmente Deep Learning baseado em TensorFlow e Keras, para extrair e discriminar caraterísticas associadas ao potencial embrionário, em imagens time-lapse. Igualmente, apresentamos um pipeline para que clínicos e investigadores, sem experiência em Machine Learning, possam utilizar com facilidade, rapidez e precisão Deep Learning como ferramenta de apoio à decisão clínica em estudos de viabilidade de embriões, bem como noutras áreas médicas onde a análise de imagens seja proeminenteMestrado em Biologia Molecular e Celula

Surface and Contextual Linguistic Cues in Dialog Act Classification: A Cognitive Science View

What role do linguistic cues on a surface and contextual level have in identifying the intention behind an utterance? Drawing on the wealth of studies and corpora from the computational task of dialog act classification, we studied this question from a cognitive science perspective. We first reviewed the role of linguistic cues in dialog act classification studies that evaluated model performance on three of the most commonly used English dialog act corpora. Findings show that frequency‐based, machine learning, and deep learning methods all yield similar performance. Classification accuracies, moreover, generally do not explain which specific cues yield high performance. Using a cognitive science approach, in two analyses, we systematically investigated the role of cues in the surface structure of the utterance and cues of the surrounding context individually and combined. By comparing the explained variance, rather than the prediction accuracy of these cues in a logistic regression model, we found that (1) while surface and contextual linguistic cues can complement each other, surface linguistic cues form the backbone in human dialog act identification, (2) with word frequency statistics being particularly important for the dialog act, and (3) the similar trends across corpora, despite differences in the type of dialog, corpus setup, and dialog act tagset. The importance of surface linguistic cues in dialog act classification sheds light on how both computers and humans take advantage of these cues in speech act recognition

PSO based Neural Networks vs. Traditional Statistical Models for Seasonal Time Series Forecasting

Seasonality is a distinctive characteristic which is often observed in many practical time series. Artificial Neural Networks (ANNs) are a class of promising models for efficiently recognizing and forecasting seasonal patterns. In this paper, the Particle Swarm Optimization (PSO) approach is used to enhance the forecasting strengths of feedforward ANN (FANN) as well as Elman ANN (EANN) models for seasonal data. Three widely popular versions of the basic PSO algorithm, viz. Trelea-I, Trelea-II and Clerc-Type1 are considered here. The empirical analysis is conducted on three real-world seasonal time series. Results clearly show that each version of the PSO algorithm achieves notably better forecasting accuracies than the standard Backpropagation (BP) training method for both FANN and EANN models. The neural network forecasting results are also compared with those from the three traditional statistical models, viz. Seasonal Autoregressive Integrated Moving Average (SARIMA), Holt-Winters (HW) and Support Vector Machine (SVM). The comparison demonstrates that both PSO and BP based neural networks outperform SARIMA, HW and SVM models for all three time series datasets. The forecasting performances of ANNs are further improved through combining the outputs from the three PSO based models.Comment: 4 figures, 4 tables, 31 references, conference proceeding

Method based on data mining techniques for breast cancer recurrence analysis

Cancer is a constantly evolving disease, which affects a large number of people worldwide. Great efforts have been made at the research level for the development of tools based on data mining techniques that allow to detect or prevent breast cancer. The large volumes of data play a fundamental role according to the literature consulted, a great variety of dataset oriented to the analysis of the disease has been generated, in this research the Breast Cancer dataset was used, the purpose of the proposed research is to submit comparison of the J48 and randomforest, NaiveBayes and NaiveBayes Simple, SMO Poli-kernel and SMO RBF-Kernel classification algorithms, integrated with the Simple K-Means cluster algorithm for the generation of a model that allows the successful classification of patients who are or Non-recurring breast cancer after having previously undergone surgery for the treatment of said disease, finally the methods that obtained the best levels were SMO Poly-Kernel + Simple K-Means 98.5% of Precision, 98.5% recall, 98.5% TPRATE and 0.2% FPRATE. The results obtained suggest the possibility of using intelligent computational tools based on data mining methods for the detection of breast cancer recurrence in patients who had previously undergone surgery

Simplifying credit scoring rules using LVQ+PSO

One of the key elements in the banking industry rely on the appropriate selection of customers. In order to manage credit risk, banks dedicate special efforts in order to classify customers according to their risk. The usual decision making process consists in gathering personal and financial information about the borrower. Processing this information can be time consuming, and presents some difficulties due to the heterogeneous structure of data. We offer in this paper an alternative method that is able to classify customers' profiles from numerical and nominal attributes. The key feature of our method, called LVQ+PSO, is the finding of a reduced set of classifying rules. This is possible, due to the combination of a competitive neural network with an optimization technique. These rules constitute a predictive model for credit risk approval. The reduced quantity of rules makes this method not only useful for credit officers aiming to make quick decisions about granting a credit, but also could act as borrower's self selection. Our method was applied to an actual database of a credit consumer financial institution in Ecuador. We obtain very satisfactory results. Future research lines are exposed