Flaws (and quality) in research today: can artificial intelligence intervene?

The existing flaws in both conducting and reporting of research have been outlined and criticized in the past. Weak research design, poor methodology, lack of fresh ideas and poor reporting are the main points to blame. Issues have been continually raised on the types of results published, review process, sponsorship, notion, ethics, and incentives in publishing, the role of regulatory agencies and stakeholders, the role of funding, and the cooperation between funders and academic institutions and the training of both clinicians and methodologists or statisticians. As a result, there is loss of the utmost goal: the production of robust research to form recommendations to support pragmatic decision in a real-world context. We propose the construction of a model based on artificial intelligence that could assist stakeholders, clinicians, and patients to guide conducting the best quality of research. We briefly describe the levels of the workflow, including the input and output data collection, the feature extraction/selection, the architecture, and parameterization of the model, along with its training, operation, and refinement. © 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group

Special characteristics, reproductive, and clinical profile of women with unexplained infertility versus other causes of infertility: a comparative study

Purpose: To investigate whether women with unexplained infertility (UI) demonstrate different demographic and IVF characteristics compared to those with other causes of infertility. Methods: Data on 245 couples that underwent a total of 413 IVF/ICSI cycles were analyzed (UI 114 cycles, 73 women; anovulation (PCO/PCOS) 83 cycles, 51 women; tubal factor 85 cycles, 47 women; male factor 131 cycles, 74 women). Features of UI were compared versus other infertility groups, after adjustment for multiple comparisons. Generalized least squares (GLS) and random-effects logistic regression analysis were also performed. Results: Live birth rates, consisting of the primary outcome, were similar in all compared infertility groups. Compared to male infertility, UI was associated with woman’s older age at cycle, lower body mass index (BMI), and higher follicle-stimulating hormone (FSH). Compared to tubal infertility, UI was linked to lower endometrial thickness at oocyte retrieval and lower BMI; compared to anovulatory infertility related to PCO/PCOS, UI was linked to woman’s older age, more frequent smoking, and poorer ovarian reserve tests (FSH and antral follicle count). After adjustment for other types of infertility, woman’s age, age at menarche, and FSH levels, anovulatory infertility presented with higher odds of clinical pregnancy compared to UI (adjusted OR = 2.13, 95% C: 1.01–4.52). Conclusions: Infertile women with UI undergoing assisted reproduction demonstrate different demographic and clinical characteristics compared to those of other causes of infertility, albeit live birth rates are similar. © 2020, Springer Science+Business Media, LLC, part of Springer Nature

Mobile apps for helping patient-users: Is it still far-fetched?

Emergence of health-related smartphone applications and their wide dissemination in public as well as healthcare practitioners has undergone criticism under the scope of public health. Still, despite methodological issues curbing the initial enthusiasm, availability, safety and, in certain cases, documented efficacy of these measures has secured regulatory approval. Bearing in mind these pitfalls, we describe the necessary steps towards implementation of deep learning techniques in the specific clinical context of women's health and infertility in particular. © 2019 by the authors

Mobile platforms supporting health professionals: Need, technical requirements, and applications

Mobile computing is beginning defining the future of healthcare. The vast majority of mHealth applications are related to fitness, training and self-monitoring; limited applications are targeting physicians and doctor-patient interactions. However this can change. In this chapter the background of applications related to medical imaging and clinical and laboratory medicine is analyzed. A technological framework supporting mHealth applications in an agnostic manner is also introduced. Within this framework there are implemented two application examples, one application (ImaginX) supporting a health ecosystem (hospitals, radiologists, clinicians, patients) for medical image management. The second application (HPVGuard) supports a divergent but cooperating environment of laboratory and clinical doctors and patients involved in cervical cancer prevention and control. The two applications are analyzed and issues related to user acceptance and future directions are presented. mHealth has the potential to shape health future not by just translating existing applications but by inspiring new ideas. © 2016 by IGI Global. All rights reserved

Artificial intelligence in IVF: A need

Predicting the outcome of in-vitro fertilization (IVF) treatment is an extremely semantic issue in reproductive medicine. Discrepancies in results among reproductive centres still exist making the construction of new systems capable to foresee the desired outcome a necessity. As such, artificial neural networks (ANNs) represent a combination of a learning, self-adapting, and predicting machine. In this review hypothesis paper we summarize the past efforts of the ANNs systems to predict IVF outcomes. This will be considered together with other statistical models, such as the ensemble techniques, Classification And Regression Tree (CART) and regression analysis techniques, discriminant analysis, and case based reasoning systems. We also summarize the various inputs that have been employed as parameters in these studies to predict the IVF outcome. Finally, we report our attempt to construct a new ANN architecture based on the Learning Vector Quantizer promising good generalization: a system filled by a complete data set of our IVF unit, formulated parameters most commonly used in similar studies, trained by a network expert, and evaluated in terms of predictive power. © 2011 Informa Healthcare USA, Inc