464 research outputs found

    motilitAI: a machine learning framework for automatic prediction of human sperm motility

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    In this article, human semen samples from the Visem dataset are automatically assessed with machine learning methods for their quality with respect to sperm motility. Several regression models are trained to automatically predict the percentage (0–100) of progressive, non-progressive, and immotile spermatozoa. The videos are adopted for unsupervised tracking and two different feature extraction methods—in particular custom movement statistics and displacement features. We train multiple neural networks and support vector regression models on the extracted features. Best results are achieved using a linear Support Vector Regressor with an aggregated and quantized representation of individual displacement features of each sperm cell. Compared to the best submission of the Medico Multimedia for Medicine challenge, which used the same dataset and splits, the mean absolute error (MAE) could be reduced from 8.83 to 7.31. We provide the source code for our experiments on GitHub (Code available at: https://github.com/EIHW/motilitAI)

    VISEM-Tracking, a human spermatozoa tracking dataset

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    A manual assessment of sperm motility requires microscopy observation, which is challenging due to the fast-moving spermatozoa in the field of view. To obtain correct results, manual evaluation requires extensive training. Therefore, computer-assisted sperm analysis (CASA) has become increasingly used in clinics. Despite this, more data is needed to train supervised machine learning approaches in order to improve accuracy and reliability in the assessment of sperm motility and kinematics. In this regard, we provide a dataset called VISEM-Tracking with 20 video recordings of 30 seconds (comprising 29,196 frames) of wet sperm preparations with manually annotated bounding-box coordinates and a set of sperm characteristics analyzed by experts in the domain. In addition to the annotated data, we provide unlabeled video clips for easy-to-use access and analysis of the data via methods such as self- or unsupervised learning. As part of this paper, we present baseline sperm detection performances using the YOLOv5 deep learning (DL) model trained on the VISEM-Tracking dataset. As a result, we show that the dataset can be used to train complex DL models to analyze spermatozoa

    A review of different deep learning techniques for sperm fertility prediction

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    Sperm morphology analysis (SMA) is a significant factor in diagnosing male infertility. Therefore, healthy sperm detection is of great significance in this process. However, the traditional manual microscopic sperm detection methods have the disadvantages of a long detection cycle, low detection accuracy in large orders, and very complex fertility prediction. Therefore, it is meaningful to apply computer image analysis technology to the field of fertility prediction. Computer image analysis can give high precision and high efficiency in detecting sperm cells. In this article, first, we analyze the existing sperm detection techniques in chronological order, from traditional image processing and machine learning to deep learning methods in segmentation and classification. Then, we analyze and summarize these existing methods and introduce some potential methods, including visual transformers. Finally, the future development direction and challenges of sperm cell detection are discussed. We have summarized 44 related technical papers from 2012 to the present. This review will help researchers have a more comprehensive understanding of the development process, research status, and future trends in the field of fertility prediction and provide a reference for researchers in other fields

    Artificial intelligence in andrology: From Semen Analysis to Image Diagnostics

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    Artificial intelligence (AI) in medicine has gained a lot of momentum in the last decades and has been applied to various fields of medicine. Advances in computer science, medical informatics, robotics, and the need for personalized medicine have facilitated the role of AI in modern healthcare. Similarly, as in other fields, AI applications, such as machine learning, artificial neural networks, and deep learning, have shown great potential in andrology and reproductive medicine. AI-based tools are poised to become valuable assets with abilities to support and aid in diagnosing and treating male infertility, and in improving the accuracy of patient care. These automated, AI-based predictions may offer consistency and efficiency in terms of time and cost in infertility research and clinical management. In andrology and reproductive medicine, AI has been used for objective sperm, oocyte, and embryo selection, prediction of surgical outcomes, cost-effective assessment, development of robotic surgery, and clinical decision-making systems. In the future, better integration and implementation of AI into medicine will undoubtedly lead to pioneering evidence-based breakthroughs and the reshaping of andrology and reproductive medicine

    The Ecology and Evolution of Human Reproductive Behavior

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    The complexity of human reproductive behavior has necessitated its examination through a variety of scientific disciplines, each focusing on specific elements of our biology, behavior, and society. However, this complexity also necessitates that we reintegrate the information learned from each discipline into a single framework, one rooted in the evolutionary principles that have shaped the development of all life on earth. In this dissertation, I use this framework to explore human reproductive behavior, with a particular focus on sexual coercion and fertility-mediated sexual behavior. In Chapter 1, I introduce the approach taken in this document, identify several key limitations, and outline the general structure. In Chapter 2, I conduct a comprehensive and interdisciplinary review that includes the fundamentals of sexual conflict and reproductive strategies; the evolution of human reproductive characteristics in response to socio-cognitive demands; the aspects of human sociality expected to influence reproductive behavior; the identified trends in human mating behavior; the proposed pressures behind concealed ovulation in primates; the essentials of the menstrual cycle; and the existing evidence for behavioral fertility in humans. In Chapter 3, I use a game-theory model to investigate the emergence of sexually coercive behavior across a variety of species, including humans, in which male coercion is a non-developmentally-determined reproductive strategy to identify several ecological and behavioral characteristics that predict the emergence of coercive behavior generally consistent with observed trends. In Chapter 4, I use face-trait research to investigate the degree to which women recognize and discriminate between images of men with personality traits associated with different male reproductive strategies as well as how these preferences might be mediated by her relationship and fertility status. In Chapter 5, explore the intersection of fertility, fertility belief, and sexuality, specifically testing the hypothesis that a woman’s sexual interest shifts in response to her fertility while taking into consideration her beliefs regarding her fertility. Finally, in Chapter 6, I review the primary take-home messages of this work and recommend that future research take these into consideration as they move forward. By taking an interdisciplinary approach rooted in evolutionary biology, this work reveals the need for an understanding of human reproductive behavior that incorporates a wider view of reproductive ecology. In doing so, we can gain a more accurate, comprehensive, and nuanced understanding of human reproductive behavior

    South Dakota Farm and Home Research

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    Director\u27s comments: Spirit and dedication to South Dakota [p] 1Ag Communications: Bridge between research and South Dakotans [p] 2Agricultural Engineering: Impact on present and future agriculture [p] 3Animal and Range Sciences: A reputation for excellence; a commitment to South Dakotans [p] 4Biology/Microbiology: Partnerships speed progress of fundamental research [p] 5Chemistry/Biochemistry: Increasing agricultural productivity while preserving the environment [p] 6Dairy Science: Benefits for students, producers, consumers, cows themselves [p] 7Economics: projects contribute to increased profitability [p] 8College of Home Economics: Research adds value, improves diets, protects human health [p] 9Horticulture, Forestry, Landscape and Parks: Managing natural resources and beautifying homes and communities [p] 10Plant Science: From fields to high-tech labs, Plant Science research has national impact [p] 11Rural Sociology: Taking the pulse of South Dakota\u27s farms and communities [p] 12Veterinary Science: Benefits of animal health improvements stretch from producer to consumer [p] 13Wildlife and Fisheries Sciences: Needs of citizens, producers, and wildlife are combined in research [p] 14108th Annual Report: The 108th annual report of the South Dakota Agricultural Experiment Station. The report includes lists of staff, projects, and publications. [p] 15https://openprairie.sdstate.edu/agexperimentsta_sd-fhr/1169/thumbnail.jp

    Male reproductive health : reasons why men may choose to participate in trials

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    This portfolio thesis comprises of three parts: a systematic literature review, an empirical report and a reflective statement.Part one is a systematic review in which literature relating to the empirical paper is reviewed. Due to a paucity of literature about reasons to participate in male reproductive health trials (RHTs), the broader area of reasons to participate in clinical trials, from a non-clinical sample, was reviewed. The review attempts to determine reasons why ‘healthy’ people participate in clinical trials and compares the findings with literature on reasons why patients participate. Recommendations are then made for future clinical trial recruitment strategies.Part two is an empirical paper encompassing two studies. Study one aimed to test hypotheses about factors that influence male participation in RHTs, specifically masculinity and altruism. Comments from participants about their own idiosyncratic reasons were then used to triangulate findings. Study two aimed to complement study one by exploring experiences of men participating in a RHT. Thematic constructions of stigma, altruism and masculinity were considered within a decision-making framework.Part three comprises of ppendices, including a reflective summary drawing on all aspects of the research process

    Too real for comfort? Uncanny responses to computer generated faces

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    As virtual humans approach photorealistic perfection, they risk making real humans uncomfortable. This intriguing phenomenon, known as the uncanny valley, is well known but not well understood. In an effort to demystify the causes of the uncanny valley, this paper proposes several perceptual, cognitive, and social mechanisms that have already helped address riddles like empathy, mate selection, threat avoidance, cognitive dissonance, and psychological defenses. In the four studies described herein, a computer generated human character’s facial proportions, skin texture, and level of detail were varied to examine their effect on perceived eeriness, human likeness, and attractiveness. In Study I, texture photorealism and polygon count increased human likeness. In Study II, texture photorealism heightened the accuracy of human judgments of ideal facial proportions. In Study III, atypical facial proportions were shown to be more disturbing on photorealistic faces than on other faces. In Study IV, a mismatch in the size and texture of the eyes and face was especially prone to make a character eerie. These results contest the depiction of the uncanny valley as a simple relation between comfort level and human likeness. This paper concludes by introducing a set of design principles for bridging the uncanny valley
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