/pdf/a-review-on-deep-learning-algorithms-for-fetal-ultrasound-ejqth3ya.pdf

A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysis

Infertility is a global health issue affecting women and men of reproductive age with increasing incidence worldwide, in part due to greater awareness and better diagnosis. Assisted reproduction technologies (ART) are considered the ultimate step in the treatment of infertility. Recently, artificial intelligence (AI) has been progressively used in the many fields of medicine, integrating knowledge and computer science through machine learning algorithms. AI has the potential to improve infertility diagnosis and ART outcomes estimated as pregnancy and/or live birth rate, especially with recurrent ART failure. A broad-ranging review has been conducted, focusing on clinical AI applications up until September 2022, which could be estimated in terms of possible applications, such as ultrasound monitoring of folliculogenesis, endometrial receptivity, embryo selection based on quality and viability, and prediction of post implantation embryo development, in order to eliminate potential contributing risk factors. Oocyte morphology assessment is highly relevant in terms of successful fertilization rate, as well as during oocyte freezing for fertility preservation, and substantially valuable in oocyte donation cycles. AI has great implications in the assessment of male infertility, with computerised semen analysis systems already in use and a broad spectrum of possible AI-based applications in environmental and lifestyle evaluation to predict semen quality. In addition, considerable progress has been made in terms of harnessing AI in cases of idiopathic infertility, to improve the stratification of infertile/fertile couples based on their biological and clinical signatures. With AI as a very powerful tool of the future, our review is meant to summarise current AI applications and investigations in contemporary reproduction medicine, mainly focusing on the nonsurgical aspects of it; in addition, the authors have briefly explored the frames of reference and guiding principles for the definition and implementation of legal, regulatory, and ethical standards for AI in healthcare.

/pdf/the-future-is-coming-artificial-intelligence-in-the-10onmrj0.pdf

The Future Is Coming: Artificial Intelligence in the Treatment of Infertility Could Improve Assisted Reproduction Outcomes—The Value of Regulatory Frameworks

Prenatal ultrasonography is the most crucial imaging modality during pregnancy. However, problems such as high fetal mobility, excessive maternal abdominal wall thickness, and inter-observer variability limit the development of traditional ultrasound in clinical applications. The combination of artificial intelligence (AI) and obstetric ultrasound may help optimize fetal ultrasound examination by shortening the examination time, reducing the physician’s workload, and improving diagnostic accuracy. AI has been successfully applied to automatic fetal ultrasound standard plane detection, biometric parameter measurement, and disease diagnosis to facilitate conventional imaging approaches. In this review, we attempt to thoroughly review the applications and advantages of AI in prenatal fetal ultrasound and discuss the challenges and promises of this new field.

/pdf/application-and-progress-of-artificial-intelligence-in-fetal-2oenushz.pdf

Application and Progress of Artificial Intelligence in Fetal Ultrasound

Imaging fetal anatomy.

http://www.cell.com/article/S2589004222009853/pdf

Toward deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via ultrasound images

Artificial intelligence (AI) can support clinical decisions and provide quality assurance for images. Although ultrasonography is commonly used in the field of obstetrics and gynecology, the use of AI is still in a stage of infancy. Nevertheless, in repetitive ultrasound examinations, such as those involving automatic positioning and identification of fetal structures, prediction of gestational age (GA), and real-time image quality assurance, AI has great potential. To realize its application, it is necessary to promote interdisciplinary communication between AI developers and sonographers. In this review, we outlined the benefits of AI technology in obstetric ultrasound diagnosis by optimizing image acquisition, quantification, segmentation, and location identification, which can be helpful for obstetric ultrasound diagnosis in different periods of pregnancy.

/pdf/artificial-intelligence-in-obstetric-ultrasound-an-update-1l1jqa1ae4.pdf

Artificial Intelligence in Obstetric Ultrasound: An Update and Future Applications.

The incidence of infertility is continuously increasing nearly all over the world in recent years, and novel methods for accurate assessment are of great need. Artificial Intelligence (AI) has gradually become an effective supplementary method for the assessment of female reproductive function. It has been used in clinical follicular monitoring, optimum timing for transplantation, and prediction of pregnancy outcome. Some literatures summarize the use of AI in this field, but few of them focus on the assessment of female reproductive function by AI-aided ultrasound. In this review, we mainly discussed the applicability, feasibility, and value of clinical application of AI in ultrasound to monitor follicles, assess endometrial receptivity, and predict the pregnancy outcome of in vitro fertilization and embryo transfer (IVF-ET). The limitations, challenges, and future trends of ultrasound combined with AI in providing efficient and individualized evaluation of female reproductive function had also been mentioned.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/jum.15827

Artificial Intelligence in the Assessment of Female Reproductive Function Using Ultrasound: A Review.

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Artificial Intelligence in Obstetric Ultrasound: An Update and Future Applications.

Artificial Intelligence in the Assessment of Female Reproductive Function Using Ultrasound: A Review.