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

Chen, Zhiyi; Wang, Ziyao; Du, Min; Liu, Zhenyu

doi:10.1002/jum.15827

Cited by 16 publications

(10 citation statements)

References 82 publications

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“…The evaluation of ovarian reserve and endometrial receptivity using ultrasound (US) is very important for female fertility [ 47 ]. With regards to ovarian reserve follicular monitoring in the diagnosis of Polycystic Ovary Syndrome (PCOS), as well as prediction of oocyte quality and pregnancy outcomes, variables such as ovarian follicular diameter and volume, number of follicles, and ovarian stromal blood flow index were considered [ 48 ].…”

Section: Resultsmentioning

confidence: 99%

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

Medenica

Zivanovic²,

Batkoska

et al. 2022

Diagnostics

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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.

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Section: Resultsmentioning

confidence: 99%

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

Medenica

Zivanovic²,

Batkoska

et al. 2022

Diagnostics

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“…It has been used in clinical follicular monitoring, optimum timing for embryo transfer, and prediction of pregnancy outcome. Chen et al published 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) [15].…”

Section: Artificial Intelligence-aided Ultrasoundmentioning

confidence: 99%

In Contemporary Reproductive Medicine Human Beings are Not Yet Dispensable

Allahbadia

Gupta³

2023

J Obstet Gynecol India

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In the past few years almost every aspect of an IVF cycle has been investigated, including research on sperm, color doppler in follicular studies, prediction of embryo cleavage, prediction of blastocyst formation, scoring blastocyst quality, prediction of euploid blastocysts and live birth from blastocysts, improving the embryo selection process, and for developing deep machine learning (ML) algorithms for optimal IVF stimulation protocols. Also, artificial intelligence (AI)-based methods have been implemented for some clinical aspects of IVF, such as assessing patient reproductive potential and individualizing gonadotropin stimulation protocols. As AI has the inherent capacity to analyze "Big" data, the goal will be to apply AI tools to the analysis of all embryological, clinical, and genetic data to provide patient-tailored individualized treatments. Human skillsets including hand eye coordination to perform an embryo transfer is probably the only step of IVF that is outside the realm of AI & ML today. Embryo transfer success is presently human skill dependent and deep machine learning may one day intrude into this sacred space with the advent of programed humanoid robots. Embryo transfer is arguably the rate limiting step in the sequential events that complete an IVF cycle. Many variables play a role in the success of embryo transfer, including catheter type, atraumatic technique, and the use of sonography guidance before and during the procedure of embryo transfer. In contemporary Reproductive Medicine human beings are not yet dispensable.

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“…The pieces are then stacked in the order of their actual status. Then, according to [44], 3D connectedcomponent labelling was applied. In order to identify specific areas and gauge the size of regions, each connected component in an image is given a unique label through this technique.…”

Section: B Utilization Of 2d-arvnet For Crd Localizationmentioning

confidence: 99%

“…The use of AI techniques during pregnancy is possible with a distinct set of imaging technologies, such as ultrasound, MRI, and Computed Tomography (CT) [44]. Ultrasound imaging has emerged in clinical studies and is used throughout every trimester of pregnancy.…”

Section: Introductionmentioning

confidence: 99%

Echocardiographic Image Segmentation for Diagnosing Fetal Cardiac Rhabdomyoma During Pregnancy Using Deep Learning

et al. 2022

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Automated interpretation of cardiac images has the potential to change clinical practice in many ways. For example, it could make it possible for non-experts in primary care and rural settings to test the heart's function over time. In this paper, we tested the research hypothesis that recent developments in computer vision would make it possible to create a fully automated, scalable analysis for echocardiogram interpretation, covering all of the steps from view identification and Medical Image Segmentation (MIS) to structure and function quantification and Fetal Cardiac RhabDomyomas (FCRD) detection. Even though they are rare, FCRDs are the most frequent cause of Fetal Cardiac Tumor (FCT). When it comes to diagnosing and monitoring fetuses with an injured circulatory system, imaging (particularly echocardiography (ECG)) has proven helpful in the field of fetal cardiology. Because of the severe lack of qualified and experienced sonographers, it is very challenging t o diagnose Cardiac RhabDomyomas (CRD). Prior to delivery, accurate segmentation of the FC to identify structural cardiac defects is critical for minimizing the illness among newborns. To automate the process of segmenting the cardiac chamber for the CRD, we propose a novel Attention-Residual Network-based V-Net architecture (ARVNet). In this study, examinations were performed on Fetal Rhabdomyomas noted in the Right Ventricle (FRRV), Fetal Rhabdomyomas noted in the Left Ventricle (FRLV), Fetal Rhabdomyomas noted in the Right Atrium (FRRA), Fetal Rhabdomyomas noted in the Left Atrium (FRLA), Fetal Rhabdomyomas noted in the Tricuspid Valve (FRTV). Images without Rhabdomyoma mean "Normal Condition (NC)" at Selvam Hospital in Melapalayam, Tirunelveli, Tamil Nadu, India. Even with a relatively small number of datasets, the proposed technique possesses high CRD detection performance, as evidenced by the results. The results showed that the proposed model did a good job segmenting all the views, with a specificity of 99.7% and a Dice coefficient similarit y of 99.8%. It also did well at finding CRDs, with an average mean accuracy of around 99.85%.

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

Cited by 16 publications

References 82 publications

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

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

In Contemporary Reproductive Medicine Human Beings are Not Yet Dispensable

Echocardiographic Image Segmentation for Diagnosing Fetal Cardiac Rhabdomyoma During Pregnancy Using Deep Learning

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