Adoption of artificial intelligence in precision matching of donor sperm during assisted reproduction process (Preprint)

2021 ◽  
Author(s):  
wang qiling ◽  
huang weibiao
Keyword(s):  
Artificial Intelligence ◽  
Assisted Reproduction ◽  
Donor Sperm ◽  
Reproduction Process

Data sharing: using blockchain and decentralized data technologies to unlock the potential of artificial intelligence: What can assisted reproduction learn from other areas of medicine?

2020 ◽  
Vol 114 (5) ◽  
pp. 927-933 ◽  
Author(s):  
Cristina Fontes Lindemann Hickman ◽  
Hoor Alshubbar ◽  
Jerome Chambost ◽  
Celine Jacques ◽  
Chris-Alexandre Pena ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Data Sharing ◽  
Assisted Reproduction

Adoption of artificial intelligence in precision matching of donor sperm during assisted reproduction process (Preprint)

2021 ◽  
Author(s):  
Wang Qiling ◽  
huang weibiao
Keyword(s):  
Artificial Intelligence ◽  
Skin Color ◽  
High Speed ◽  
High Efficiency ◽  
Muscular Atrophy ◽  
Genetic Diseases ◽  
Blood Type ◽  
Sperm Donor ◽  
Donor Sperm ◽  
Face Shape

UNSTRUCTURED An artificial intelligence (AI) based sperm donor humanized matching system was launched to meet infertile patients' requirements on personalized physical appearance of the expected sperm donor such as blood type, origin, ethnicity, height, weight, body build, skin color, hair, face shape, nose bridge, eyelids, iris color, lips, etc. Relying on high-speed 5G networks, the AI matching information in an encoded pattern is fed back to patients in real time and ranked according to similarity. To date, the highest similarity is up to 96%. This system can provide high efficiency and accuracy and avoid the drawbacks of previous manual operations which were tedious, slow and error-prone. In addition, the system helps patients carrying genetic mutations (including thalassemia, spinal muscular atrophy) avoid off-springs’ genetic diseases by matching donors who are qualified by further genetic testing. This system sets a good example of the smart medical market which can also play an important role in addressing patients' personalized medical requirements in addition to aiding in the diagnosis and treatment of diseases.

Techniques for sperm recovery in assisted reproduction

1999 ◽  
Vol 7 (2) ◽  
pp. 131-139 ◽  
Author(s):  
Sarah K Girardi ◽  
Peter N Schlegel
Keyword(s):  
Assisted Reproduction ◽  
Testicular Biopsy ◽  
Surgical Reconstruction ◽  
Significant Heterogeneity ◽  
Obstructive Azoospermia ◽  
Donor Insemination ◽  
Nonobstructive Azoospermia ◽  
Sperm Retrieval ◽  
Donor Sperm ◽  
Sperm Aspiration

During the past decade, few fields in medicine have changed as dramatically as reproductive medicine and the treatment of male infertility. Whereas previously only men with obstructive azoospermia were candidates for treatment, either through surgical reconstruction or sperm aspiration, now even men with nonobstructive azoospermia are able to achieve pregnancies without having to resort to donor sperm. The extraordinary success of assisted reproduction after sperm retrieval for azoospermic men is the result of three important discoveries. First is the clinical observation that epididymal transit of sperm is not required for successful fertilization. Second is the recognition that significant heterogeneity in testicular biopsy specimens exists. Lastly is the advent of intracytoplasmic sperm injection (ICSI), which has enabled fertilization regardless of the degree of sperm impairment or retrieval source as long as sperm are viable. These three discoveries have enabled fertilizations and pregnancies for men previously referred for donor insemination or adoption, and have therefore broadened the indications for sperm retrieval. This review is intended to describe in detail the available techniques for the recovery of sperm, with emphasis on the latest technique, testicular microdissection for sperm retrieval in nonobstructive azoospermia.

Adoption of artificial intelligence in precision matching of donor sperm during assisted reproduction process (Preprint)

2021 ◽  
Author(s):  
Wang Qiling
Keyword(s):  
Artificial Intelligence ◽  
Skin Color ◽  
High Speed ◽  
High Efficiency ◽  
Muscular Atrophy ◽  
Genetic Diseases ◽  
Blood Type ◽  
Sperm Donor ◽  
Donor Sperm ◽  
Face Shape

UNSTRUCTURED An artificial intelligence (AI) based sperm donor humanized matching system was launched to meet infertile patients' requirements on personalized physical appearance of the expected sperm donor such as blood type, origin, ethnicity, height, weight, body build, skin color, hair, face shape, nose bridge, eyelids, iris color, lips, etc. Relying on high-speed 5G networks, the AI matching information in an encoded pattern is fed back to patients in real time and ranked according to similarity. To date, the highest similarity is up to 96%. This system can provide high efficiency and accuracy and avoid the drawbacks of previous manual operations which were tedious, slow and error-prone. In addition, the system helps patients carrying genetic mutations (including thalassemia, spinal muscular atrophy) avoid off-springs’ genetic diseases by matching donors who are qualified by further genetic testing. This system sets a good example of the smart medical market which can also play an important role in addressing patients' personalized medical requirements in addition to aiding in the diagnosis and treatment of diseases.

scholarly journals Big Data and Artificial Intelligence (AI) are Poised to Transform Infertility Healthcare

2020 ◽  
Author(s):  
Ines de Santiago ◽  
Lukasz Polanski
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Assisted Reproduction ◽  
Clinical Decision Making ◽  
Clinical Medicine ◽  
Clinical Decision ◽  
Poor Quality ◽  
Research Progress ◽  
Successful Outcome

Advances in machine learning (ML) and artificial intelligence (AI) are transforming the way we treat patients in ways not even imagined a few years ago. Cancer research is at the forefront of this movement. Infertility, though not a life-threatening condition, affects around 15% of couples trying for a pregnancy. Increasing availability of large datasets from various sources creates an opportunity to introduce ML and AI into infertility prevention and treatment. At present in the field of assisted reproduction, very little is done in order to prevent infertility from arising, with the main focus put on treatment when often advanced maternal age and low ovarian reserve make it very difficult to conceive. A shift from this disease-centric model to a health centric model in infertility is already taking place with more emphasis on the patient as an active participator in the process. Poor quality and incomplete data as well as biological variability remain the main limitations in the widespread and reliable implementation of AI in the field of reproductive medicine. That said, one of the areas where this technology managed to find a foothold is identification of developmentally competent embryos. More work is required however to learn about ways to improve natural conception, the detection and diagnosis of infertility, and improve assisted reproduction treatments (ART) and ultimately, develop clinically useful algorithms able to adjust treatment regimens in order to assure a successful outcome of either fertility preservation or infertility treatment. Progress in genomics, digital technologies and advances in integrative biology has had a tremendousimpact on research and clinical medicine. With the rise of ‘big data’, artificial intelligence, and the advances in molecular profiling, there is an enormous potential to transform not only scientific research progress, but also clinical decision making towards predictive, preventive, and personalized medicine. In the field of reproductive health, there is now an exciting opportunity to leverage these technologies and develop more sophisticated approaches to diagnose and treat infertility disorders. In this review, we present a comprehensive analysis and interpretation of different innovation forces that are driving the emergence of a system approach to the infertility sector. Here we discuss recent influential work and explore the limitations of the use of Machine Learning models in this rapidly developing area.

Infections, Infertility, and Assisted Reproduction

2004 ◽  
Author(s):  
Kay Elder ◽  
Doris J. Baker ◽  
Julie A. Ribes
Keyword(s):  
Assisted Reproduction
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