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Jan 2026
Kaur KirandeepCorresponding author
Background Female infertility has always been an issue and social stigma in India and it’s not only the matter of physical health of the female but also related to psychological health and the social status of the women. The diagnosis of this problem criterion requires medical symptoms for the condition, including clinical and metabolic parameters. The application of AI-driven algorithms into medical science is responsible for the specific phrase Medical Artificial Intelligence (MAI) which has introduced a transformative swing in female reproductive health, qualifying higher diagnostic accuracy, reproducibility, and efficiency in addressing complex conditions such as infertility and hormonal disorders. From Ovarian stimulation to Artificial Intelligence in Assisted Reproductive Technology, there have been significant advancements in the incorporation of machine learning and IoT in the medical Labs. AI algorithms are expected to bring about a more calculated, computed, and standardized approach to the infertility problem today females are facing. Aim This review aims to establish connection between female infertility problems with AI and discusses how an effort can be made to resolve such sensitive health issue, keeping in view the recent and past contribution of emerging AI technologies in health sciences. The AI trained models are based on either supervised learning algorithms of unsupervised learning algorithms. Basically the input to the medical trained AI models is the set of instances referred to as medical dataset which can be categorized, clustered or correlated as per requirements. Each instance is further described by the values of a set of attributes like diagnosis, treatment, laboratory tests data, prescription drugs etc. These medical dataset can be further represented in the form of matrix and later can be used to aid learning methods that is supervised or unsupervised learning methods to train AI medical models. That is why the term MAI is being used in this review paper. Methods Used Studies were explored across IEEE Xplore, PubMed, ResearchGate, SpringerLink and npj Digital Medicine for papers published. Following the database probing, identical articles were pull out and the lasting titles and abstracts were inspected for suitability. Studies were incorporated if they portrayed the role of Artificial Intelligence for female reproductive health or infertility problems in reproductive age. Result Following data was extracted from 12 selected articles. These studies were eventually chosen for complete estimation. Conclusion AI has proved to be an incomparable assistance tool in providing specialist approach in the reproductive health issues in females. But there is need to describe ways to develop a framework of wearable technology in the form of sensor to predict correct ovulation period.
Dec 2025 DOI 10.14302/issn.2575-1212.jvhc-24-4889
A Elmetwally MohammedCorresponding author
L-Carnitine (Lc) acts as an antioxidant that neutralizes free radicals, especially superoxide anions and protects cells against oxidative damage-induced apoptosis, as following ovulation, intracellular reactive oxygen species (ROS) accumulation increases in oocytes, Oocytes exhibit an intracellular defense mechanism against an oxidative attack. This outcome adversely affects fertilization and subsequent embryonic development, thereby increasing the risk of an early miscarriage and abnormal development of offspring. The purpose of this study was to see how adding LC to either maturation or fertilization medium affected the developmental competence of immature bovine oocytes. In this study, Ovaries from apparently normal reproductive organs of cattle were collected within 30 minutes from slaughter and evisceration of animals. Cumulus oocyte complexes (COCs) were collected by aspiration of medium sized ovarian follicles (4-8 mm). COCs of acceptable quality were selected, washed and incubated in tissue culture media 199 (TCM199) supplemented with 10% heat inactivated fetal calf serum, 5 μg/ml luteinizing hormone (LH), 0.5 μg/ml follicle stimulating hormone (FSH) and 1 μg/ml estradiol-17β for 20:22 hour at 38.5 C◦ under 5% CO2 in air with 90% humidity. different concentrations of LC (1.25,2.5 and 5mM) were used. The results were consistent for both maturation and fertilization and there is a significant increase in maturation, fertilization., cleavage and blastocyst rate. In conclusion, LC has important role in IVEP through addition of LC to maturation media or culture media it improved nuclear maturation and blastocyst formation rates in bovine oocytes.
Nov 2021 DOI 10.14302/issn.2575-1212.jvhc-21-3973
A Elmetwally MohammedCorresponding author
Departments of Theriogenology,
This review focused on the various methods for controlling estrous cycles in well-managed dairy cows. Because up to 70% of dairy cows may stay non-pregnant after an AI procedure, an effective approach for identifying and reinseminating open cows is essential for dairy herds to achieve optimal reproductive performance. Overall, well-managed dairy farms with effective estrus detection programs inseminate 50% or more of non-pregnant cows after behavioral estrus is detected. Cows not detected in estrus are admitted in a resynchronization of ovulation procedure to receive a timed AI (TAI) service to avoid a long interbreeding interval. In Egypt, a widely used program involves starting the Ovsynch protocol (GnRH-7 d-PGF2-56 h-GnRH-16 to 20 h-TAI) 32 days after an initial AI, regardless of pregnancy status. Previous studies have proven that there was no difference in pregnancy/artificial insemination (P/AI) between Ovsynch+P4 and Presynch-Ovsynch, both protocols were equally effective in improving the fertility of cows with a CL 15 mm. The review also addressed different methods for synchronization of ovulation and different factors affecting the selection of the management program.