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Differences in the anatomical structure of the uterus between fertile and infertile individuals.

Betul SevindikNadire Unver DoganOzlem SecilmisEmine UysalZeliha FazliogullariAhmet Kagan Karabulut
Published in: Clinical anatomy (New York, N.Y.) (2023)
Infertility affects a significant portion of the reproductive population and is caused by structural and hormonal factors. The prevalence of congenital uterine anomalies is 3%-4%, with the most common anomaly being septate uterus. However, further research is needed to determine whether these anomalies can cause infertility. In this study, we compared the morphometric parameters of the uterus of fertile and infertile individuals. Based on the data obtained, we aimed to determine the parameters to be evaluated for fertility prediction and to investigate the effect of uterine septum on fertility. The uteruses of 55 infertile and 80 fertile individuals between the age range of 20-45 years were analyzed retrospectively using magnetic resonance images. Infertile individuals were categorized into two groups according to the reasons for infertility: Group I, which included women with congenital uterine anomalies (septate uterus), and Group II, which included women with tubal and male factors. Group III comprised fertile individuals. Uterine length (UL), uterine body length (UbL), cervical length (CxL), uterine cavity length (UcL), anteroposterior diameter (APD), transverse diameter (TD), fundal thickness (FT), and ostial distance (OD) were measured. The uterine positions were examined. The data of uterine variables were evaluated statistically according to age and groups. The mean ages of individuals in Groups I, II, and III were 29.88 ± 6.69, 29.21 ± 4.59, and 27.45 ± 5.43 years, respectively. Significant differences were observed between the groups in terms of UL, UbL, CxL, UcL, APD, FT, and OD variables (p < 0.05), except for TD (p > 0.05). We observed that UL, UcL, length/width ratio, and APD parameters are important factors that influence fertility. Evaluating these parameters before septum resection would be useful in predicting the contribution of this surgical operation to fertility.
Keyphrases
  • polycystic ovary syndrome
  • magnetic resonance
  • deep learning
  • magnetic resonance imaging
  • machine learning
  • big data
  • type diabetes
  • metabolic syndrome
  • adipose tissue
  • skeletal muscle
  • artificial intelligence
  • optic nerve