Am J Perinatol
DOI: 10.1055/a-2265-9177
Original Article

Artificial Intelligence to Determine Fetal Sex

1   Women's Health Institute, Cleveland Clinic, Cleveland, Ohio
,
Anant Jain
2   Centaur Labs, Boston, Massachusetts
,
Mike Jin
2   Centaur Labs, Boston, Massachusetts
,
Erik P. Duhaime
2   Centaur Labs, Boston, Massachusetts
,
Amol Malshe
1   Women's Health Institute, Cleveland Clinic, Cleveland, Ohio
,
Steve Corey
3   BabyFlix, San Diego, California
,
Robert Allen
3   BabyFlix, San Diego, California
,
Nicole M. Duggan
4   Department of Emergency Medicine, Brigham and Women's Hospital, Boston, Massachusetts
,
Chanel E. Fischetti
4   Department of Emergency Medicine, Brigham and Women's Hospital, Boston, Massachusetts
5   Harvard Medical School, Boston, Massachusetts
› Author Affiliations

Abstract

Objective This proof-of-concept study assessed how confidently an artificial intelligence (AI) model can determine the sex of a fetus from an ultrasound image.

Study Design Analysis was performed using 19,212 ultrasound image slices from a high-volume fetal sex determination practice. This dataset was split into a training set (11,769) and test set (7,443). A computer vision model was trained using a transfer learning approach with EfficientNetB4 architecture as base. The performance of the computer vision model was evaluated on the hold out test set. Accuracy, Cohen's Kappa and Multiclass Receiver Operating Characteristic area under the curve (AUC) were used to evaluate the performance of the model.

Results The AI model achieved an Accuracy of 88.27% on the holdout test set and a Cohen's Kappa score 0.843. The ROC AUC score for Male was calculated to be 0.896, for Female a score of 0.897, for Unable to Assess a score of 0.916, and for Text Added a score of 0.981 was achieved.

Conclusion This novel AI model proved to have a high rate of fetal sex capture that could be of significant use in areas where ultrasound expertise is not readily available.

Key Points

  • This is the first proof-of-concept AI model to determine fetal sex.

  • This study adds to the growing research in ultrasound AI.

  • Our findings demonstrate AI integration into obstetric care.

Note

This research was presented at Society of Maternal Fetal Medicine Annual Conference in February 2023.




Publication History

Received: 06 November 2023

Accepted: 06 February 2024

Accepted Manuscript online:
09 February 2024

Article published online:
01 March 2024

© 2024. Thieme. All rights reserved.

Thieme Medical Publishers, Inc.
333 Seventh Avenue, 18th Floor, New York, NY 10001, USA

 
  • References

  • 1 Witchel SF. Management of CAH during pregnancy: optimizing outcomes. Curr Opin Endocrinol Diabetes Obes 2012; 19 (06) 489-496
  • 2 Reddy UM, Abuhamad AZ, Levine D, Saade GR, Participants FIWI. Fetal Imaging Workshop Invited Participants. Fetal imaging: Executive summary of a joint Eunice Kennedy Shriver National Institute of Child Health and Human Development, Society for Maternal-Fetal Medicine, American Institute of Ultrasound in Medicine, American College of Obstetricians and Gynecologists, American College of Radiology, Society for Pediatric Radiology, and Society of Radiologists in Ultrasound Fetal Imaging Workshop. Am J Obstet Gynecol 2014; 210 (05) 387-397
  • 3 Kearin M, Pollard K, Garbett I. Accuracy of sonographic fetal gender determination: predictions made by sonographers during routine obstetric ultrasound scans. Australas J Ultrasound Med 2014; 17 (03) 125-130
  • 4 Colmant C, Morin-Surroca M, Fuchs F, Fernandez H, Senat M-V. Non-invasive prenatal testing for fetal sex determination: is ultrasound still relevant?. Eur J Obstet Gynecol Reprod Biol 2013; 171 (02) 197-204
  • 5 Manzanares S, Benítez A, Naveiro-Fuentes M, López-Criado MS, Sánchez-Gila M. Accuracy of fetal sex determination on ultrasound examination in the first trimester of pregnancy. J Clin Ultrasound 2016; 44 (05) 272-277
  • 6 Odeh M, Granin V, Kais M, Ophir E, Bornstein J. Sonographic fetal sex determination. Obstet Gynecol Surv 2009; 64 (01) 50-57
  • 7 Wright CF, Wei Y, Higgins JP, Sagoo GS. Non-invasive prenatal diagnostic test accuracy for fetal sex using cell-free DNA a review and meta-analysis. BMC Res Notes 2012; 5 (01) 476
  • 8 Mujezinovic F, Alfirevic Z. Procedure-related complications of amniocentesis and chorionic villous sampling: a systematic review. Obstet Gynecol 2007; 110 (03) 687-694
  • 9 Finning KM, Chitty LS. Non-invasive fetal sex determination: impact on clinical practice. Elsevier; 2008: 69-75
  • 10 Kim HY, Cho GJ, Kwon HS. Applications of artificial intelligence in obstetrics. Ultrasonography 2023; 42 (01) 2-9
  • 11 Drukker L, Noble JA, Papageorghiou AT. Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology. Ultrasound Obstet Gynecol 2020; 56 (04) 498-505
  • 12 Doig M, Dizon J, Guerrero K, Parange N. Exploring the availability and impact of antenatal point-of-care ultrasound services in rural and remote communities: a scoping review. Australas J Ultrasound Med 2019; 22 (03) 174-185
  • 13 Peterman NJ, Yeo E, Kaptur B. et al. Analysis of rural disparities in ultrasound access. Cureus 2022; 14 (05) e25425
  • 14 Chigbu CO, Odugu B, Okezie O. Implications of incorrect determination of fetal sex by ultrasound. Int J Gynaecol Obstet 2008; 100 (03) 287-290
  • 15 Shukar-Ud-Din S, Ubaid F, Shahani E, Saleh F. Reasons for disclosure of gender to pregnant women during prenatal ultrasonography. Int J Womens Health 2013; 5: 781-785
  • 16 Shipp TD, Shipp DZ, Bromley B. et al. What factors are associated with parents' desire to know the sex of their unborn child?. Birth 2004; 31 (04) 272-279