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Deep learning based detection and classification of fetal lip in ultrasound images

  • Yapeng Li , Peiya Cai , Yubing Huang , Weifeng Yu , Zhonghua Liu EMAIL logo and Peizhong Liu EMAIL logo
Published/Copyright: July 22, 2024
Journal of Perinatal Medicine
From the journal Journal of Perinatal Medicine Volume 52 Issue 7

Abstract

Objectives

Fetal cleft lip is a common congenital defect. Considering the delicacy and difficulty of observing fetal lips, we have utilized deep learning technology to develop a new model aimed at quickly and accurately assessing the development of fetal lips during prenatal examinations. This model can detect ultrasound images of the fetal lips and classify them, aiming to provide a more objective prediction for the development of fetal lips.

Methods

This study included 632 pregnant women in their mid-pregnancy stage, who underwent ultrasound examinations of the fetal lips, collecting both normal and abnormal fetal lip ultrasound images. To improve the accuracy of the detection and classification of fetal lips, we proposed and validated the Yolov5-ECA model.

Results

The experimental results show that, compared with the currently popular 10 models, our model achieved the best results in the detection and classification of fetal lips. In terms of the detection of fetal lips, the mean average precision (mAP) at 0.5 and mAP at 0.5:0.95 were 0.920 and 0.630, respectively. In the classification of fetal lip ultrasound images, the accuracy reached 0.925.

Conclusions

The deep learning algorithm has accuracy consistent with manual evaluation in the detection and classification process of fetal lips. This automated recognition technology can provide a powerful tool for inexperienced young doctors, helping them to accurately conduct examinations and diagnoses of fetal lips.

Keywords: fetal cleft lip; deep learning; detection; classification
Corresponding authors: Zhonghua Liu, Department of Ultrasound, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, 362000, China, E-mail: ; and Peizhong Liu, School of Medicine, Huaqiao University, Quanzhou, 362021, China; and College of Engineering, Huaqiao University, Quanzhou, 362021, China, E-mail:

Funding source: Quanzhou Scientific and Technological Planning Projects

Award Identifier / Grant number: 2022NS057, 2022C006R

Funding source: National Natural Science Foundation of Fujian

Award Identifier / Grant number: 2021J011394, 2021J011404

Funding source: scientific Research Funds of Huaqiao University

Award Identifier / Grant number: No. 605-50Y23038

  1. Research ethics: This study has been approved by the Medical Ethics Committees of Quanzhou First Hospital Affiliated to Fujian Medical University and The Second Affiliated Hospital of Fujian Medical University.

  2. Informed consent: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Author contributions: Yapeng Li is in charge of the model experiments and wrote the paper; Peiya Cai is responsible labeling data; Yubing Huang is responsible for verifying the annotated data; Weifeng Yu is responsible labeling data; Peizhong Liu is responsible conceptualization; Zhonghua Liu is responsible for providing data. All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Competing interests: Authors state no conflict of interest.

  5. Research funding: None declared.

  6. Data availability: Due to the confidentiality of the data, some data is available upon request.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/jpm-2024-0122).

Received: 2024-03-19
Accepted: 2024-07-07
Published Online: 2024-07-22
Published in Print: 2024-09-25

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/jpm-2024-0122
Keywords for this article
fetal cleft lip; deep learning; detection; classification

Articles in the same Issue

  1. Frontmatter
  2. Corner of Academy
  3. The IAPM New York 2024 declaration on professional responsibility and abortion
  4. Review
  5. Common foot and ankle disorders in pregnancy: the role of diagnostic ultrasound
  6. Opinion Paper
  7. Reproductive genetic carrier screening in pregnancy: improving health outcomes and expanding access
  8. Original Articles – Obstetrics
  9. “It feels like you have to choose one or the other”: a qualitative analysis of obstetrician focus groups on periviability counseling
  10. Expectant management vs. cerclage in cases with prolapsed or visible membranes in the second trimester: is 24 weeks gestation threshold critical?
  11. Prevention of preterm birth in twin-to-twin transfusion syndrome: a systematic review and network meta-analysis
  12. Accidental uterine extensions in cesarean deliveries – outcome of subsequent pregnancy
  13. Effect of acidic vaginal pH on the efficacy of dinoprostone (PGE2) vaginal tablet for labor induction in full term pregnant women: a randomized controlled trial
  14. Oligohydramnios at term in the high-risk population – how severe is severe?
  15. Original Articles – Fetus
  16. Assessment of the fetal thymic-thoracic ratio in pregnant women with intrahepatic cholestasis: a prospective case-control study
  17. Congenital diaphragmatic hernia treated via fetal endoscopic tracheal occlusion improves outcome in a middle-income country
  18. Fetal bradyarrhythmias: classification, monitoring and outcomes of 40 cases at a single center
  19. Deep learning based detection and classification of fetal lip in ultrasound images
  20. Original Articles – Neonates
  21. Cytomegalovirus congenital infection: long-term outcomes in a valaciclovir treated population
  22. Does placental VEGF-A protein expression predict early neurological outcome of neonates from FGR complicated pregnancies?
  23. Letters to the Editor
  24. Why do women choose home births: correspondence
  25. Enhancing safety and outcomes in home births: a detailed response to concerns and recommendations
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