Using deep-learning in fetal ultrasound analysis for diagnosis of cystic hygroma in the first trimester
Open accessAbstract
Objective: To develop and internally validate a deep-learning algorithm from fetal ultrasound images for the diagnosis of cystic hygromas in the first trimester. Methods: All first trimester ultrasound scans with a diagnosis of a cystic hygroma between 11 and 14 weeks gestation at our tertiary care centre in Ontario, Canada were studied. Ultrasound scans with normal nuchal translucency were used as controls. The dataset was partitioned with 75% of images used for model training and 25% used for model validation. Images were analyzed using a DenseNet model and the accuracy of the trained model to correctly identify cases of cystic hygroma was assessed by calculating sensitivity, specificity, and the area under the receiver-operating characteristic (ROC) curve. Gradient class activation heat maps (Grad-CAM) were generated to assess model interpretability. Results: The dataset included 289 sagittal fetal ultrasound images;129 cystic hygroma cases and 160 normal NT controls. Overall model accuracy was 93% (95% CI: 88–98%), sensitivity 92% (95% CI: 79–100%), specificity 94% (95% CI: 91–96%), and the area under the ROC curve 0.94 (95% CI: 0.89–1.0). Grad-CAM heat maps demonstrated that the model predictions were driven primarily by the fetal posterior cervical area. Conclusions: Our findings demonstrate that deep-learning algorithms can achieve high accuracy in diagnostic interpretation of cystic hygroma in the first trimester, validated against expert clinical assessment.
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Walker et al., Zhang et al., Sciortino et al., and Deng et al. introduced AI models for automated detection and measurement of NT.
“The research groups Walker et al. [132], Zhang et al. [133], Sciortino et al. [134], and Deng et al. [135] addressed the time consuming process of NT measurement by introducing AI models for its automated detection and measurement”
From Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology (2023) · cites this paper as [132]
The Introduction and Methods (page not provided) describe Walker et al.’s deep-learning model for identifying cystic hygroma in first-trimester ultrasound images, using images with normal NT as controls. They do not describe a model that automatically measures NT thickness; the Introduction discusses NT-measurement systems as work from other studies. (LLM, conf=0.90)
Experimental — accuracy not yet measuredDetection of rare pathologies is limited due to missing training of pattern recognition models, as AI algorithms depend on large data sets.
“As AI algorithms are usually dependent on large data sets for training, the detection of rare pathologies is limited due to missing training of pattern recognition models [132,133].”
From Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology (2023) · cites this paper as [132]
The Introduction says deep-learning models process large amounts of data, and the Discussion links the low incidence of cystic hygroma to the need for a larger, more varied dataset. But the paper does not say that insufficient training prevented detection of this rare condition: it reports high predictive accuracy using a relatively small dataset and strategies to address its limitations. (LLM, conf=0.84)
Experimental — accuracy not yet measuredA limitation of first trimester AI models was small data sets in rare anomalies.
“small data sets in rare anomalies [132,133]”
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- Authors
- Walker, M. C., Willner, I., Miguel, O. X., Murphy, M. S. Q., El-Chaâr, D., Moretti, F., Dingwall Harvey, A. L. J., Rennicks White, R., Muldoon, K. A., Carrington, A. M., Hawken, S., & Aviv, R. I.
- Affiliations
- University of Ottawa (CA)
- Ottawa Hospital Research Institute (CA)
- University of Waterloo (CA)
- Corresponding author
- Walker, Mark C.
- ORCID
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2022-06-22
- Volume
- 17
- Issue
- 6
- Pages
- e0269323
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC9216531
- OpenAlex
- W4283375416
- Cited by
- 31
- References
- 25
- Citations by year
- 2026: 10 · 2025: 6 · 2024: 8 · 2023: 6 · 2022: 1
- FWCI
- 1.58
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Institute of Human Development, Child and Youth Health (148438)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Prenatal Screening and Diagnostics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- cystic lymphangioma, first trimester, prenatal ultrasound, deep learning, DenseNet, nuchal translucency, Grad-CAM, model interpretability, prenatal diagnosis, internal validation
- MeSH terms
- Deep Learning, Chromosome Aberrations, Female, Humans, Ontario, Pregnancy, Pregnancy Trimester, First, Ultrasonography, Prenatal, Lymphangioma, Cystic
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