Using deep-learning algorithms to classify fetal brain ultrasound images as normal or abnormal
Open accessAbstract
Objectives: To evaluate the feasibility of using deep‐learning algorithms to classify as normal or abnormal sonographic images of the fetal brain obtained in standard axial planes. Methods: We included in the study images retrieved from a large hospital database from 10 251 normal and 2529 abnormal pregnancies. Abnormal cases were confirmed by neonatal ultrasound, follow‐up examination or autopsy. After a series of pretraining data processing steps, 15 372 normal and 14 047 abnormal fetal brain images in standard axial planes were obtained. These were divided into training and test datasets (at case level rather than image level), at a ratio of approximately 8:2. The training data were used to train the algorithms for three purposes: performance of image segmentation along the fetal skull, classification of the image as normal or abnormal and localization of the lesion. The accuracy was then tested on the test datasets, with performance of segmentation being assessed using precision, recall and Dice's coefficient (DICE), calculated to measure the extent of overlap between human‐labeled and machine‐segmented regions. We assessed classification accuracy by calculating the sensitivity and specificity for abnormal images. Additionally, for 2491 abnormal images, we determined how well each lesion had been localized by overlaying heat maps created by an algorithm on the segmented ultrasound images; an expert judged these in terms of how satisfactory was the lesion localization by the algorithm, classifying this as having been done precisely, closely or irrelevantly. Results: Segmentation precision, recall and DICE were 97.9%, 90.9% and 94.1%, respectively. For classification, the overall accuracy was 96.3%. The sensitivity and specificity for identification of abnormal images were 96.9% and 95.9%, respectively, and the area under the receiver‐operating‐characteristics curve was 0.989 (95% CI, 0.986–0.991). The algorithms located lesions precisely in 61.6% (1535/2491) of the abnormal images, closely in 24.6% (614/2491) and irrelevantly in 13.7% (342/2491). Conclusions: Deep‐learning algorithms can be trained for segmentation and classification of normal and abnormal fetal brain ultrasound images in standard axial planes and can provide heat maps for lesion localization. This study lays the foundation for further research on the differential diagnosis of fetal intracranial abnormalities. Copyright © 2020 ISUOG. Published by John Wiley & Sons Ltd.
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US is time consuming and can require substantial training and experience for specific indications such as fetal echocardiography or neurosonography.
“In clinical settings, US is known to be time consuming and can require a substantial amount of training and experience for specific indications, such as fetal echocardiography or neurosonography [5,6,7].”
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 [6]
The Discussion (p. 7) says fetal-abnormality diagnosis depends heavily on the doctor's experience and that training a qualified sonologist takes a long time. It does not say that ultrasound examinations themselves are time-consuming or address fetal echocardiography specifically. (LLM, conf=0.86)
Experimental — accuracy not yet measuredAI is not meant to replace human work and input, but should assist them and reduce the increasing workload.
“existing literature still emphasizes the fact that AI is not meant to replace human work and input, but should assist them and reduce the increasing workload [3,6].”
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 [6]
The Discussion (p. 7) explicitly says AI should assist humans rather than replace them, and the conclusion (p. 8) says it could help alleviate a shortage of sonologists. The paper does not say that workloads are increasing or establish that AI reduces workload. (LLM, conf=0.84)
Experimental — accuracy not yet measuredXie et al. additionally localized the structural lesions, which lead the algorithm to declare it 'abnormal' and thus recommend the clinician to recheck the labeled area.
“in which Xie et al. additionally localized the structural lesions, which lead the algorithm to declare it ‘abnormal’ and thus recommend the clinician to recheck the labeled area.”
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 [6]
The lesion-localization section (p. 5) describes heat maps and overlay images used to locate abnormalities, and the Discussion (p. 8) says highlighted areas provide doctors with information about where to recheck. But the Discussion explicitly cautions that a heat map shows where the algorithm focused most, not where its classification result was determined. The claim's implication that the localized lesion led the algorithm to declare an image abnormal is therefore not supported. (LLM, conf=0.88)
Experimental — accuracy not yet measuredA limitation of AI US imaging in fetal neurosonography was the problem of miscalculations when US images were not in accordance with the guidelines for standard planes.
“and the problem of miscalculations when US images were not in accordance with the guidelines for standard planes [6,80].”
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 [6]
The Data acquisition section (p. 2) says the study selected images in guideline-based standard axial planes. The Discussion (p. 8) says the algorithms did not yet support other diagnostic planes. The paper does not report miscalculations when images fail to follow standard-plane guidelines, so that specific problem is unsupported. (LLM, conf=0.82)
Experimental — accuracy not yet measuredXie et al. and Sahli et al. reported a method for classifying US images into a binary system of 'normal' and 'abnormal' cases.
“Researchers Xie et al. [6,93] and Sahli et al. [94] reported a method for classifying US images into a binary system of ‘normal’ and ‘abnormal’ cases”
The main limitation of AI models in US imaging described in the summarized literature was that most AI models still need experts for image acquisition, image or ROI selection, and for interpretation of the results.
“The main limitation of AI models in US imaging described in the summarized literature was the fact that most AI models still need experts for image acquisition, image or ROI selection to obtain an adequate image quality for accurate model performance [6,205,209,216], and for interpretation of the results [51,220].”
AI models are primarily intended to assist the clinician, not to replace them.
“AI models are primarily intended to assist the clinician, not to replace them [6,231].”
Artificial neural networks are particularly good at image pattern recognition and can be utilized to improve the speed and accuracy of diagnosing patient conditions in medical fields that depend heavily on images and video, as in ultrasound.
“Artificial neural networks are particularly good at image pattern recognition and can be utilized to improve the speed and accuracy of diagnosing patient conditions in medical fields that depend heavily on images and video, as in the case of ultrasound 11 , 12 .”
From Clinical workflow of sonographers performing fetal anomaly ultrasound scans: Deep-learning-based analysis (2022) · cites this paper as [12]
In fetal ultrasonography, CNNs have shown potential for standard plane recognition, detection, and localization.
“In fetal ultrasonography (US), CNNs have shown potential for standard plane recognition, detection, and localization [19].”
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- Authors
- Xie, H. N., Wang, N., He, M., Zhang, L. H., Cai, H. M., Xian, J. B., Lin, M. F., Zheng, J., & Yang, Y. Z.
- Affiliations
- The First Affiliated Hospital, Sun Yat-sen University (CN)
- Guangzhou Aiyunji Information Technology Co., Ltd Guangdong China
- South China University of Technology (CN)
- Corresponding author
- Xie, H. N.
- Venue
- Ultrasound in Obstetrics & Gynecology
- Publisher
- Wiley
- Published
- 2020-10
- Volume
- 56
- Issue
- 4
- Pages
- 579-587
- ISSN
- 0960-7692 (ISSN-L)
- 1469-0705
- OpenAlex
- W3000155670
- Cited by
- 176
- References
- 25
- Citations by year
- 2026: 26 · 2025: 42 · 2024: 33 · 2023: 25 · 2022: 23 · 2021: 21 · 2020: 6
- FWCI
- 18.02
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Ultrasound in Obstetrics and Gynecology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (61771007, 81571687)
- Topics
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Fetal and Pediatric Neurological Disorders
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- fetal brain ultrasound, deep learning, fetal brain anomalies, lesion localization, heatmaps, normal vs abnormal classification, intracranial anomalies, prenatal diagnosis, computer-aided diagnosis, Dice coefficient, sensitivity and specificity, autopsy confirmation, fetal hydrocephalus
- MeSH terms
- Deep Learning, Brain, Diagnosis, Differential, Feasibility Studies, Female, Fetus, Humans, Image Processing, Computer-Assisted, Nervous System Malformations, Pregnancy, ROC Curve, Sensitivity and Specificity, Ultrasonography, Prenatal
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