How much can AI see in early pregnancy: A multi-center study of fetus head characterization in week 10–14 in ultrasound using deep learning
|Corresponding Author||Cui，Chen; She，Ruilian|
Purpose: To investigate if artificial intelligence can identify fetus intracranial structures in pregnancy week 11–14; to provide an automated method of standard and non-standard sagittal view classification in obstetric ultrasound examination Method and materials: We proposed a newly designed scheme based on deep learning (DL) – Fetus Framework to identify nine fetus intracranial structures: thalami, midbrain, palate, 4th ventricle, cisterna magna, nuchal translucency (NT), nasal tip, nasal skin, and nasal bone. Fetus Framework was trained and tested on a dataset of 1528 2D sagittal-view ultrasound images from 1519 females collected from Shenzhen People's Hospital. Results from Fetus Framework were further used for standard/non-standard (S-NS) plane classification, a key step for NT measurement and Down Syndrome assessment. S-NS classification was also tested with 156 images from the Longhua branch of Shenzhen People's Hospital. Sensitivity, specificity, and area under the curve (AUC) were evaluated for comparison among Fetus Framework, three classic DL models, and human experts with 1-, 3- and 5-year ultrasound training. Furthermore, 4 physicians with more than 5 years of experience conducted a reader study of diagnosing fetal malformation on a dataset of 316 standard images confirmed by the Fetus framework and another dataset of 316 standard images selected by physicians. Accuracy, sensitivity, specificity, precision, and F1-Score of physicians’ diagnosis on both sets are compared. Results: Nine intracranial structures identified by Fetus Framework in validation are all consistent with that of senior radiologists. For S-NS sagittal view identification, Fetus Framework achieved an AUC of 0.996 (95%CI: 0.987, 1.000) in internal test, at par with classic DL models. In external test, FF reaches an AUC of 0.974 (95%CI: 0.952, 0.995), while ResNet-50 arrives at AUC∼0.883, 95% CI 0.828–0.939, Xception AUC∼0.890, 95% CI 0.834–0.946, and DenseNet-121 AUC∼0.894, 95% CI 0.839–0.949. For the internal test set, the sensitivity and specificity of the proposed framework are (0.905, 1), while the first-, third-, and fifth-year clinicians are (0.619, 0.986), (0.690, 0.958), and (0.798, 0.986), respectively. For the external test set, the sensitivity and specificity of FF is (0.989, 0.797), and first-, third-, and fifth-year clinicians are (0.533, 0.875), (0.609, 0.844), and (0.663, 0.781), respectively.On the fetal malformation classification task, all physicians achieved higher accuracy and F1-Score on Fetus selected standard images with statistical significance (p < 0.01). Conclusion: We proposed a new deep learning-based Fetus Framework for identifying key fetus intracranial structures. The framework was tested on data from two different medical centers. The results show consistency and improvement from classic models and human experts in standard and non-standard sagittal view classification during pregnancy week 11–13+6. Clinical relevance/application: With further refinement in larger population, the proposed model can improve the efficiency and accuracy of early pregnancy test using ultrasound examination.
First ; Corresponding
Science, Technology and Innovation Commission of Shenzhen Municipality[GJHZ20200731095401004];
|WOS Research Area|
Computer Science ; Engineering ; Medical Informatics
Computer Science, Interdisciplinary Applications ; Computer Science, Theory & Methods ; Engineering, Biomedical ; Medical Informatics
|WOS Accession No|
|ESI Research Field|
Cited Times [WOS]:0
|Document Type||Journal Article|
|Department||Shenzhen People's Hospital|
1.Department of Ultrasound,Shenzhen People's Hospital,The Second Clinical Medical College,Jinan University,The First Affiliated Hospital,Southern University of Science and Technology,Shenzhen,Guangdong,China
2.Department of Obstetric,Shenzhen People's Hospital,The Second Clinical Medical College,Jinan University,The First Affiliated Hospital,Southern University of Science and Technology),Shenzhen,Guangdong,China
|First Author Affilication||Shenzhen People's Hospital|
|Corresponding Author Affilication||Shenzhen People's Hospital|
|First Author's First Affilication||Shenzhen People's Hospital|
Lin，Qi,Zhou，Yuli,Shi，Siyuan,等. How much can AI see in early pregnancy: A multi-center study of fetus head characterization in week 10–14 in ultrasound using deep learning[J]. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE,2022,226.
Lin，Qi.,Zhou，Yuli.,Shi，Siyuan.,Zhang，Yujuan.,Yin，Shaoli.,...&Dong，Fajin.(2022).How much can AI see in early pregnancy: A multi-center study of fetus head characterization in week 10–14 in ultrasound using deep learning.COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE,226.
Lin，Qi,et al."How much can AI see in early pregnancy: A multi-center study of fetus head characterization in week 10–14 in ultrasound using deep learning".COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 226(2022).
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