CASIA OpenIR  > 中国科学院分子影像重点实验室
Development and Validation of a Deep Learning Model to Screen for Trisomy 21 During the First Trimester From Nuchal Ultrasonographic Images
Zhang, Liwen1; Dong, Di1,2; Sun, Yongqing3; Hu, Chaoen1; Sun, Congxin4; Wu, Qingqing3; Tian, Jie1,5
Source PublicationJAMA NETWORK OPEN
ISSN2574-3805
2022-06-21
Volume5Issue:6Pages:11
Corresponding AuthorWu, Qingqing(qingqingwu@ccmu.edu.cn) ; Tian, Jie(jie.tian@ia.ac.cn)
AbstractIMPORTANCE Accurate screening of trisomy 21 in the first trimester can provide an early opportunity for decision-making regarding reproductive choices. OBJECTIVE To develop and validate a deep learning model for screening fetuses with trisomy 21 based on ultrasonographic images. DESIGN, SETTING, AND PARTICIPANTS This diagnostic study used data from all available cases and controls enrolled at 2 hospitals in China between January 2009 and September 2020. Two-dimensional images of the midsagittal plane of the fetal face in singleton pregnancies with gestational age more than 11 weeks and less than 14 weeks were examined. Observers were blinded to subjective fetus nuchal translucency (NT) marker measurements. A convolutional neural network was developed to construct a deep learning model. Data augmentation was applied to generate more data. Different groups were randomly selected as training and validation sets to assess the robustness of the deep learning model. The fetal NT was shown and measured. Each detection of trisomy 21 was confirmed by chorionic villus sampling or amniocentesis. Data were analyzed from March 1, 2021, to January 3, 2022. MAIN OUTCOMES AND MEASURES The primary outcome was detection of fetuses with trisomy 21. The receiver operating characteristic curve, metrics of accuracy, area under the curve (AUC), sensitivity, and specificity were used for model performance evaluation. RESULTS A total of 822 case and control participants (mean [SD] age, 31.9 [4.6] years) were enrolled in the study, including 550 participants (mean [SD] age, 31.7 [4.7] years) in the training set and 272 participants (mean [SD] age, 32.3 [4.7] years) in the validation set. The deep learning model showed good performance for trisomy 21 screening in the training (AUC, 0.98; 95% CI, 0.97-0.99) and validation (AUC, 0.95; 95% CI, 0.93-0.98) sets. The deep learning model had better detective performance for fetuses with trisomy 21 than the model with NT marker and maternal age (training: AUC, 0.82; 95% CI, 0.77-0.86; validation: AUC, 0.73; 95% CI, 0.66-0.80). CONCLUSIONS AND RELEVANCE These findings suggest that this deep learning model accurately screened fetuses with trisomy 21, which indicates that the model is a potential tool to facilitate universal primary screening for trisomy 21.
DOI10.1001/jamanetworkopen.2022.17854
WOS KeywordGASTRIC-CANCER ; MATERNAL AGE ; NASAL BONE ; SURVIVAL ; TRANSLUCENCY ; PREDICTION
Indexed BySCI
Language英语
Funding ProjectMinistry of Science and Technology of China[2017YFA0205200] ; National Key R&D Program of China[2017YFC1309100] ; National Natural Science Foundation of China[82022036] ; National Natural Science Foundation of China[91959130] ; National Natural Science Foundation of China[81971776] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[6202790004] ; National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[8197070119] ; Beijing Natural Science Foundation[L182061] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB 38040200] ; Chinese Academy of Sciences[GJJSTD20170004] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Project of High-Level Talents Team Introduction in Zhuhai City[HLHPTP201703] ; Special Foundation of State Key Laboratory of Complex Systems Management and Control[2022QN03] ; Youth Innovation Promotion Association CAS[2017175]
Funding OrganizationMinistry of Science and Technology of China ; National Key R&D Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Strategic Priority Research Program of Chinese Academy of Sciences ; Chinese Academy of Sciences ; Project of High-Level Talents Team Introduction in Zhuhai City ; Special Foundation of State Key Laboratory of Complex Systems Management and Control ; Youth Innovation Promotion Association CAS
WOS Research AreaGeneral & Internal Medicine
WOS SubjectMedicine, General & Internal
WOS IDWOS:000815246800006
PublisherAMER MEDICAL ASSOC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/49226
Collection中国科学院分子影像重点实验室
Corresponding AuthorWu, Qingqing; Tian, Jie
Affiliation1.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, 95 Zhongguancun E Rd, Beijing 100190, Peoples R China
2.Jinan Univ, Zhuhai Peoples Hosp, Zhuhai Precis Med Ctr, Zhuhai, Peoples R China
3.Capital Med Univ, Beijing Obstet & Gynecol Hosp, Beijing Maternal & Child Hlth Care Hosp, 251 Yaojia Yuan Rd, Beijing 100026, Peoples R China
4.Shijiazhuang Obstet & Gynecol Hosp, Dept Ultrasound, Shijiazhuang, Hebei, Peoples R China
5.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med & Engn, Beijing, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhang, Liwen,Dong, Di,Sun, Yongqing,et al. Development and Validation of a Deep Learning Model to Screen for Trisomy 21 During the First Trimester From Nuchal Ultrasonographic Images[J]. JAMA NETWORK OPEN,2022,5(6):11.
APA Zhang, Liwen.,Dong, Di.,Sun, Yongqing.,Hu, Chaoen.,Sun, Congxin.,...&Tian, Jie.(2022).Development and Validation of a Deep Learning Model to Screen for Trisomy 21 During the First Trimester From Nuchal Ultrasonographic Images.JAMA NETWORK OPEN,5(6),11.
MLA Zhang, Liwen,et al."Development and Validation of a Deep Learning Model to Screen for Trisomy 21 During the First Trimester From Nuchal Ultrasonographic Images".JAMA NETWORK OPEN 5.6(2022):11.
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