Risk assessment of early pregnancy depression based on automated retinal image analysis technology

ZHANG Daqian; QU Yimin; XU Zhongying; LI Zuowei; MA Haihui; YE Qi; JIANG Yu

doi:10.16462/j.cnki.zhjbkz.2025.04.016

Volume 29 Issue 4

Apr. 2025

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Article Navigation > CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION > 2025 > 29(4): 473-480

ZHANG Daqian, QU Yimin, XU Zhongying, LI Zuowei, MA Haihui, YE Qi, JIANG Yu. Risk assessment of early pregnancy depression based on automated retinal image analysis technology[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2025, 29(4): 473-480. doi: 10.16462/j.cnki.zhjbkz.2025.04.016

Citation:

ZHANG Daqian, QU Yimin, XU Zhongying, LI Zuowei, MA Haihui, YE Qi, JIANG Yu. Risk assessment of early pregnancy depression based on automated retinal image analysis technology[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2025, 29(4): 473-480. doi: 10.16462/j.cnki.zhjbkz.2025.04.016

Citation:

ZHANG Daqian, QU Yimin, XU Zhongying, LI Zuowei, MA Haihui, YE Qi, JIANG Yu. Risk assessment of early pregnancy depression based on automated retinal image analysis technology[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2025, 29(4): 473-480. doi: 10.16462/j.cnki.zhjbkz.2025.04.016

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Risk assessment of early pregnancy depression based on automated retinal image analysis technology

doi: 10.16462/j.cnki.zhjbkz.2025.04.016

1.
School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
2.
Centre for Clinical Research and Biostatistic, Jockey Club School of Public Health and Primary Care, the Chinese University of Hong Kong, HongKong 999077, China
3.
Tongzhou Maternal & Child Health Hospital, Beijing 101101, China

Funds:

Naional Natural Science Foundation of China 82404400

More Information

Corresponding author: JIANG Yu, E-mail: jiangyu@pumc.edu.cn
Received Date: 2025-01-07
Rev Recd Date: 2025-03-13
Publish Date: 2025-04-10

Abstract

Abstract

Objective This study aimed to explore the use of machine learning-based retinal image analysis techniques for risk assessment of early pregnancy depression. Methods From May 2023 to May 2024, a total of 936 pregnant women were recruited at Tongzhou District Maternal and Child Health Hospital, Beijing. Bilateral retinal images were captured during their first trimester of pregnancy, and maternal depressive symptoms were assessed using the Chinese version of the Edinburgh postnatal depression scale (EPDS). A case-control matching was performed with an age difference of ±2 years, maintaining a ratio between 1∶1 and 1∶4. Conventional retinal features were calculated using automated retinal image analysis techniques, and specific image features related to early pregnancy depression were extracted using a convolutional neural network model. Modeling was conducted using a logistic regression model, with model performance evaluated via ten-fold cross-validation. Results A total of 449 subjects were included in the analysis, with 92 cases in the early pregnancy depression group. The retinal venous branching angle (69.36±2.17) and venous branching coefficient (0.83±0.01) in the depression group were both lower than those in the non-depression group (69.95±2.00, t=2.47, P=0.014; 0.84±0.01, t=2.56, P=0.011). The area under the receiver operating characteristic curve for the risk assessment model based on retinal features was 0.995 (95% CI: 0.990-0.999), with a sensitivity of 0.978, a specificity of 0.958. Conclusions Automated retinal image analysis techniques can accurately identify early pregnancy depression, providing strong support for early intervention and treatment.
- Early pregnancy depression,
- Retinal images,
- Artificial intelligence,
- Risk assessment

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References(30)

References

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