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Value of Ultrasonic Image Features in Diagnosis of Perinatal Outcomes of Severe Preeclampsia on account of Deep Learning Algorithm
Open accessAbstract
This study is aimed at discussing the value of ultrasonic image features in diagnosis of perinatal outcomes of severe preeclampsia on account of deep learning algorithm. 140 pregnant women singleton with severe preeclampsia were selected as the observation group. At the same time, 140 normal singleton pregnant women were selected as the control group. The hemodynamic indexes were detected by color Doppler ultrasound. The CNN algorithm was used to classify ultrasound images of two groups of pregnant women. The differential scanning calorimetry (DSC), mean pixel accuracy (MPA), and mean intersection of union (MIOU) values of CNN algorithm were 0.9410, 0.9228, and 0.8968, respectively. Accuracy, precision, recall, and F 1 -score were 93.44%, 95.13%, 95.09%, and 94.87%, respectively. The differences were statistically significant ( P < 0.05 ). Compared with the normal control group, the umbilical artery (UA), uterine artery-systolic/diastolic (UTA-S/D), uterine artery (UTA), and digital video (DV) of pregnant women in the observation group were remarkably increased; the minimum alveolar effective concentration (MCA) of the observation group was obviously lower than the MCA of the control group, and the differences between groups were statistically valid ( P < 0.05 ). Logistic regression analysis showed that UA-S/D, UA-resistance index (UA-RI), UTA-S/D, UTA-pulsatility index (UTA-PI), DV-peak velocity index for veins (DV-PVIV), and MCA-S/D were independent risk factors for the outcome of perinatal children with severe preeclampsia. In the perinatal management of severe epilepsy, the combination of the above blood flow indexes to select the appropriate delivery time had positive significance to improve the pregnancy outcome and reduce the perinatal mortality.
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“These algorithms may improve medical diagnosis and potentially reduce clinical workload by replacing the sonographer’s manual tracing [168].”
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 [168]
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“Wang et al. presented an algorithm to assess the color Doppler US images of fetal and maternal vessels as an approach to facilitate the diagnosis of severe preeclampsia linked to the medical outcome [168].”
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Cites
Machine Learning and Deep Learning in Medical Imaging: Intelligent Imaging
Currie, G. M., Hawk, K. E., ROHREN, E. M., Vial, A., & Klein, R. (2019) · Journal of medical imaging and radiation sciences · doi:10.1016/j.jmir.2019.09.005 · Cited by 454
Diriba, K., Awulachew, E., & Getu, E. (2020) · European journal of medical research · doi:10.1186/s40001-020-00439-w · Cited by 240
Sinkey, R. G., Battarbee, A. N., Bello, N. A., Ives, C. W., Oparil, S., & Tita, A. (2020) · Current Hypertension Reports · doi:10.1007/s11906-020-01082-w · Cited by 208
Chappell, L. C., Brocklehurst, P., Green, M. E., Hunter, R. M., Hardy, P., Juszczak, E., Linsell, L., Chiocchia, V., Greenland, M., Placzek, A., et al. (2019) · The Lancet · doi:10.1016/s0140-6736(19)31963-4 · Cited by 199
Preeclampsia: Pathophysiology and management
Ramadas, N., Divyashree, S., Janhavi, P., Peddha, M. S., & Veeranna, R. P. (2020) · Journal of Gynecology Obstetrics and Human Reproduction · doi:10.1016/j.jogoh.2020.101975 · Cited by 192
Hypertensive Disorders of Pregnancy and Future Cardiovascular Health
Melchiorre, K., Thilaganathan, B., Giorgione, V., Ridder, A., Memmo, A., & Khalil, A. (2020) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2020.00059 · Cited by 141
Evidence-Based Revised View of the Pathophysiology of Preeclampsia
Ahmed, A., Rezai, H., & Broadway‐Stringer, S. (2016) · Advances in experimental medicine and biology · doi:10.1007/5584_2016_168 · Cited by 102
Correlation between fetal autopsy and prenatal diagnosis by ultrasound: A systematic review
Rossi, A. C., & Prefumo, F. (2016) · European Journal of Obstetrics & Gynecology and Reproductive Biology · doi:10.1016/j.ejogrb.2016.12.024 · Cited by 88
Metabolomic Profiles of Placenta in Preeclampsia
Kawasaki, K., Kondoh, E., Chigusa, Y., KAWAMURA, Y. L., Mogami, H., Takeda, S., Horie, A., Baba, T., Matsumura, N., Mandai, M., et al. (2018) · Hypertension · doi:10.1161/hypertensionaha.118.12389 · Cited by 72
Automatic segmentation of bone surfaces from ultrasound using a filter-layer-guided CNN
Alsinan, A. Z., Patel, V. M., & Hacihaliloglu, I. (2019) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-019-01934-0 · Cited by 71
Cited by
Jost, E., Kosian, P., Jimenez Cruz, J., Albarqouni, S., Gembruch, U., Strizek, B., & Recker, F. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12216833 · Cited by 44
“These algorithms may improve medical diagnosis and potentially reduce clinical workload by replacing the sonographer’s manual tracing [168].”
An imbalance-aware deep neural network for early prediction of preeclampsia
Bennett, R., Mulla, Z. D., Parikh, P., Hauspurg, A. K., & Razzaghi, T. (2022) · PLoS ONE · doi:10.1371/journal.pone.0266042 · Cited by 36
Liang, Z., Chen, K., Luo, T., Jiang, W., Wen, J., Zhao, L., & Song, W. (2023) · Health Information Science and Systems · doi:10.1007/s13755-023-00225-y · Cited by 14
Preeclampsia prediction via machine learning: a systematic literature review
Özcan, M., & Peker, S. (2024) · Health Systems · doi:10.1080/20476965.2024.2435845 · Cited by 11
Zhang, Z., Liu, F., Zhang, Q., Li, D., & Cai, L. (2023) · Journal of Obstetrics and Gynaecology · doi:10.1080/01443615.2023.2232656 · Cited by 10
Metadata
- Authors
- Wang, Q., Liu, D., & Liu, G.
- Affiliations
- Yidu Central Hospital of Weifang (CN)
- Weifang People's Hospital (CN)
- Corresponding author
- Liu, Guangheng
- Venue
- Computational and Mathematical Methods in Medicine
- Publisher
- Wiley
- Published
- 2022-01-07
- Volume
- 2022
- Pages
- 1-10
- ISSN
- 1748-670X (ISSN-L)
- 1748-6718
- PMC
- PMC8759876
- OpenAlex
- W4205773289
- Cited by
- 11
- References
- 24
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 7 · 2022: 1
- FWCI
- 2.76
- Type
- journal article
- Language
- en
- Status
- Retracted
- Update notices
- retraction (2023-12-06)
- Open access
- hybrid
- doi.org
- Locations
- Computational and Mathematical Methods in Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Weifang Health Commission (WFWSJK-2020-016)
- Topics
- Health Sciences › Medicine › Obstetrics and Gynecology › Pregnancy and preeclampsia studies
- Health Sciences › Medicine › Clinical Neurology › Neurological Complications and Syndromes
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- severe preeclampsia, perinatal outcomes, color Doppler ultrasound, deep learning, convolutional neural networks, umbilical artery, uterine artery, middle cerebral artery, resistance index, pulsatility index, delivery timing, perinatal mortality, risk factors
- MeSH terms
- Deep Learning, Algorithms, Bayes Theorem, China, Female, Hemodynamics, Humans, Infant, Newborn, Pre-Eclampsia, Pregnancy, Pregnancy Outcome, Logistic Models, Ultrasonography, Prenatal, Neural Networks, Computer, Ultrasonography, Doppler, Color, Computational Biology, Perinatal Mortality, Uterine Artery, Support Vector Machine
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- The paper
- Wang, Q., Liu, D., & Liu, G. (2022). Value of Ultrasonic Image Features in Diagnosis of Perinatal Outcomes of Severe Preeclampsia on account of Deep Learning Algorithm. Computational and Mathematical Methods in Medicine. https://doi.org/10.1155/2022/4010339
