Application of Transfer Learning and Feature Fusion Algorithms to Improve the Identification and Prediction Efficiency of Premature Ovarian Failure
Open accessAbstract
Ultrasound imaging technology has the advantages of noninvasiveness, real-time, low price, and easy operation. It is one of the most used diagnostic tools for early detection and classification of premature ovarian failure. Although the rapid development of computer-aided diagnosis has provided a great help to the ultrasound diagnosis of premature ovarian failure, it still has many limitations and shortcomings, so this paper adopts transfer learning and feature fusion algorithms to improve the identification and prediction efficiency of premature ovarian failure. In this study, the POF group and the control group both adopted a unified scale. From the four aspects of sociological characteristics, past medical history, environmental factors, and living habits, a dedicated person asked and filled out the scale face to face. All patients participating in the experiment underwent ultrasound examinations. In this paper, the bottom-level feature fusion method is used to improve classification performance. The experiment uses 100 epochs. After each epoch training is completed, we used all the data and labels of the target domain to test. All experiments were performed five times, and the result is the average of five experiments. All the results of baseline and direct classification without migration use the average of five experimental results as the result. Migrating the features extracted by the InceptionV3 network has the best performance for predicting premature ovarian failure. Its classification accuracy is as high as 85.13%, and the F1 value is 0.78. The results show that the migration learning and feature fusion algorithms used in this paper can provide reliable predictive analysis and decision support for doctors in the diagnosis of premature ovarian failure.
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This review lists two studies on premature ovarian failure, evidencing that ovarian artery flow parameters obtained by AI analyzed color Doppler imaging can be used as a predictive factor.
“This review lists two studies on this topic, evidencing that ovarian artery flow parameters obtained by AI analyzed color Doppler imaging can be used as a predictive factor”
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 [218]
The Results and Discussion report that three-dimensional ultrasound VI and FI parameters had diagnostic accuracy for premature ovarian failure, and the Conclusions describe an AI-based prediction model using ultrasound features (pages not provided). The copy does not establish that these were ovarian artery flow parameters obtained through AI analysis of color Doppler images, or that those particular parameters were predictors in the AI model. (LLM, conf=0.82)
Experimental — accuracy not yet measuredIn total, 11 articles were summarized in the other applications in gynecology section, including endometriosis, premature ovarian failure, uterine fibroids, follicle tracking, and ectopic pregnancies.
“In total, 11 articles were summarized in this section, including the topics of endometriosis [216,217], premature ovarian failure [218,219], uterine fibroids [220,221], follicle tracking [222,223], and ectopic pregnancies [224,225].”
Both AI models for premature ovarian failure showed reliability for disease prediction.
“both AI models showed reliability for disease prediction [210,218].”
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Retracted
- Authors
- Zhang, Y., Hou, J., Wang, Q., Hou, A., & Liu, Y.
- Affiliations
- Yanan University Affiliated Hospital (CN)
- Corresponding author
- Hou, Aiqin
- Venue
- Journal of Healthcare Engineering
- Publisher
- Wiley
- Published
- 2022-03-27
- Volume
- 2022
- Pages
- 1-10
- ISSN
- 2040-2295 (ISSN-L)
- 2040-2309
- PMC
- PMC8977322
- OpenAlex
- W4220942123
- Cited by
- 6
- References
- 25
- Citations by year
- 2024: 1 · 2023: 4 · 2022: 1
- FWCI
- 1.12
- Type
- journal article
- Language
- en
- Status
- Retracted
- Update notices
- retraction (2023-10-11)
- Open access
- hybrid
- doi.org
- Locations
- Journal of Healthcare Engineering (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Shaanxi Administration of Traditional Chinese Medicine (2021-ZZ-LC023, 21JK0974, 2019JQ-986, 2018ZD-02)
- Education Department of Shaanxi Province (2021-ZZ-LC023, 21JK0974, 2019JQ-986, 2018ZD-02)
- Natural Science Basic Research Program of Shaanxi Province (2021-ZZ-LC023, 21JK0974, 2019JQ-986, 2018ZD-02)
- Affiliated Hospital of Yan’an University (2021-ZZ-LC023, 21JK0974, 2019JQ-986, 2018ZD-02)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- premature ovarian failure, transfer learning, feature fusion, ovarian ultrasonography, computer-aided diagnosis, InceptionV3, clinical risk factors
- MeSH terms
- Machine Learning, Algorithms, Diagnosis, Computer-Assisted, Humans, Ultrasonography, Primary Ovarian Insufficiency
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