Diagnosis and nursing intervention of gynecological ovarian endometriosis with magnetic resonance imaging under artificial intelligence algorithm
Open accessAbstract
This research was aimed to study the application value of the magnetic resonance imaging (MRI) diagnosis under artificial intelligence algorithms and the effect of nursing intervention on patients with gynecological ovarian endometriosis. 116 patients with ovarian endometriosis were randomly divided into a control group (routine nursing) and an experimental group (comprehensive nursing), with 58 cases in each group. The artificial intelligence fuzzy C-means (FCM) clustering algorithm was proposed and used in the MRI diagnosis of ovarian endometriosis. The application value of the FCM algorithm was evaluated through the accuracy, Dice, sensitivity, and specificity of the imaging diagnosis, and the nursing satisfaction and the incidence of adverse reactions were used to evaluate the effect of nursing intervention. The results showed that, compared with the traditional hard C-means (HCM) algorithm, the artificial intelligence FCM algorithm gave a significantly higher partition coefficient, and its partition entropy and running time were significantly reduced, with significant differences ( P < 0.05 ). The average values of Dice, sensitivity, and specificity of patients’ MRI images were 0.77, 0.73, and 0.72, respectively, which were processed by the traditional HCM algorithm, while those values obtained by the improved artificial intelligence FCM algorithm were 0.92, 0.90, and 0.93, respectively; all the values were significantly improved ( P < 0.05 ). In addition, the accuracy of MRI diagnosis based on the artificial intelligence FCM algorithm was 94.32 ± 3.05%, which was significantly higher than the 81.39 ± 3.11% under the HCM algorithm ( P < 0.05 ). The overall nursing satisfaction of the experimental group was 96.5%, which was significantly better than the 87.9% of the control group ( P < 0.05 ). The incidence of postoperative adverse reactions in the experimental group (7.9%) was markedly lower than that in the control group (24.1%), with a significant difference ( P < 0.05 ). In short, MRI images under the artificial intelligence FCM algorithm could greatly improve the clinical diagnosis of ovarian endometriosis, and the comprehensive nursing intervention would also improve the prognosis and recovery of patients.
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Jiang et al. (35) demonstrated that AI-based FCM algorithms enhanced MRI diagnostics for ovarian endometriosis, increasing accuracy from 63.15% (conventional MRI) to 94.32%.
“For instance, Jiang et al. (35) demonstrated that AI-based FCM algorithms enhanced MRI diagnostics for ovarian endometriosis, increasing accuracy from 63.15% (conventional MRI) to 94.32%.”
From Artificial intelligence in nursing: an integrative review of clinical and operational impacts (2025) · cites this paper as [35]
The 18 included studies were rated as having a moderate risk of bias.
“Overall, the 18 studies were rated as having a moderate risk of bias (29–48).”
From Artificial intelligence in nursing: an integrative review of clinical and operational impacts (2025) · cites this paper as [35]
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Artificial intelligence in nursing: an integrative review of clinical and operational impacts
Hassanein, S., El Arab, R. A., Abdrbo, A., Abu-Mahfouz, M. S., Gaballah, M. K. F., Seweid, M. M., Almari, M., & Alzghoul, H. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1552372 · Cited by 134 · see citing works
- Authors
- Jiang, N., Xie, H., Lin, J., Wang, Y., & Yin, Y.
- Affiliations
- Department of Gynecology, West China Second University Hospital, Chengdu 610041, Sichuan, China
- West China Second University Hospital of Sichuan University (CN)
- Corresponding author
- Yin, Yanan
- Venue
- Computational Intelligence and Neuroscience
- Publisher
- Wiley
- Published
- 2022-06-11
- Volume
- 2022
- Pages
- 1-10
- ISSN
- 1687-5265 (ISSN-L)
- 1687-5273
- PMC
- PMC9206576
- OpenAlex
- W4282016600
- Cited by
- 19
- References
- 22
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 1 · 2023: 3
- FWCI
- 2.95
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Computational Intelligence and Neuroscience (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Health Sciences › Medicine › Reproductive Medicine › Endometriosis Research and Treatment
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Obstetrics and Gynecology › Gynecological conditions and treatments
- Keywords
- endometrioma, magnetic resonance imaging, fuzzy c-means clustering, comprehensive nursing intervention, postoperative adverse reactions, diagnostic accuracy, patient satisfaction
- MeSH terms
- Algorithms, Artificial Intelligence, Endometriosis, Female, Humans, Magnetic Resonance Imaging, Fuzzy Logic
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