ArticleContrast media & molecular imaging2022
Diagnosis of Early Cervical Cancer with a Multimodal Magnetic Resonance Image under the Artificial Intelligence Algorithm.
Article in Contrast media & molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- High-precision endoscopic optical coherence elastography with a miniaturized catheter for in vivo assessment of endometrial injury.Biomedical optics express · 2026Article
- Artificial intelligence for colposcopic and cytological image analysis in early cervical cancer detection.iScience · 2026Review
- Tumor markers and multimodal magnetic resonance imaging in predicting rectal cancer stage and differentiation.World journal of gastrointestinal oncology · 2025Article
- Endovaginal coil for pelvic high-resolution magnetic resonance imaging of cervical cancer: a preliminary parameter optimization study.Quantitative imaging in medicine and surgery · 2024Article
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4 authors.
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Abstract
This research was conducted to explore the value of multimodal magnetic resonance imaging (MRI) based on the alternating direction algorithm in the diagnosis of early cervical cancer. 64 patients diagnosed with early cervical cancer clinicopathologically were included, and according to the examination methods, they were divided into A group with conventional multimodal MRI examination and B group with the multimodal MRI examination under the alternating direction algorithm. The diagnostic results of two types of multimodal MRI for early cervical cancer staging were compared with the results of clinicopathological examination to judge the application value in the early diagnosis of cervical cancer. The results showed that in the 6 randomly selected samples of early cervical cancer patients, the peak signal-to-noise ratio (PSNR) and structural similarity image measurement (SSIM) of multimodal MRI images under the alternating direction algorithm were significantly higher than those of conventional multimodal MRI images and the image reconstruction was clearer under this algorithm. By comparing MRI multimodal staging, statistical analysis showed that the staging accuracy of B group was 75%, while that of A group was only 59.38%. For the results of postoperative medical examinations, the examination consistency of B group was better than that of A group, with a statistically significant difference (
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