Department of Ultrasound Diagnosis, Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi 830011, Xinjiang, China.
J Healthc Eng. 2021 Nov 29;2021:7538984. doi: 10.1155/2021/7538984. eCollection 2021.
This study aimed to analyze the effect of the deep learning algorithm on ultrasound elastography on the treatment of cervical cancer with clustered regularly interspaced short palindromic repeats (CRISPR) short hairpin ribonucleic acid (shRNA) nanoparticles, aiming to provide a reference for the clinical application of deep learning to analyze the therapeutic effect of the disease. In this study, CRISPR and shRNA plasmid nanoparticle drugs were used to treat 55 patients with cervical cancer in the experimental group, and normal saline was injected to another 53 patients in the control group, so compare the effect of nanoparticles in the treatment of cervical cancer. Professional doctors and the recurrent neural network (RNN) intelligent algorithm were used to score cervical cancer based on the ultrasound elastograph images by taking blue, green, and red (BGR) as diagnosis criteria. As a result, the experimental group had a total of 217 points before drug administration and a total of 224 points after drug administration. Each patient had an average increase of 0.13 points. The control group had a total of 200 points before drug administration and a total of 223 points after drug administration, and each patient had an average increase of 0.43 points. The experimental group was obviously different from the control group ( < 0.05). Each tissue image output by the RNN was clearer than the original image, and the score given by intelligent calculation was faster than that of professional doctors. The monitoring effect of the deep learning RNN intelligent algorithm on the therapeutic effect of nanomedicine was analyzed. It was found that the average accuracy of the experimental group and the control group was 98.95% and 90.34%, respectively; and the experimental group was greatly different from the control group ( < 0.05). In short, nano-CRISPR and shRNA drugs had remarkable effects on the treatment of cervical cancer, and the scores given by the deep learning intelligent algorithm were faster and more accurate, which provided theoretical guidance for the clinical application of deep learning algorithms to analyze the treatment effects of diseases.
本研究旨在分析深度学习算法对聚簇规则间隔短回文重复（CRISPR）短发夹 RNA（shRNA）纳米颗粒超声弹性成像治疗宫颈癌的影响，旨在为深度学习分析疾病治疗效果的临床应用提供参考。在本研究中，实验组用 CRISPR 和 shRNA 质粒纳米颗粒药物治疗 55 例宫颈癌患者，对照组用生理盐水注射治疗 53 例，比较纳米颗粒治疗宫颈癌的效果。专业医生和递归神经网络（RNN）智能算法以蓝、绿、红（BGR）为诊断标准，根据超声弹性图像对宫颈癌进行评分。结果显示，实验组给药前总分为 217 分，给药后总分为 224 分，每位患者平均增加 0.13 分；对照组给药前总分为 200 分，给药后总分为 223 分，每位患者平均增加 0.43 分。实验组与对照组差异有统计学意义（<0.05）。RNN 输出的每个组织图像都比原始图像更清晰，智能计算给出的分数比专业医生更快。分析深度学习 RNN 智能算法对纳米药物治疗效果的监测作用，发现实验组和对照组的平均准确率分别为 98.95%和 90.34%，实验组与对照组差异有统计学意义（<0.05）。总之，纳米 CRISPR 和 shRNA 药物对宫颈癌治疗有显著效果，深度学习智能算法给出的评分更快、更准确，为深度学习算法分析疾病治疗效果的临床应用提供了理论指导。
