人工智能在泌尿外科影像学诊断的现状及展望

杨龙雨禾; 王跃强; 邱学德; 张贵福; 杨智明; 黄曦; 俞林

doi:10.12122/j.issn.1674-4500.2020.02.10

人工智能在泌尿外科影像学诊断的现状及展望

doi: 10.12122/j.issn.1674-4500.2020.02.10

1.
红河州第一人民医院泌尿外科，云南红河 661199
2.
昆明医科大学第二附属医院泌尿外科，云南昆明 650101

详细信息

作者简介:
杨龙雨禾，硕士，住院医师，E-mail: 394774084@qq.com

通讯作者:
王跃强，主任医师，E-mail: wyq_wsk@163.com

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- 文章访问数: 1049
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- 被引次数: 0
出版历程
- 收稿日期: 2020-03-24
- 刊出日期: 2020-04-15

Application and prospect of artificial intelligence in department of urology

1.
Department of Urology, Honghe First People's Hospital, Honghe 661199, China
2.
Department of Urology, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, China

摘要

摘要: 目前在国内外，人工智能已经在泌尿外科领域的日常医疗保健中有所运用，如通过建立神经网络，从CT结果上预测尿石症的预后，结石清除率，预测术中出血量等，使医生更好地选择治疗方案。通过CT纹理研究，经深度学习和机器学习，准确鉴别肾脏良恶性肿瘤，提高透明细胞癌的检出率，大大降低漏诊及误诊率。使用放射学和纹理特征分析来研究膀胱癌，区分低级别和高级别肿瘤，使医生选定对病人创伤小的术式。并使用算法预测治疗反应，肿瘤复发率，从而延长患者生存率。开发前列腺癌Gleason评分预测、MRI计算机辅助算法诊断，手术结果和生化复发预测等，针对不同病人提供个体化治疗方案。研究发现，这些方法优于传统的统计方法。本文旨在全面回顾近年来人工智能在泌尿外科领域影像学应用的发展，从尿石症、肾肿瘤、膀胱癌、前列腺癌4种常见泌尿系疾病的影像学应用综述，为今后人工智能的临床运用提供更开阔的思路。
- 人工智能 /
- 机器学习 /
- 深度学习 /
- 前列腺癌 /
- 膀胱癌 /
- 肾细胞癌 /
- 尿石症
Abstract: Artificial intelligence (AI) has been used in the daily medical care of Urology field. For example, through the establishment of neural network, the prognosis of urolithiasis can be predicted from the CT results. The stone clearance rate and the amount of intraoperative bleeding can be predicted, so that doctors can better choose the treatment plan. Through the study of CT texture, we can distinguish the benign and malignant renal tumors accurately, improve the detection rate of clear cell carcinoma, and greatly reduce the rate of missed diagnosis and misdiagnosis. The radiology and texture analysis are used to study bladder cancer, distinguish between low-level and high-level tumors, so that doctors can choose the surgical methods with less trauma to patients. The algorithm is used to predict the response to treatment and the recurrence rate of tumor, so as to prolong the survival rate of patients. To develop Gleason score prediction, MRI computer- aided algorithm diagnosis, surgical results and biochemical recurrence prediction for prostate cancer, and provide individualized treatment programs for different patients. It is found that these methods are superior to the traditional statistical methods. The purpose of this paper is to review the development of imaging system application of AI in urology in recent years, and provide a broader idea for clinical application of AI in the future.
- artificial intelligence /
- machine learning /
- deep learning /
- prostate cancer /
- bladder cancer /
- renal cell carcinoma /
- urolithiasis

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