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Abstract:
: Introduction: Nonmuscle-invasive bladder cancer has a relatively high postoperative recurrence rate despite the implementation of conventional treatment methods. Cystoscopy is essential for diagnosing and monitoring bladder cancer, but lesions are overlooked while using white-light imaging. Using cystoscopy, tumors with a small diameter; flat tumors, such as carcinoma in situ; and the extent of flat lesions associated with the elevated lesions are difficult to identify. In addition, the accuracy of diagnosis and treatment using cystoscopy varies according to the skill and experience of physicians. Therefore, to improve the quality of bladder cancer diagnosis, we aimed to support the cystoscopic diagnosis of bladder cancer using artificial intelligence (AI). Materials and Methods: A total of 2102 cystoscopic images, consisting of 1671 images of normal tissue and 431 images of tumor lesions, were used to create a dataset with an 8:2 ratio of training and test images. We constructed a tumor classifier based on a convolutional neural network (CNN). The performance of the trained classifier was evaluated using test data. True-positive rate and false-positive rate were plotted when the threshold was changed as the receiver operating characteristic (ROC) curve. Results: In the test data (tumor image: 87, normal image: 335), 78 images were true positive, 315 true negative, 20 false positive, and 9 false negative. The area under the ROC curve was 0.98, with a maximum Youden index of 0.837, sensitivity of 89.7%, and specificity of 94.0%. Conclusion: By objectively evaluating the cystoscopic image with CNN, it was possible to classify the image, including tumor lesions and normality. The objective evaluation of cystoscopic images using AI is expected to contribute to improvement in the accuracy of the diagnosis and treatment of bladder cancer.
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SCI期刊coverage:Science Citation Index Expanded(科学引文索引扩展)
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The only peer-reviewed journal completely devoted to the closed, controlled manipulation of the urinary tract. Contains papers on percutaneous renal and ureteral procedures, including extraction of calculi, dilation or incision of strictures, and diagnosis and treatment of urinary tract tumors; ureteroscopy for diagnostic and therapeutic indications, and laparoscopic urological procedures; endoscopic use of lasers, and extracorporeal lithotripsy of renal and ureteral stones. The official journal of the Endourological Society.
唯一一本完全致力于闭合的、可控的尿道操作的同行评议杂志。载有关于经皮肾和输尿管手术的论文,包括结石的取出、狭窄的扩张或切口、尿路肿瘤的诊断和治疗;用于诊断和治疗适应症的输尿管镜检查,以及腹腔镜泌尿外科手术;内窥镜下使用激光和体外碎石术的肾和输尿管结石,是内分泌学会的官方杂志。
大类(学科) | 小类(学科) | 学科排名 |
医学 |
UROLOGY & NEPHROLOGY (泌尿学与肾脏学) 3区 |
38/76 |
年度总发文量 | 年度论文发表量 | 年度综述发表量 |
221 | 203 | 18 |
引文计数(2018)
文献(2015-2017)
1491次引用
769篇文献
序号 | 类别 | 排名 | 百分位 |
1 |
大类(学科):Medicine
小类(学科):Urology
|
#24/97
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影响因子:6.497
ISSN:1172-7047
研究方向:医学-精神病学
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研究方向:医学-精神病学
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研究方向:医学-精神病学
影响因子:5.345
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研究方向:医学-精神病学
影响因子:3.492
ISSN:1540-2002
研究方向:CLINICAL NEUROLOGY-PSYCHIATRY
影响因子:4.562
ISSN:0269-8811
研究方向:医学-精神病学
影响因子:6.533
ISSN:0165-0327
研究方向:医学-精神病学
影响因子:5.415
ISSN:0924-977X
研究方向:医学-精神病学
影响因子:2.493
ISSN:0925-4927
研究方向:医学-精神病学
发表一篇学和医学成像类SCI论文
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