Automated system for diagnosing endometrial cancer by adopting deep-learning technology in hysteroscopy.
Automated system for diagnosing endometrial cancer by adopting deep-learning technology in hysteroscopy.
复制标题
宫腔镜术中采用深度学习技术的子宫内膜癌自动诊断系统。
DOI：
10.1371/journal.pone.0248526
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发表时间：
2021
期刊：
影响因子：
3.7
通讯作者：
Fujii T
中科院分区：
文献类型：
--
作者：
Takahashi Y;Sone K;Noda K;Yoshida K;Toyohara Y;Kato K;Inoue F;Kukita A;Taguchi A;Nishida H;Miyamoto Y;Tanikawa M;Tsuruga T;Iriyama T;Nagasaka K;Matsumoto Y;Hirota Y;Hiraike-Wada O;Oda K;Maruyama M;Osuga Y;Fujii T
子宫内膜癌是一种普遍存在的妇科疾病，全球发病率不断上升。因此，尽管迄今为止缺乏成熟的筛查技术，但子宫内膜癌的早期诊断至关重要。本文提出了一种基于人工智能的宫腔镜图像自动检测子宫内膜癌影响区域的系统。本研究招募有宫腔镜检查史的177例患者（子宫内膜正常60例，子宫肌瘤21例，子宫内膜息肉60例，不典型子宫内膜增生15例，子宫内膜癌21例）。采用基于三种流行的深度神经网络模型的机器学习技术，并开发了一种连续性分析方法来提高癌症诊断的准确性。最后，我们研究了将所有训练好的模型结合起来是否可以提高准确率。结果表明，采用标准方法诊断准确率约为80%(78.91 ~ 80.93%)，采用本文提出的连续性分析方法和三种神经网络联合诊断准确率分别提高到89%（83.94 ~ 89.13%）和90%以上（即90.29%）。相应的灵敏度和特异性分别为91.66%和89.36%。这些发现表明，所提出的方法足以在不久的将来促进子宫内膜癌的及时诊断。
Endometrial cancer is a ubiquitous gynecological disease with increasing global incidence. Therefore, despite the lack of an established screening technique to date, early diagnosis of endometrial cancer assumes critical importance. This paper presents an artificial-intelligence-based system to detect the regions affected by endometrial cancer automatically from hysteroscopic images. In this study, 177 patients (60 with normal endometrium, 21 with uterine myoma, 60 with endometrial polyp, 15 with atypical endometrial hyperplasia, and 21 with endometrial cancer) with a history of hysteroscopy were recruited. Machine-learning techniques based on three popular deep neural network models were employed, and a continuity-analysis method was developed to enhance the accuracy of cancer diagnosis. Finally, we investigated if the accuracy could be improved by combining all the trained models. The results reveal that the diagnosis accuracy was approximately 80% (78.91–80.93%) when using the standard method, and it increased to 89% (83.94–89.13%) and exceeded 90% (i.e., 90.29%) when employing the proposed continuity analysis and combining the three neural networks, respectively. The corresponding sensitivity and specificity equaled 91.66% and 89.36%, respectively. These findings demonstrate the proposed method to be sufficient to facilitate timely diagnosis of endometrial cancer in the near future.
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影响因子：
64.8
作者：
Esteva A;Kuprel B;Novoa RA;Ko J;Swetter SM;Blau HM;Thrun S
通讯作者：
Thrun S
影响因子：
2.6
作者：
Anderson, Annie S.;Key, Timothy J.;Romieu, Isabelle
通讯作者：
Romieu, Isabelle
DOI：
10.1196/annals.1310.020
发表时间：
2004-01-01
期刊：
APPLICATIONS OF BIOINFORMATICS IN CANCER DETECTION
影响因子：
--
作者：
McCarthy, JF;Marx, KA;Hotchkiss, J
通讯作者：
Hotchkiss, J
影响因子：
2.9
作者：
Hinton, Geoffrey E.;Osindero, Simon;Teh, Yee-Whye
通讯作者：
Teh, Yee-Whye
DOI：
10.1111/j.1365-2303.2008.00581.x
发表时间：
2009-12
期刊：
Cytopathology : official journal of the British Society for Clinical Cytology
影响因子：
--
作者：
Yanoh K;Norimatsu Y;Hirai Y;Takeshima N;Kamimori A;Nakamura Y;Shimizu K;Kobayashi TK;Murata T;Shiraishi T
通讯作者：
Shiraishi T