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由美国国立卫生研究院（NIH）牵头的研究小组与Global Good联合开发出一种可以用来检测宫颈癌前病变的人工智能（AI）技术，甚至可以应用于资源欠发达地区。
这种称为自动视觉评估的AI技术涉及一种计算机算法，用于分析子宫颈的数字图像并准确检测需要就医的癌前病变。
研究人员使用综合数据集“训练”算法，以识别复杂视觉输入中的模式，例如医学图像。
“经过测试，发现图像的计算机算法分析在识别宫颈癌前期病变方面比在显微镜下观察巴氏试验的人类专家评估者更好。”
该研究团队利用来自国家癌症研究所（NCI）的超过六万张宫颈图像进行算法训练，以便区分哪些宫颈疾病需要治疗，哪些宫颈疾病不需要治疗。
医务人员只需使用手机之类的摄像设备进行宫颈检查。此外，这种AI新方法只需极少的培训即可完成宫颈检查，即使在医疗资源有限的地区也可以使用。
本研究的资深作者Mark Schiffman说：“我们的研究结果显示，深度学习算法可以利用常规宫颈癌筛查中收集到的图像来识别癌前病变，而如果这些癌前病变没有及时得到治疗，可能会发展为癌症。
事实上，图像的计算机分析在识别癌前病变方面比在显微镜下观察巴氏试验的人类专家评估者（细胞学）更好。”
该研究团队打算使用各种成像方法，进一步对来自世界各地妇女的宫颈癌前病变和正常宫颈组织的代表性图像进行算法训练。
Global Good执行副总裁Maurizio Vecchione表示：“当这种算法与不断改良的HPV疫苗接种、新兴HPV检测技术以及治疗手段相结合时，即使在资源匮乏的地区，宫颈癌也可以得到控制。”
NIH researchers develop AI technique to detect cervical precancer
A research team led by the National Institutes of Health (NIH), in alliance with Global Good, has developed an artificial intelligence (AI)-based approach to identify cervical precancer, even in low-resource settings.
Dubbed automated visual evaluation, the new technique involves a computer algorithm designed to analyse digital images of the cervix and accurately detect precancerous changes requiring medical attention.
The researchers used comprehensive datasets to ‘train’ the algorithm to recognise patterns in complex visual inputs such as medical images.
“When tested, the computer algorithm was found to be better than a human expert reviewing Pap tests at detecting cervical precancer.”
More than 60,000 cervical images from the National Cancer Institute (NCI) were used to train the algorithm to differentiate between cervical conditions that need treatment and those that do not.
Health workers will only require a camera device such as a cell phone for cervical screening. In addition, the new method can be performed with minimal training, allowing it to be used even in regions with limited health care resources.
Study senior author Mark Schiffman said: “Our findings show that a deep learning algorithm can use images collected during routine cervical cancer screening to identify precancerous changes that, if left untreated, may develop into cancer.
“In fact, the computer analysis of the images was better at identifying precancer than a human expert reviewer of Pap tests under the microscope (cytology).”
The team intends to use various imaging methods to further train the algorithm on representative images of cervical precancers and normal cervical tissue obtained from women across the world.
Global Good executive vice-president Maurizio Vecchione said: “When this algorithm is combined with advances in HPV vaccination, emerging HPV detection technologies, and improvements in treatment, it is conceivable that cervical cancer could be brought under control, even in low-resource setting.”
内容来自：Verdict Medical Devices
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