Abstract

Background

With the advent of primary human papillomavirus testing followed by cytology for cervical cancer screening, visual interpretation of cytology slides remains the last subjective analysis step and suffers from low sensitivity and reproducibility.

Methods

We developed a cloud-based whole-slide imaging platform with a deep-learning classifier for p16/Ki-67 dual-stained (DS) slides trained on biopsy-based gold standards. We compared it with conventional Pap and manual DS in 3 epidemiological studies of cervical and anal precancers from Kaiser Permanente Northern California and the University of Oklahoma comprising 4253 patients. All statistical tests were 2-sided.

Results

In independent validation at Kaiser Permanente Northern California, artificial intelligence (AI)-based DS had lower positivity than cytology (P < .001) and manual DS (P < .001) with equal sensitivity and substantially higher specificity compared with both Pap (P < .001) and manual DS (P < .001), respectively. Compared with Pap, AI-based DS reduced referral to colposcopy by one-third (41.9% vs 60.1%, P < .001). At a higher cutoff, AI-based DS had similar performance to high-grade squamous intraepithelial lesions cytology, indicating a risk high enough to allow for immediate treatment. The classifier was robust, showing comparable performance in 2 cytology systems and in anal cytology.

Conclusions

Automated DS evaluation removes the remaining subjective component from cervical cancer screening and delivers consistent quality for providers and patients. Moving from Pap to automated DS substantially reduces the number of colposcopies and also achieves excellent performance in a simulated fully vaccinated population. Through cloud-based implementation, this approach is globally accessible. Our results demonstrate that AI not only provides automation and objectivity but also delivers a substantial benefit for women by reduction of unnecessary colposcopies.

中文翻译：

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基于深度学习的双染色细胞学自动化在宫颈癌筛查中的准确性和效率。

摘要

背景

随着主要的人类乳头瘤病毒测试以及随后的用于宫颈癌筛查的细胞学检查的出现，细胞学载玻片的视觉解释仍然是最后的主观分析步骤，并且灵敏度低且再现性差。

方法

我们开发了一个基于云的全幻灯片成像平台，该平台具有用于基于活检金标准训练的p16 / Ki-67双色（DS）幻灯片的深度学习分类器。我们将其与传统的Pap和手册DS进行了比较，在北加州Kaiser Permanente和俄克拉荷马大学的425例患者的3例宫颈癌和肛门癌的流行病学研究中进行了比较。所有统计检验均为2面检验。

结果

在北加州Kaiser Permanente的独立验证中，基于人工智能（AI）的DS的阳性率低于细胞学（P  <.001）和手动DS（P  <.001），与两种Pap（P  <.001）和手动DS（P  <.001）。与Pap相比，基于AI的DS将阴道镜转诊率降低了三分之一（41.9％对60.1％，P  <.001）。在较高的临界值下，基于AI的DS与高级鳞状上皮内病变细胞学检查具有相似的表现，表明该风险高到可以立即治疗。分类器功能强大，在2种细胞学系统和肛门细胞学中显示出可比的性能。

结论

自动化的DS评估从子宫颈癌筛查中去除了剩余的主观成分，并为医疗服务提供者和患者提供了一致的质量。从Pap转换为自动DS可以大大减少复制的数量，并且在完全接种疫苗的模拟人群中也可以实现出色的性能。通过基于云的实施，此方法可全局访问。我们的结果表明，人工智能不仅可以提供自动化和客观性，而且还可以通过减少不必要的抄本来为女性带来可观的收益。