A fully automated and accurate assay of rare cell phenotypes in densely-packed fluorescently-labeled liquid biopsy images remains elusive./nMethods: Employing a hybrid artificial intelligence (AI) paradigm that combines traditional rule-based morphological manipulations with modern statistical machine learning, we deployed a next generation software, ALICE (Automated Liquid Biopsy Cell Enumerator) to identify and enumerate minute amounts of tumor cell phenotypes bestrewed in massive populations of leukocytes. As a code designed for futurity, ALICE is armed with internet of things (IOT) connectivity to promote pedagogy and continuing education and also, an advanced cybersecurity system to safeguard against digital attacks from malicious data tampering./nResults: By combining robust principal component analysis, random forest classifier and cubic support vector machine, ALICE was able to detect synthetic, anomalous and tampered input images with an average recall and precision of 0.840 and 0.752, respectively. In terms of phenotyping enumeration, ALICE was able to enumerate various circulating tumor cell (CTC) phenotypes with a reliability ranging from 0.725 (substantial agreement) to 0.961 (almost perfect) as compared to human analysts. Further, two subpopulations of circulating hybrid cells (CHCs) were serendipitously discovered and labeled as CHC-1 (DAPI+/CD45+/E-cadherin+/vimentin-) and CHC-2 (DAPI+ /CD45+/E-cadherin+/vimentin+) in the peripheral blood of pancreatic cancer patients. CHC-1 was found to correlate with nodal staging and was able to classify lymph node metastasis with a sensitivity of 0.615 (95% CI: 0.374-0.898) and specificity of 1.000 (95% CI: 1.000-1.000)./nConclusion: This study presented a machine-learning-augmented rule-based hybrid AI algorithm with enhanced cybersecurity and connectivity for the automatic and flexibly-adapting enumeration of cellular liquid biopsies. ALICE has the potential to be used in a clinical setting for an accurate and reliable enumeration of CTC phenotypes.

中文翻译：

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ALICE：具有增强的连接性和网络安全性的混合AI范例，可以偶然遇到循环混合细胞

密密麻麻的荧光标记液体活检图像中稀有细胞表型的全自动，准确测定仍然难以实现。/n方法：采用混合人工智能（AI）范例，将传统的基于规则的形态学操作与现代统计机器学习相结合，我们部署了下一代软件ALICE（自动液体活检细胞枚举器）来识别和枚举大量白细胞中散布的微量肿瘤细胞表型。至于专为未来性代码，ALICE装备有东西（IOT）的连接，以促进教学和继续教育，也，一个先进的网络安全系统免受恶意tampering./n数据的数字攻击保障互联网的结果：通过结合鲁棒的主成分分析，随机森林分类器和三次支持向量机，ALICE能够检测合成，异常和篡改的输入图像，其平均召回率和精确度分别为0.840和0.752。在表型枚举方面，与人类分析人员相比，ALICE能够枚举各种循环肿瘤细胞（CTC）表型，其可靠性范围为0.725（基本一致）至0.961（几乎完美）。此外，偶然发现了循环杂交细胞（CHC）的两个亚群，并在外围标记为CHC-1（DAPI + / CD45 + / E-cadherin + / vimentin-）和CHC-2（DAPI + / CD45 + / E-cadherin + / vimentin +）胰腺癌患者的血液。发现CHC-1与淋巴结分期相关，并且能够以0的敏感性对淋巴结转移进行分类。结论：本研究提出了一种基于机器学习增强的基于规则的混合AI算法，该算法具有增强的网络安全性和连通性，可以自动灵活地对细胞活检进行枚举。ALICE有潜力用于临床环境中，以准确，可靠地枚举CTC表型。