Globally, bladder cancer (BLC) is one of the most common cancers and has a high recurrence and mortality rate. Current clinical diagnostic approaches are either invasive or inaccurate. Here, we report on a cost-efficient, artificially intelligent chemiresistive sensor array made of polyaniline (PANI) derivatives that can noninvasively diagnose BLC at an early stage and maintain postoperative surveillance through ″smelling″ clinical urine samples at room temperature. In clinical trials, 18 healthy controls and 76 BLC patients (60 and 16 at early and advanced stages, respectively) are assessed by the artificial olfactory system. With the assistance of a support vector machine (SVM), very high sensitivity and accuracy from healthy controls are achieved, exceeding those obtained by the current techniques in practice. In addition, the recurrences of both early and advanced stages are diagnosed well, with the effect of confounding factors on the performance of the artificial olfactory system found to have a negligible influence on the diagnostic performance. Overall, this study contributes a novel, noninvasive, easy-to-use, inexpensive, real-time, accurate method for urine disease diagnosis, which can be useful for personalized care/diagnosis and postoperative surveillance, resulting in saving more lives.

中文翻译：

---

人工智能嗅觉从尿液中快速、无创地诊断膀胱癌

在全球范围内，膀胱癌（BLC）是最常见的癌症之一，具有较高的复发率和死亡率。当前的临床诊断方法要么是侵入性的，要么是不准确的。在这里，我们报告了一种由聚苯胺 (PANI) 衍生物制成的具有成本效益的人工智能化学电阻传感器阵列，该阵列可以在早期无创诊断 BLC，并通过在室温下“闻”临床尿液样本来维持术后监测。在临床试验中，人工嗅觉系统评估了 18 名健康对照和 76 名 BLC 患者（早期和晚期分别为 60 名和 16 名）。在支持向量机 (SVM) 的帮助下，从健康控制中获得了非常高的灵敏度和准确性，超过了当前技术在实践中获得的那些。此外，早期和晚期的复发都得到了很好的诊断，混杂因素对人工嗅觉系统性能的影响对诊断性能的影响可以忽略不计。总体而言，本研究为尿液疾病诊断提供了一种新颖、无创、易于使用、廉价、实时、准确的方法，可用于个性化护理/诊断和术后监测，从而挽救更多生命。