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.

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This paper presents a smart “e-nose” device to monitor indoor hazardous air. Indoor hazardous odor is a threat for seniors, infants, children, pregnant women, disabled residents, and patients. To overcome the limitations of using existing non-intelligent, slow-responding, deficient gas sensors, we propose a novel artificial-intelligent-based multiple hazard gas detector (MHGD) system that is mounted on a motor vehicle-based robot which can be remotely controlled. First, we optimized the sensor array for the classification of three hazardous gases, including cigarette smoke, inflammable ethanol, and off-flavor from spoiled food, using an e-nose with a mixing chamber. The mixing chamber can prevent the impact of environmental changes. We compared the classification results of all combinations of sensors, and selected the one with the highest accuracy (98.88%) as the optimal sensor array for the MHGD. The optimal sensor array was then mounted on the MHGD to detect and classify the target gases without a mixing chamber but in a controlled environment. Finally, we tested the MHGD under these conditions, and achieved an acceptable accuracy (70.00%).

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