Artificially Intelligent Olfaction for Fast and Noninvasive Diagnosis of Bladder Cancer from Urine

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.