Vesicoureteral reflux detection with reliable probabilistic outputs

Vesicoureteral Reflux (VUR) is a pediatric disorder in which urine flows backwards from the bladder to the upper urinary tract. Its detection is of great importance as it increases the risk of a Urinary Tract Infection, which can then lead to a kidney infection since bacteria may have direct access to the kidneys. Unfortunately the detection of VUR requires a rather painful medical examination, called voiding cysteourethrogram (VCUG), that exposes the child to radiation. In an effort to avoid the exposure to radiation required by VCUG some recent studies examined the use of machine learning techniques for the detection of VUR based on data that can be obtained without exposing the child to radiation. This work takes one step further by proposing an approach that provides lower and upper bounds for the conditional probability of a given child having VUR. The important property of these bounds is that they are guaranteed (up to statistical fluctuations) to contain well-calibrated probabilities with the only requirement that observations are independent and identically distributed (i.i.d.). Therefore they are much more informative and reliable than the plain yes/no answers provided by other techniques.