Multimodal characterization of optical properties of urinary stones ex vivo by machine-learning classification methods based on autofluorescence and integrating sphere measurements data: feasibility study and preliminary results

Kidney stones are a global problem that cause physical pain and may lead to chronic kidney disease. Recent statistics indicate the incidence of kidney stones is increasing worldwide, and usually varies from 2 to 20% depending on countries¹ and especially on diabetes or obesity incidence in such countries. Intra-operative (i.e. in vivo) characterization of kidney stones is at stake for a better diagnostic management of patients. Such a goal could be achieved by optical methods. The current study aims at evaluating if absorption and scattering coefficients measurements combined to automatic classification based on machine-learning methods could be of interest in assisting urologists with kidney stones characterization. Absorption and scattering coefficients were measured using the inverse adding doubling method (IAD). This method based on solving inverse problem takes as input data measurements acquired on a double integrating spheres optical bench developed in the CRAN laboratory. The dataset is made of absorption and scattering coefficients measured every 10 nm from 535 to 845 nm on 16 kidney stones: 4 kidney stones in each diagnostic class under consideration (1a, 3a, 4c and 5a). Class 3a (5a respectively) kidney stones display the highest (lowest resp.) absorption and scattering coefficients: 3 and 30 mm⁻¹ (1 and 10 mm⁻¹ respectively) at 650 nm. Support-vector machine (SVM) and k-nearest neighbors (k-NN) methods were used to perform automatic classification: k-NN reached 98%-accuracy in the four-class confusion matrix when considering both absorption and scattering coefficients. Although a high intra-class variability was observed and may be seen as the main limitation of the study, this good classification rate is worth taking into account to keep on investigating this method on more kidney stones per class as a potential tool for diagnostic assistance for urologists.