Explainability of radiomics through formal methods.

BACKGROUND AND OBJECTIVE: Artificial Intelligence has proven to be effective in radiomics. The main problem in using Artificial Intelligence is that researchers and practitioners are not able to know how the predictions are generated. This is currently an open issue because results' explainability is advantageous in understanding the reasoning behind the model, both for patients than for implementing a feedback mechanism for medical specialists using decision support systems. METHODS: Addressing transparency issues related to the Artificial Intelligence field, the innovative technique of Formal methods use a mathematical logic reasoning to produce an automatic, quick and reliable diagnosis. In this paper we analyze results given by the adoption of Formal methods for the diagnosis of the Coronavirus disease: specifically, we want to analyse and understand, in a more medical way, the meaning of some radiomic features to connect them with clinical or radiological evidences. RESULTS: In particular, the usage of Formal methods allows the authors to do statistical analysis on the feature value distributions, to do pattern recognition on disease models, to generalize the model of a disease and to reach high performances of results and interpretation of them. A further step for explainability can be accounted by the localization and selection of the most important slices in a multi-slice approach. CONCLUSIONS: In conclusion, we confirmed the clinical significance of some First order features as Skewness and Kurtosis. On the other hand, we suggest to decline the use of the Minimum feature because of its intrinsic connection with the Computational Tomography exam of the lung.

On the Adoption of Radiomics and Formal Methods for COVID-19 Coronavirus Diagnosis.

Considering the current pandemic, caused by the spreading of the novel Coronavirus disease, there is the urgent need for methods to quickly and automatically diagnose infection. To assist pathologists and radiologists in the detection of the novel coronavirus, in this paper we propose a two-tiered method, based on formal methods (to the best of authors knowledge never previously introduced in this context), aimed to (i) detect whether the patient lungs are healthy or present a generic pulmonary infection; (ii) in the case of the previous tier, a generic pulmonary disease is detected to identify whether the patient under analysis is affected by the novel Coronavirus disease. The proposed approach relies on the extraction of radiomic features from medical images and on the generation of a formal model that can be automatically checked using the model checking technique. We perform an experimental analysis using a set of computed tomography medical images obtained by the authors, achieving an accuracy of higher than 81% in disease detection.