Multi-objective automatic analysis of lung ultrasound data from COVID-19 patients by means of deep learning and decision trees

COVID-19 raised the need for automatic medical diagnosis, to increase the physicians' efficiency in managing the pandemic. Among all the techniques for evaluating the status of the lungs of a patient with COVID-19, lung ultrasound (LUS) offers several advantages: portability, cost-effectiveness, safety. Several works approached the automatic detection of LUS imaging patterns related COVID-19 by using deep neural networks (DNNs). However, the decision processes based on DNNs are not fully explainable, which generally results in a lack of trust from physicians. This, in turn, slows down the adoption of such systems. In this work, we use two previously built DNNs as feature extractors at the frame level, and automatically synthesize, by means of an evolutionary algorithm, a decision tree (DT) that aggregates in an interpretable way the predictions made by the DNNs, returning the severity of the patients' conditions according to a LUS score of prognostic value. Our results show that our approach performs comparably or better than previously reported aggregation techniques based on an empiric combination of frame-level predictions made by DNNs. Furthermore, when we analyze the evolved DTs, we discover properties about the DNNs used as feature extractors. We make our data publicly available for further development and reproducibility.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

The need for automatic medical diagnosis has been raised by the COVID-19 pandemic to enhance physicians' efficiency. Lung ultrasound (LUS) has advantages such as portability, cost-effectiveness, and safety for evaluating the lung condition of COVID-19 patients. This study utilizes deep neural networks (DNNs) as feature extractors and an evolutionary algorithm to generate a decision tree (DT) that aggregates DNN predictions in an interpretable manner. The results demonstrate that this approach performs as well as or better than previous aggregation techniques based on empirical combinations of DNN predictions.