Neuro-symbolic interpretable AI for automatic COVID-19 patient-stratification based on standardised lung ultrasound data

In the current pandemic, being able to efficiently stratify patients depending on their probability to develop a severe form of COVID-19 can improve the outcome of treatments and optimize the use of the available resources. To this end, recent studies proposed to use deep-networks to perform automatic stratification of COVID-19 patients based on lung ultrasound (LUS) data. In this work, we present a novel neuro-symbolic approach able to provide video-level predictions by aggregating results from frame-level analysis made by deep-networks. Specifically, a decision tree was trained, which provides direct access to the decision process and a high-level explainability. This approach was tested on 1808 LUS videos acquired from 100 patients diagnosed as COVID-19 positive by a RT-PCR swab test. Each video was scored by LUS experts according to a 4-level scoring system specifically developed for COVID-19. This information was utilised for both the training and testing of the algorithms. A five-folds cross-validation process was utilised to assess the performance of the presented approach and compare it with results achieved by deep-learning models alone. Results show that this novel approach achieves better performance (82% of mean prognostic agreement) than a threshold-based ensemble of deep-learning models (78% of mean prognostic agreement). © 2022 Acoustical Society of America.

Automatically Scoring Lung Ultrasound Videos of COVID-19 and post-COVID-19 Patients

Lung ultrasound (LUS) imaging is playing an important role in the current pandemic, allowing the evaluation of patients affected by COVID-19 pneumonia. However, LUS is limited to the visual inspection of ultrasound data, which negatively affects the reproducibility and reliability of the findings. For these reasons, we were the first to propose a standardized imaging protocol and a scoring system, from which we developed the first artificial intelligence (AI) models able to evaluate LUS videos. Furthermore, we demonstrated prognostic value of our approach and its utility for patients' stratification. In this study, we report on the level of agreement between AI and LUS clinical experts (MD) on LUS data acquired from both COVID-19 patients and post-COVID-19 patients. © 2022 IEEE.

Automatic Scoring of COVID-19 LUS Videos Using Cross-correlation-Based Features Aggregated from Frame-Level Confidence Levels Obtained by a Pre-trained Deep Neural Network

The emergence of COVID-19 has encouraged researchers to seek a method to detect and monitor patients infected with SARS-CoV 2. The use of lung ultrasound (LUS) in this setting is rapidly spreading because of its portability, cost-effectiveness, real-time imaging, and safety. LUS has demonstrated the potential to be widely used to assess the condition of the lungs in COVID-19 patients. Given frame-level labels provided by a pre-trained deep neural network (DNN), our goal is to identify an aggregation strategy that allows to move from frame-level to video-level, which is the output required by physicians for clinical evaluation. To achieve this goal, we propose a novel aggregation method based on the cross-correlation coefficients. The logic behind this idea is that, based on the similarity between the score's variables (at frame level), the cross-correlation should be informative as to how to discriminate at video level. We applied our approach to the LUS data from a multi-center study comprising of 283, 231, and 448 LUS videos from Lodi General, Gemelli, and San Matteo Hospital, respectively. Results show that the video-level agreement with clinical experts is obtained in 87.6% of the cases, which represents a promising video-level accuracy. © 2022 IEEE.

A Multicenter Study Assessing Artificial Intelligence Capability in Scoring Lung Ultrasound Videos of COVID-19 Patients

Lung ultrasound (LUS) imaging is playing an important role in the current pandemic, allowing the evaluation of patients affected by COVID-19 pneumonia. However, LUS is limited to the visual inspection of ultrasound data, which negatively affects the reproducibility and reliability of the findings. For these reasons, we were the first to propose a standardized imaging protocol and a scoring system, from which we developed the first artificial intelligence (AI) models able to evaluate LUS videos. Furthermore, we demonstrated the prognostic value of our approach and its utility for patients' stratification. In this study, we report on the level of agreement between the AI and LUS clinical experts (MD) when evaluating LUS data. Specifically, in the stratification between patients at high risk of clinical worsening and patients at low risk, the agreement between MDs and AI reached 82%. These encouraging results open to the possibility of exploiting AI for fast and accurate stratification of COVID-19 patients. © 2021 IEEE.