Explainable AI for COVID-19 prognosis from early Chest X-ray and clinical data in the context of the COVID-CXR international hackathon

Although many AI-based scientific works regarding chest X-ray (CXR) interpretation focused on COVID-19 diagnosis, fewer papers focused on other relevant tasks, like severity estimation, deterioration, and prognosis. The same holds for explainable decisions to estimate COVID-19 prognosis as well. The international hackathon launched during Dubai Expo 2020, aimed at designing machine learning solutions to help physicians formulate COVID-19 patients' prognosis, was the occasion to develop a machine learning model capable of predicting such prognoses and justifying them through interpretable explanations. The large hackathon dataset comprised subjects characterized by their CXR and numerous clinical features collected during triage. To calculate the prognostic value, our model considered both patients' CXRs and clinical features. After automatic pre-processing to improve their quality, CXRs were processed by a Deep Learning model to estimate the lung compromise degree, which has been considered as an additional clinical feature. Original clinical parameters suffered from missing values that were adequately handled. We trained and evaluated multiple models to find the best one and fine-tune it before the inference process. Finally, we produced novel explanations, both visual and numerical, to justify the model predictions. Ultimately, our model processes a CXR and several clinical data to estimate a patient's prognosis related to the COVID-19 disease. It proved to be accurate and was ranked second in the final rankings with 75%, 73.9%, and 74.4% in sensitivity, specificity, and balanced accuracy, respectively. In terms of model explainability, it was ranked first since it was agreed to be the most interpretable by health professionals. © 2023 SPIE.

Let the data speak: analysing data from multiple health centers of the São Paulo metropolitan area for COVID-19 clinical deterioration prediction

With the spread of different COVID-19 variants in the Brazilian territory, the national health system has been facing a constant overload. Using data from five different health centers located in the Sao Paulo metropolitan area, this work seeks to identify key common factors associated with the prognosis of COVID-19 severity. The proxies for severity considered are hospitalization time, death and use of mechanical ventilation. The induced models predicted ob-jective short-term COVID-19 clinical deterioration outcomes with AUC, sensitivity and specificity up to 0.880, 0.824 and 0.833, respectively. Parameters such as C-reactive protein and percentage of neutrophils have shown most influence on the predictions. Given the nature of the lab tests highlighted, we note that innate inflammatory status in admission can play a significant role in patient outcome. © 2022 IEEE.

Data Driven Estimation of Covid-19 Prognosis

Continuous spread of novel coronavirus (COVID-19) and availability of limited resources force the severity-based allocation of resources. While it is essential to have a reliable severity assessment method, it is even more critical to have a prognosis model to estimate infection progress in individuals. An accurate estimate of infection progression would naturally help in optimized treatment and morbidity reduction. We aim at the prognosis of the COVID-19 infections including, ground-glass opacities, consolidation, and pleural effusion, from the longitudinal chest X-ray (CXR) images of the patient. For this purpose, we first propose a learning-based framework that predicts infection type from a given CXR image. This helps in finding low dimensional embeddings of CXR images, which we use in a recurrent learning framework to predict the type of infection for the subsequent days. We achieve a test AUC of 0.85 for infection type prediction and a test AUC of 0.88 for prognosis on the benchmark COVID-19 dataset. © 2022 IEEE.

Detecting Early Signs of Insufficiency in COVID-19 Patients from CBC Tests Through a Supervised Learning Approach

One important task in the COVID-19 clinical protocol involves the constant monitoring of patients to detect possible signs of insufficiency, which may eventually rapidly progress to hepatic, renal or respiratory failures. Hence, a prompt and correct clinical decision not only is critical for patients prognosis, but also can help when making collective decisions regarding hospital resource management. In this work, we present a network-based high-level classification technique to help healthcare professionals on this activity, by detecting early signs of insufficiency based on Complete Blood Count (CBC) test results. We start by building a training dataset, comprising both CBC and specific tests from a total of 2,982 COVID-19 patients, provided by a Brazilian hospital, to identify which CBC results are more effective to be used as biomarkers for detecting early signs of insufficiency. Basically, the trained classifier measures the compliance of the test instance to the pattern formation of the network constructed from the training data. To facilitate the application of the technique on larger datasets, a network reduction option is also introduced and tested. Numerical results show encouraging performance of our approach when compared to traditional techniques, both on benchmark datasets and on the built COVID-19 dataset, thus indicating that the proposed technique has potential to help medical workers in the severity assessment of patients. Especially those who work in regions with scarce material resources. © 2021, Springer Nature Switzerland AG.