Lessons Learned from the Development and Application of Medical Imaging-Based AI Technologies for Combating COVID-19: Why Discuss, What Next

The global COVID-19 pandemic has resulted in huge pressures on healthcare systems, with lung imaging, from chest radiographs (CXR) to computed tomography (CT) and ultrasound (US) of the thorax, playing an important role in the diagnosis and management of patients with coronavirus infection. The AI community reacted rapidly to the threat of the coronavirus pandemic by contributing numerous initiatives of developing AI technologies for interpreting lung images across the different modalities. We performed a thorough review of all relevant publications in 2020 [1] and identified numerous trends and insights that may help in accelerating the translation of AI technology in clinical practice in pandemic times. This workshop is devoted to the lessons learned from this accelerated process and in paving the way for further AI adoption. In particular, the objective is to bring together radiologists and AI experts to review the scientific progress in the development of AI technologies for medical imaging to address the COVID-19 pandemic and share observations regarding the data relevance, the data availability and the translational aspects of AI research and development. We aim at understanding if and what needs to be done differently in developing technologies of AI for lung images of COVID-19 patients, given the pressure of an unprecedented pandemic - which processes are working, which should be further adapted, and which approaches should be abandoned. © 2021, Springer Nature Switzerland AG.

Accelerating Covid-19 Differential Diagnosis with Explainable Ultrasound Image Analysis: An Ai Tool

Introduction Care during the COVID-19 pandemic hinges upon the existence of fast, safe, and highly sensitive diagnostic tools. Ultrasound has practical advantages over other radio-logical modalities and can serve as a globally-available first-line examination technique. However, the specific LUS patterns such as B-lines or subpleural irregularities can be hard to discern, calling into play AI-based image analysis as a support tool for physicians. Methods A LUS dataset of patients with COVID-19, bacterial pneumonias, non-COVID-19 viral pneumonia and healthy volunteers was constructed to assess the value of deep learning methods for the differential diagnosis of COVID-19. We hypothesized that a frame-based convolutional neural network would correctly classify COVID-19 LUS with a high sensitivity and specificity. Results 202 LUS videos were analysed. The frame-based convolutional neural network correctly classified COVID-19 with a sensitivity of 0.90 ± 0.08 and a specificity of 0.96 ± 0.04 (frame-based sensitivity 0.88 ± 0.07, specificity 0.94±0.05). We further employed class activation maps for the spatio-temporal localization of pulmonary biomarkers, which we subsequently validated for human-in-the-loop scenarios in a blindfolded study with medical experts. Aiming for scalability and robustness, we also performed ablation studies comparing mobile friendly, frame- A nd video-based architectures and show reliability of the best model by aleatoric and epistemic uncertainty estimates. We validated our model on an independent test dataset of 39 videos with COVID-19 severity scores and report promising performance (sensitivity 0.806, specificity 0.962). Figure 1 shows the flowchart. Conclusion Our work shows the potential of interpretable AI to serve as a decision support system for diagnosis and thereby provide an accessible and efficient screening method. Further clinical validation of the proposed method is underway. Data and code are publicly available at https://github. com/jannisborn/covid19-pocus-ultrasound.

Accelerating detection of lung pathologies with explainable ultrasound image analysis

Care during the COVID-19 pandemic hinges upon the existence of fast, safe, and highly sensitive diagnostic tools. Considering significant practical advantages of lung ultrasound (LUS) over other imaging techniques, but difficulties for doctors in pattern recognition, we aim to leverage machine learning toward guiding diagnosis from LUS. We release the largest publicly available LUS dataset for COVID-19 consisting of 202 videos from four classes (COVID-19, bacterial pneumonia, non-COVID-19 viral pneumonia and healthy controls). On this dataset, we perform an in-depth study of the value of deep learning methods for the differential diagnosis of lung pathologies. We propose a frame-based model that correctly distinguishes COVID-19 LUS videos from healthy and bacterial pneumonia data with a sensitivity of 0.90 ± 0.08 and a specificity of 0.96 ± 0.04. To investigate the utility of the proposed method, we employ interpretability methods for the spatio-temporal localization of pulmonary biomarkers, which are deemed useful for human-in-the-loop scenarios in a blinded study with medical experts. Aiming for robustness, we perform uncertainty estimation and demonstrate the model to recognize low-confidence situations which also improves performance. Lastly, we validated our model on an independent test dataset and report promising performance (sensitivity 0.806, specificity 0.962). The provided dataset facilitates the validation of related methodology in the community and the proposed framework might aid the development of a fast, accessible screening method for pulmonary diseases. Dataset and all code are publicly available at: https://github.com/BorgwardtLab/covid19_ultrasound. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.