# Accelerating Detection of Lung Pathologies with Explainable Ultrasound Image Analysis
###### Abstract  
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 \(0.90\pm 0.08\) and a specificity of \(0.96\pm 0.04\). 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](https://github.com/BorgwardtLab/covid19_ultrasound)]([https://github.com/BorgwardtLab/covid19_ultrasound](https://github.com/BorgwardtLab/covid19_ultrasound)). computer vision; Convolutional neural network; COVID-19; deep learning; interpretability; pneumonia; Lung imaging; machine learning; medical imaging; ultrasound; supervised learning +
Footnote †: journal: _Sci. 2021_, 11, 672. [[https://doi.org/10.3390/app11020672](https://doi.org/10.3390/app11020672)]([https://doi.org/10.3390/app11020672](https://doi.org/10.3390/app11020672))  
Footnote †: journal: _Sci. 2021_, 111, 672. [[https://doi.org/10.3390/app11020672](https://doi.org/10.3390/app11020672)]([https://doi.org/10.3390/app11020672](https://doi.org/10.3390/app11020672))  
Footnote †: journal: _Sci. techniques for early disease diagnosis to successfully fight the spread [1]. The standard genetic test (RT-PCR ) has a processing time of up to 2 days [2]. Several publications have reported sensitivity as low as 70% [3,4] and a meta-analysis estimated the _false negative_ rate to be at least 20% over the course of the infection [5]. Medical imaging complements the diagnostic process that can guide further PCR-testing, especially in triage situations [6]. CT (Computed Tomography) scanning is the imaging gold standard for pulmonary diseases [7] and is considered reliable for COVID-19 diagnosis in some countries [4,8,9], although a significant amount of patients exhibit normal CT scans [10]. However, CT scanning is expensive and highly irradiating, carries significant risk of cross infection to healthcare workers and requires extensive, time-consuming sterilization [11]. It is furthermore reserved for developed countries; there are only \(\sim\)30 k CT scanners globally [12]. A chest X-ray (CXR) is the most common first line procedure in diagnostic imaging, despite reports of low specificity and sensitivity for COVID-19 (for example, Reference [13] found 89% normal CXR in 493 COVID-19 patients). Ultrasound (US), by contrast, is a cheap, safe, non-invasive and repeatable technique that can be performed with portable devices at patient bedside and is ubiquitously available around the globe [14]. Lung ultrasound (LUS) developed into an established tool to diagnose pulmonary diseases [15,16,17], as it is superior to CXR for detecting pulmonary conditions [18,19,20,21].