Automated Interpretation of Lung Ultrasound for COVID-19 and Tuberculosis diagnosis

Abstract: BACKGROUND. Early and accurate detection of infectious respiratory diseases like COVID-19 and tuberculosis (TB) plays a crucial role in effective management and the reduction of preventable mortality. However, molecular diagnostic tests for these infections are expensive and not easily implementable in resource-limited settings, which suffer the majority of the burden. Lung Ultrasound (LUS) presents a cost-effective alternative for disease detection at the point of care, and its potential can be enhanced through automation using deep learning techniques to overcome the challenges of difficult image interpretation. DeepChest, a neural attention network, has been designed to predict the diagnosis of COVID-19 from LUS images and has shown promising results. AIM. This study aims to further explore the predictive capabilities of DeepChest with an out-of-distribution dataset and extend its application to TB diagnosis. METHODS/FINDINGS. For COVID-19 (resp. TB), this study is based on a main dataset and an out-of-distribution dataset consisting of patients attending an emergency department in Switzerland (resp. an outpatient facility in a TB-endemic region) between February 2020 and March 2021 (resp. between October 2021 and May 2023) with suspected COVID-19 (resp. TB) pneumonia and ground truth labels are RT-PCR. To assess the generalizability of DeepChest for COVID-19 diagnosis, the model trained on the main dataset (296 patients, still LUS images) was tested on an out-of-distribution dataset (135 patients more severely affected, mainly frames extracted from LUS videos). We found that the performance on the out-of-distribution dataset was poor. However, by fine-tuning DeepChest on the latter, the best performance was achieved when using three random frames per video (AUC ROC 0.84 +/- 0.03) instead of one (AUC ROC 0.78 +/- 0.06). To assess the performance of DeepChest for TB diagnosis, the model was trained on the main dataset (386 patients, still LUS images collected in an urban area of Benin). Its performance (AUC ROC 0.92 +/- 0.02) outperformed the LUS expert baseline (AUC ROC 0.84 +/- 0.01) and the clinical baseline (AUC ROC 0.89 +/- 0.01). Additionally, a multimodal model incorporating clinical data alongside LUS images was developed. It achieved the best classification performance (AUC ROC 0.94 +/- 0.01). However, when tested on an out-of-distribution dataset (150 patients, still LUS images collected in a rural region of South Africa, with much more severe presentation), the generalizability of DeepChest was found to be low (AUC ROC 0.64 +/- 0.03). CONCLUSION. The findings of this study are promising, demonstrating the potential of DeepChest for COVID-19 and TB diagnosis using LUS images. Poor generalization to populations with more severe forms of the diseases shows the importance of either collecting more representative samples or ensuring that implementation is constrained to the target population.