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. 2025 Sep;29(9):6301-6310.
doi: 10.1109/JBHI.2025.3543686.

COVID-BLUeS - a Prospective Study on the Value of AI in Lung Ultrasound Analysis

Nina Wiedemann, Dianne de Korte-de Boer, Matthias Richter, Sjors van de Weijer, Charlotte Buhre, Franz A M Eggert, Sophie Aarnoudse, Lotte Grevendonk, Steffen Rober, Carlijn M E Remie, Wolfgang Buhre, Ronald Henry, Jannis Born

COVID-BLUeS - a Prospective Study on the Value of AI in Lung Ultrasound Analysis

Nina Wiedemann et al. IEEE J Biomed Health Inform. 2025 Sep.

Abstract

As a lightweight and non-invasive imaging technique, lung ultrasound (LUS) has gained importance for assessing lung pathologies. The use of Artificial intelligence (AI) in medical decision support systems is promising due to the time- and expertise-intensive interpretation, however, due to the poor quality of existing data used for training AI models, their usability for real-world applications remains unclear.

Methods: In a prospective study, we analyze data from 63 COVID-19 suspects (33 positive) collected at Maastricht University Medical Centre. Ultrasound recordings at six body locations were acquired following the BLUE protocol and manually labeled for severity of lung involvement. Anamnesis and complete blood count (CBC) analyses were conducted. Several AI models were applied and trained for detection and severity of pulmonary infection.

Results: The severity of the lung infection, as assigned by human annotators based on the LUS videos, is not significantly different between COVID-19 positive and negative patients ($p = 0.89$). Nevertheless, the predictions of image-based AI models identify a COVID-19 infection with 65% accuracy when applied zero-shot (i.e., trained on other datasets), and up to 79% with targeted training, whereas the accuracy based on human annotations is at most 65% . Multi-modal models combining images and CBC improve significantly over image-only models.

Conclusion: Although our analysis generally supports the value of AI in LUS assessment, the evaluated models fall short of the performance expected from previous work. We find this is due to 1) the heterogeneity of LUS datasets, limiting the generalization ability to new data, 2) the frame-based processing of AI models ignoring video-level information, and 3) lack of work on multi-modal models that can extract the most relevant information from video-, image- and variable-based inputs. The dataset is publicly available at https://github.com/NinaWie/COVID-BLUES.

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