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

arXiv: https://arxiv.org/abs/2509.10556 · HTML: https://mlsanity.com/paper/2509.10556/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2025.

## Built on by (most cited first, 7 of 7)

- [USF-MAE: Ultrasound Self-Supervised Foundation Model with Masked Autoencoding](https://mlsanity.com/paper/2510.22990/) (2025)
- [UMind-VL: A Generalist Ultrasound Vision-Language Model for Unified Grounded Perception and Comprehensive Interpretation](https://mlsanity.com/paper/2511.22256/) (2025)
- [A texture-based framework for foundational ultrasound models](https://mlsanity.com/paper/2602.01444/) (2026)
- [Hierarchy-Aware and Anatomy-Guided Learning for Lung Ultrasound Video Classification](https://mlsanity.com/paper/2607.17551/) (2026)
- [Which Pretext Task Transfers? Self-Supervised Pretraining Objectives for Lung Ultrasound](https://mlsanity.com/paper/2609.16551/) (2026)
- [UltraBench 2: Towards Robust Evaluation of Vision Foundation Models on Ultrasound](https://mlsanity.com/paper/2609.28610/) (2026)
- [How well do routinely collected demographic and clinical variables aid point-of-care lung ultrasound TB classification](https://mlsanity.com/paper/2610.06034/) (2026)

## Abstract (arXiv)

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. 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. Several AI models were applied and trained for detection and severity of pulmonary infection. 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. 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. To aid future research, we publish the dataset at: https://github.com/NinaWie/COVID-BLUES.