Automated <scp>AI</scp> ‐Based Lung Disease Classification Using Point‐of‐Care Ultrasound
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT Timely and accurate diagnosis of lung diseases is critical for reducing related morbidity and mortality. Lung ultrasound (LUS) has emerged as a useful point‐of‐care tool for evaluating various lung conditions. However, interpreting LUS images remains challenging due to operator‐dependent variability, low image quality, and limited availability of experts in many regions. In this study, we present a lightweight and efficient deep learning model, ParSE‐CNN, alongside fine‐tuned versions of VGG‐16, InceptionV3, Xception, and Vision Transformer architectures, to classify LUS images into three categories: COVID‐19, other lung pathology, and healthy lung. Models were trained using data from public sources and Ugandan healthcare facilities, and evaluated on a held‐out Ugandan dataset. Fine‐tuned VGG‐16 achieved the highest classification performance with 98% accuracy, 97% precision, 98% recall, and a 97% F1‐score. ParSE‐CNN yielded a competitive accuracy of 95%, precision of 94%, recall of 95%, and F1‐score of 97% while offering a 58.3% faster inference time (0.006 s vs. 0.014 s) and a lower parameter count (5.18 M vs. 10.30 M) than VGG‐16. To enhance input quality, we developed a preprocessing pipeline, and to improve interpretability, we employed Grad‐CAM heatmaps, which showed high alignment with radiologically relevant features. Finally, ParSE‐CNN was integrated into a mobile LUS workflow with a PC backend, enabling real‐time AI‐assisted diagnosis at the point of care in low‐resource settings.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it