The purpose of this study is to assess the efficacy of Caption LungAI.
Patients over the age of 18
Patients presenting to the hospital or outpatient setting with shortness of breath and suspected B-lines.
Patients in extremis/in whom a research lung ultrasound would not normally be performed due to other priorities
Single group
Caption LungAI
DeviceCaption LungAI is a software that is designed to help non-expert healthcare professionals acquire diagnostic quality images on an 8-zone lung protocol for both healthy patients and patients presenting with pathology such as B-Lines.
Diagnostic Image Quality (Trained Healthcare Professional)
Evaluate the product's ability to assist the user to capture a LUS image with diagnostic quality. Percentage of zones with diagnostic image quality \> 0.80
B-Lines Detection
Evaluate the product's ability to retrospectively detect present B-lines on images acquired by a local expert with no Caption Lung AI assistance. AUROC \> 0.80; Sensitivity (Se) \> 0.80; Specificity (Sp) \> 0.75
B-Line Significance
Evaluate the product's ability to retrospectively detect B-lines of significant severity on images acquired by a local expert with no Caption Lung AI assistance. AUROC \> 0.80; Sensitivity (Se) \> 0.80; Specificity (Sp) \> 0.75
Remote Reader Performance
Evaluate the product's impact on a remote expert reader's ability to interpret LUS images. Difference Between Aided \& Unaided Groups: AUROC \> 0.00; Sensitivity (Se) \> 0.00; Specificity (Sp) \> 0.00
Sub-group Analyses
The following sub-group analyses will be performed for the primary endpoints: age (\< 65, ≥ 65), BMI group (\< 25, 25 ≤ BMI \< 30, ≥ 30), gender (Male / Female), site location, trained HCP, and zone of the image (1 - 8).
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