Prospective Real-Time Validation of a Lung Ultrasound Deep Learning Model in the ICU.
Crit Care Med
; 51(2): 301-309, 2023 02 01.
Article
in En
| MEDLINE
| ID: mdl-36661454
ABSTRACT
OBJECTIVES:
To evaluate the accuracy of a bedside, real-time deployment of a deep learning (DL) model capable of distinguishing between normal (A line pattern) and abnormal (B line pattern) lung parenchyma on lung ultrasound (LUS) in critically ill patients.DESIGN:
Prospective, observational study evaluating the performance of a previously trained LUS DL model. Enrolled patients received a LUS examination with simultaneous DL model predictions using a portable device. Clip-level model predictions were analyzed and compared with blinded expert review for A versus B line pattern. Four prediction thresholding approaches were applied to maximize model sensitivity and specificity at bedside.SETTING:
Academic ICU. PATIENTS One-hundred critically ill patients admitted to ICU, receiving oxygen therapy, and eligible for respiratory imaging were included. Patients who were unstable or could not undergo an LUS examination were excluded.INTERVENTIONS:
None. MEASUREMENTS AND MAINRESULTS:
A total of 100 unique ICU patients (400 clips) were enrolled from two tertiary-care sites. Fifty-six patients were mechanically ventilated. When compared with gold standard expert annotation, the real-time inference yielded an accuracy of 95%, sensitivity of 93%, and specificity of 96% for identification of the B line pattern. Varying prediction thresholds showed that real-time modification of sensitivity and specificity according to clinical priorities is possible.CONCLUSIONS:
A previously validated DL classification model performs equally well in real-time at the bedside when platformed on a portable device. As the first study to test the feasibility and performance of a DL classification model for LUS in a dedicated ICU environment, our results justify further inquiry into the impact of employing real-time automation of medical imaging into the care of the critically ill.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Critical Illness
/
Deep Learning
Type of study:
Diagnostic_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
Crit Care Med
Year:
2023
Document type:
Article