Prospective Real-Time Validation of a Lung Ultrasound Deep Learning Model in the ICU. Issue 2 (20th January 2023)

• Record Type: Journal Article
• Authors: Dave, Chintan; Wu, Derek; Tschirhart, Jared; Smith, Delaney; VanBerlo, Blake; Deglint, Jason; Ali, Faraz; Chaudhary, Rushil; VanBerlo, Bennett; Ford, Alex; Rahman, Marwan A.; McCauley, Joseph; Wu, Benjamin; Ho, Jordan; Li, Brian; Arntfield, Robert
• Is Part Of: Critical care medicine. Volume 51:Issue 2(2023)
• Page Start: 301
• Page End: 309
• Publication Date: 2023-01-20
• Subjects: artificial intelligence -- deep learning -- intensive care unit -- lung ultrasound -- real-time validation; Critical care medicine -- Periodicals; Soins intensifs -- Périodiques; 616.028
• DOI: 10.1097/CCM.0000000000005759
• Languages: English
• ISSNs: 0090-3493

Abstract

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 MAIN RESULTS: 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.