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Leveraging the Capabilities of AI: Novice Neurology-Trained Operators Performing Cardiac POCUS in Patients with Acute Brain Injury

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Abstract

Background

Cardiac point-of-care ultrasound (cPOCUS) can aid in the diagnosis and treatment of cardiac disorders. Such disorders can arise as complications of acute brain injury, but most neurologic intensive care unit (NICU) providers do not receive formal training in cPOCUS. Caption artificial intelligence (AI) uses a novel deep learning (DL) algorithm to guide novice cPOCUS users in obtaining diagnostic-quality cardiac images. The primary objective of this study was to determine how often NICU providers with minimal cPOCUS experience capture quality images using DL-guided cPOCUS as well as the association between DL-guided cPOCUS and change in management and time to formal echocardiograms in the NICU.

Methods

From September 2020 to November 2021, neurology-trained physician assistants, residents, and fellows used DL software to perform clinically indicated cPOCUS scans in an academic tertiary NICU. Certified echocardiographers evaluated each scan independently to assess the quality of images and global interpretability of left ventricular function, right ventricular function, inferior vena cava size, and presence of pericardial effusion. Descriptive statistics with exact confidence intervals were used to calculate proportions of obtained images that were of adequate quality and that changed management. Time to first adequate cardiac images (either cPOCUS or formal echocardiography) was compared using a similar population from 2018.

Results

In 153 patients, 184 scans were performed for a total of 943 image views. Three certified echocardiographers deemed 63.4% of scans as interpretable for a qualitative assessment of left ventricular size and function, 52.6% of scans as interpretable for right ventricular size and function, 34.8% of scans as interpretable for inferior vena cava size and variability, and 47.2% of scans as interpretable for the presence of pericardial effusion. Thirty-seven percent of screening scans changed management, most commonly adjusting fluid goals (81.2%). Time to first adequate cardiac images decreased significantly from 3.1 to 1.7 days (p < 0.001).

Conclusions

With DL guidance, neurology providers with minimal to no cPOCUS training were often able to obtain diagnostic-quality cardiac images, which informed management changes and significantly decreased time to cardiac imaging.

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Data Availability

Deidentified data will be made available upon reasonable request. Requests for data sharing can be sent to jhc9010@med.cornell.edu.

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Authors and Affiliations

  1. Clinical and Translational Neuroscience Unit, Feil Family Brain and Mind Research Institute and Department of Neurology, Weill Cornell Medicine, 525 East 68th Street, F610, New York, NY, 10065, USA

    Hooman Kamel, Santosh Murthy, Alexander E. Merkler, Cenai Zhang & Judy H. Ch’ang

  2. Department of Neurology, Weill Cornell Medicine, New York, NY, USA

    Jennifer Mears & Safa Kaleem

  3. Department of Anesthesiology, Weill Cornell Medicine, New York, NY, USA

    Rohan Panchamia, Chris Tam & Richard Thalappillil

Authors
  1. Jennifer Mears
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  2. Safa Kaleem
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  4. Hooman Kamel
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  7. Santosh Murthy
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  8. Alexander E. Merkler
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  9. Cenai Zhang
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  10. Judy H. Ch’ang
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Contributions

Authorship requirements have been met and the final manuscript was approved by all authors. Jennifer Mears contributed to the design, data collection, data analysis, and manuscript preparation. Safa Kaleem, contributed to data collection, data analysis, and manuscript preparation. Rohan Panchamia contributed to design and manuscript preparation. Hooman Kamel contributed to the design, data analysis, and manuscript preparation. Chris Tam contributed to design and manuscript preparation. Richard Thalappillil contributed to design and manuscript preparation. Santosh Murthy contributed to design, data analysis, and manuscript preparation. Alexander E. Merkler contributed to design, data analysis, and manuscript preparation. Cenai Zhang contributed to data analysis and manuscript preparation. Judy H. Ch’ang contributed to design, data collection, data analysis, and manuscript preparation.

Corresponding author

Correspondence to Judy H. Ch’ang.

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Conflicts of interest

Mears: none. Kaleem: none. Panchamia: none. Kamel: Dr. Kamel serves as a principal investigator for the National Institutes of Health–funded AtRial Cardiopathy and Antithrombotic Drugs In Prevention After Cryptogenic Stroke (ARCADIA) trial (National Institute of Neurological Disorders and Stroke grant U01NS095869), which receives in-kind study drug from the BMS-Pfizer Alliance for Eliquis and ancillary study support from Roche Diagnostics; serves as Deputy Editor for JAMA Neurology; serves on clinical trial steering/executive committees for Medtronic, Janssen, and Javelin Medical; serves on end point adjudication committees for AstraZeneca, Novo Nordisk, and Boehringer Ingelheim; and has an ownership interest in TETMedical, Inc. Tam: none. Thalappillil: none. Murthy: none. Merkler: Dr. Merkler serves as an expert witness for medicolegal cases. Zhang: none. Ch'ang: none.

Ethical Approval/Informed Consent

The Weill Cornell Medicine Institutional Review Board approved this ambidirectional analysis of data collected as part of the quality initiative.

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Mears, J., Kaleem, S., Panchamia, R. et al. Leveraging the Capabilities of AI: Novice Neurology-Trained Operators Performing Cardiac POCUS in Patients with Acute Brain Injury. Neurocrit Care (2024). https://doi.org/10.1007/s12028-024-01953-z

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  • DOI: https://doi.org/10.1007/s12028-024-01953-z

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