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Predicting elevated natriuretic peptide in chest radiography: emerging utilization gap for artificial intelligence.

Eisuke Kagawa1, Masaya Kato1, Noboru Oda1

  • 1Department of Cardiology, Hiroshima City Asa Hospital, 1-2-1, Kameyamaminami, Asakita-ku, Hiroshima 7310293, Japan.

European Heart Journal. Imaging Methods and Practice
|October 15, 2024
PubMed
Summary

An artificial intelligence (AI) model accurately predicts elevated brain natriuretic peptide (BNP) levels from chest X-rays. AI assistance significantly improved healthcare professionals' diagnostic accuracy, particularly for early-career practitioners.

Keywords:
brain natriuretic peptidedeep learningheart failuremachine learningneural network

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Biomarker Prediction

Background:

  • Elevated brain natriuretic peptide (BNP) levels are crucial indicators for heart failure diagnosis.
  • Predicting BNP levels often requires blood tests, which can delay diagnosis.
  • Chest radiography is a common imaging modality that may contain predictive information.

Purpose of the Study:

  • To evaluate an AI model's ability to predict elevated BNP levels using chest radiograms.
  • To assess the impact of AI assistance on the diagnostic performance of healthcare professionals.

Main Methods:

  • Development of two ensemble AI models to predict BNP levels (≥ 200 pg/mL and ≥ 100 pg/mL) from chest X-rays.
  • External validation of the AI model using data from a separate hospital.
  • Comparison of diagnostic accuracy with and without AI assistance among 35 healthcare professionals.

Main Results:

  • The AI model demonstrated high performance with an accuracy of 0.855 and an AUC of 0.929.
  • AI assistance significantly improved overall diagnostic accuracy from 0.708 to 0.829 (P < 0.001).
  • AI assistance notably enhanced the performance of early-career professionals, surpassing that of experienced clinicians.

Conclusions:

  • AI models can effectively predict elevated BNP levels from chest radiograms.
  • AI tools show significant potential to augment human diagnostic capabilities in clinical practice.
  • Addressing the integration gap of new AI tools is essential for realizing their full clinical benefit.