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Related Experiment Video

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Ultrasonic Assessment of Myocardial Microstructure
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Machine learning-enabled screening for aortic stenosis with handheld ultrasound.

Samuel Karmiy1, Zhe Huang2, Divya Velury1

  • 1Department of Medicine, Tufts Medical Center, Boston, MA, USA.

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|May 21, 2025
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Summary

A machine learning model for aortic stenosis (AS) detection performed poorly on handheld ultrasound images. Fine-tuning the model significantly improved its accuracy, showing promise for automated interpretation of focused cardiac ultrasound (FoCUS) data.

Keywords:
aortic stenosisdiagnosisechocardiographymachine learning

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Neural network classifiers show promise in detecting aortic stenosis (AS) using cardiac ultrasound.
  • Existing models are trained on cart-based imaging and have not been validated on focused cardiac ultrasound (FoCUS) from handheld devices.

Purpose of the Study:

  • To evaluate the performance of a cart-based AS classifier on FoCUS images acquired with handheld ultrasound devices.
  • To fine-tune the classifier using handheld data to improve AS detection accuracy.

Main Methods:

  • A prospective study included 160 patients (≥65 years) undergoing transthoracic echocardiography.
  • A pre-trained cart-based AS classifier was tested on FoCUS images from a handheld device.
  • The classifier underwent last-layer fine-tuning on handheld data and was externally validated.

Main Results:

  • The cart-based model achieved an AUROC of 0.87 on FoCUS images.
  • Fine-tuning improved the AUROC to 0.94, with external validation reaching 0.97.
  • The fine-tuned model demonstrated high positive and negative predictive values in simulated screening environments.

Conclusions:

  • A cart-based AI model for AS detection experienced reduced performance on handheld FoCUS images.
  • Fine-tuning the model with handheld data significantly enhanced its diagnostic accuracy.
  • This approach shows potential for automated AS detection using handheld ultrasound devices.