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Updated: Jun 21, 2025

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care

Published on: February 16, 2011

Audio-visual modelling in a clinical setting.

Jianbo Jiao^1,2, Mohammad Alsharid^3,4, Lior Drukker^5,6

¹Department of Engineering Science, University of Oxford, Oxford, UK. jianbo.jiao@eng.ox.ac.uk.

Scientific Reports

|July 6, 2024

Summary

This summary is machine-generated.

This study introduces a self-supervised learning framework for audio-visual modeling in clinical settings. The method effectively learns medical representations from ultrasound videos, improving downstream tasks without extensive human annotation.

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Area of Science:

Medical Imaging
Artificial Intelligence
Biomedical Engineering

Background:

Audio-visual signals are crucial in both natural and clinical environments.
Audio-visual modeling in clinical settings is challenging due to signal diversity and noise.
Current methods often require extensive expert annotations for training.

Purpose of the Study:

To develop a self-supervised learning framework for audio-visual modeling in clinical settings.
To learn transferable medical representations from multi-modal ultrasound data.
To enhance various clinical tasks without relying on dense supervisory annotations.

Main Methods:

A multi-modal self-supervised learning framework was designed.
The framework processes audio and visual signals from clinical environments.
No dense supervisory annotations from human experts were required for training.

Main Results:

The framework successfully learned anatomical representations from ultrasound videos.
It aided in identifying anatomical planes, predicting sonographer focus, and localizing regions of interest.
The learned representations improved automated downstream clinical tasks, outperforming fully-supervised methods.

Conclusions:

Self-supervised learning offers a powerful approach for medical representation learning.
This method enhances understanding of obstetric imaging and aids in sonographer training.
The framework can lead to better assistive tools and improved clinical workflows.