English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The training of deep learning models typically requires extensive data, whichare not readily available as large well-curated medical-image datasets fordevelopment of artificial intelligence (AI) models applied in Radiology.Recognizing the potential for transfer learning (TL) to allow a fully trainedmodel from one institution to be fine-tuned by another institution using a muchsmall local dataset, this report describes the challenges, methodology, andbenefits of TL within the context of developing an AI model for a basicuse-case, segmentation of Left Ventricular Myocardium (LVM) on images from4-dimensional coronary computed tomography angiography. Ultimately, our resultsfrom comparisons of LVM segmentation predicted by a model locally trained usingrandom initialization, versus one training-enhanced by TL, showed that ause-case model initiated by TL can be developed with sparse labels withacceptable performance. This process reduces the time required to build a newmodel in the clinical environment at a different institution.

Democratizing Artificial Intelligence in Healthcare: A Study of Model Development Across Two Institutions Incorporating Transfer Learning