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Clinical evaluation of medical image synthesis : a case study in wireless capsule endoscopy

Gatoula, Panagiota ; Diamantis, Dimitrios E. ; Koulaouzidis, Anastasios ; Carretero, Cristina ; Chetcuti-Zammit, Stefania ; Valdivia, Pablo Cortegoso ; González-Suárez, Begoña ; Mussetto, Alessandro ; Plevris, John and Robertson, Alexander , et al. (2025) In Scientific Reports 15(1).
Abstract

Synthetic Data Generation (SDG) based on Artificial Intelligence (AI) can transform the way clinical medicine is delivered by overcoming privacy barriers that currently render clinical data sharing difficult. This is the key to accelerating the development of digital tools contributing to enhanced patient safety. Such tools include robust data-driven clinical decision support systems, and example-based digital training tools that will enable healthcare professionals to improve their diagnostic performance for enhanced patient safety. This study focuses on the clinical evaluation of medical SDG, with a proof-of-concept investigation on diagnosing Inflammatory Bowel Disease (IBD) using Wireless Capsule Endoscopy (WCE) images. Its... (More)

Synthetic Data Generation (SDG) based on Artificial Intelligence (AI) can transform the way clinical medicine is delivered by overcoming privacy barriers that currently render clinical data sharing difficult. This is the key to accelerating the development of digital tools contributing to enhanced patient safety. Such tools include robust data-driven clinical decision support systems, and example-based digital training tools that will enable healthcare professionals to improve their diagnostic performance for enhanced patient safety. This study focuses on the clinical evaluation of medical SDG, with a proof-of-concept investigation on diagnosing Inflammatory Bowel Disease (IBD) using Wireless Capsule Endoscopy (WCE) images. Its scientific contributions include (a) a novel protocol for the systematic Clinical Evaluation of Medical Image Synthesis (CEMIS); (b) a novel variational autoencoder-based model, named TIDE-II, which enhances its predecessor model, TIDE (This Intestine Does not Exist), for the generation of high-resolution synthetic WCE images; and (c) a comprehensive evaluation of the synthetic images using the CEMIS protocol by 10 international WCE specialists, in terms of image quality, diversity, and realism, as well as their utility for clinical decision-making. The results show that TIDE-II generates clinically plausible, very realistic WCE images, of improved quality compared to relevant state-of-the-art generative models. Concludingly, CEMIS can serve as a reference for future research on medical image-generation techniques, while the adaptation/extension of the architecture of TIDE-II to other imaging domains can be promising.

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publishing date
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Contribution to journal
publication status
published
subject
in
Scientific Reports
volume
15
issue
1
article number
35068
publisher
Nature Publishing Group
external identifiers
  • scopus:105018270996
  • pmid:41062527
ISSN
2045-2322
DOI
10.1038/s41598-025-14359-4
language
English
LU publication?
yes
id
f74caca8-3489-4466-89c9-0974cdab9454
date added to LUP
2025-12-19 14:17:10
date last changed
2025-12-20 03:00:07
@article{f74caca8-3489-4466-89c9-0974cdab9454,
  abstract     = {{<p>Synthetic Data Generation (SDG) based on Artificial Intelligence (AI) can transform the way clinical medicine is delivered by overcoming privacy barriers that currently render clinical data sharing difficult. This is the key to accelerating the development of digital tools contributing to enhanced patient safety. Such tools include robust data-driven clinical decision support systems, and example-based digital training tools that will enable healthcare professionals to improve their diagnostic performance for enhanced patient safety. This study focuses on the clinical evaluation of medical SDG, with a proof-of-concept investigation on diagnosing Inflammatory Bowel Disease (IBD) using Wireless Capsule Endoscopy (WCE) images. Its scientific contributions include (a) a novel protocol for the systematic Clinical Evaluation of Medical Image Synthesis (CEMIS); (b) a novel variational autoencoder-based model, named TIDE-II, which enhances its predecessor model, TIDE (This Intestine Does not Exist), for the generation of high-resolution synthetic WCE images; and (c) a comprehensive evaluation of the synthetic images using the CEMIS protocol by 10 international WCE specialists, in terms of image quality, diversity, and realism, as well as their utility for clinical decision-making. The results show that TIDE-II generates clinically plausible, very realistic WCE images, of improved quality compared to relevant state-of-the-art generative models. Concludingly, CEMIS can serve as a reference for future research on medical image-generation techniques, while the adaptation/extension of the architecture of TIDE-II to other imaging domains can be promising.</p>}},
  author       = {{Gatoula, Panagiota and Diamantis, Dimitrios E. and Koulaouzidis, Anastasios and Carretero, Cristina and Chetcuti-Zammit, Stefania and Valdivia, Pablo Cortegoso and González-Suárez, Begoña and Mussetto, Alessandro and Plevris, John and Robertson, Alexander and Rosa, Bruno and Toth, Ervin and Iakovidis, Dimitris K.}},
  issn         = {{2045-2322}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Scientific Reports}},
  title        = {{Clinical evaluation of medical image synthesis : a case study in wireless capsule endoscopy}},
  url          = {{http://dx.doi.org/10.1038/s41598-025-14359-4}},
  doi          = {{10.1038/s41598-025-14359-4}},
  volume       = {{15}},
  year         = {{2025}},
}