The role of AI in prostate MRI quality and interpretation: Opportunities and challenges.


Journal

European journal of radiology
ISSN: 1872-7727
Titre abrégé: Eur J Radiol
Pays: Ireland
ID NLM: 8106411

Informations de publication

Date de publication:
Aug 2023
Historique:
received: 25 03 2023
revised: 06 05 2023
accepted: 20 05 2023
medline: 24 7 2023
pubmed: 29 5 2023
entrez: 28 5 2023
Statut: ppublish

Résumé

Prostate MRI plays an important role in imaging the prostate gland and surrounding tissues, particularly in the diagnosis and management of prostate cancer. With the widespread adoption of multiparametric magnetic resonance imaging in recent years, the concerns surrounding the variability of imaging quality have garnered increased attention. Several factors contribute to the inconsistency of image quality, such as acquisition parameters, scanner differences and interobserver variabilities. While efforts have been made to standardize image acquisition and interpretation via the development of systems, such as PI-RADS and PI-QUAL, the scoring systems still depend on the subjective experience and acumen of humans. Artificial intelligence (AI) has been increasingly used in many applications, including medical imaging, due to its ability to automate tasks and lower human error rates. These advantages have the potential to standardize the tasks of image interpretation and quality control of prostate MRI. Despite its potential, thorough validation is required before the implementation of AI in clinical practice. In this article, we explore the opportunities and challenges of AI, with a focus on the interpretation and quality of prostate MRI.

Identifiants

pubmed: 37245342
pii: S0720-048X(23)00201-2
doi: 10.1016/j.ejrad.2023.110887
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

110887

Informations de copyright

Copyright © 2023 Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Heejong Kim (H)

Department of Radiology, Weill Cornell Medical College, 525 E 68th St Box 141, New York, NY 10021, United States.

Shin Won Kang (SW)

Research Institute for Future Medicine, Samsung Medical Center, Republic of Korea.

Jae-Hun Kim (JH)

Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Republic of Korea.

Himanshu Nagar (H)

Department of Radiation Oncology, Weill Cornell Medical College, 525 E 68th St, New York, NY 10021, United States.

Mert Sabuncu (M)

Department of Radiology, Weill Cornell Medical College, 525 E 68th St Box 141, New York, NY 10021, United States.

Daniel J A Margolis (DJA)

Department of Radiology, Weill Cornell Medical College, 525 E 68th St Box 141, New York, NY 10021, United States. Electronic address: djm9016@med.cornell.edu.

Chan Kyo Kim (CK)

Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Republic of Korea.

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Classifications MeSH