Quality Control

Ilkay Oksuz; Alain Lalande; Esther Puyol-Antón

doi:10.1007/978-3-031-05071-8_7

Quality Control

Ilkay Oksuz
, Alain Lalande
, Esther Puyol-Antón^*

^*Bu çalışma için yazışmadan sorumlu yazar

Bilgisayar Mühendisliği Bölümü

Université de Bourgogne
University Hospital of Dijon
King's College London

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Bölüm › bilirkişi

1 Atıf (Scopus)

Özet

Assessing and controlling the quality of medical data such as images, as well as AI-derived parameters from these data, is an important component of clinical imaging and retrospective population studies. This chapter deals with the issue of how AI can be used to automatically control the quality of its results. The clinical introduction discusses the role of current role and potential of quality control techniques. The technical review summarizes the state-of-the-art in AI-enabled quality control. The main focus is on cardiac MR imaging, but applications in echocardiography are also introduced. Methods to identify motion artefacts, poor planning, missing slices and failed segmentations are discussed. The practical tutorial involves implementing a simple motion artefact detection model. The closing clinical opinion piece discusses the future role of AI in ensuring that the quality of images and their derived measures is maximized.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	AI and Big Data in Cardiology
Ana bilgisayar yayını alt yazısı	a Practical Guide
Yayınlayan	Springer International Publishing
Sayfalar	135-156
Sayfa sayısı	22
ISBN (Elektronik)	9783031050718
ISBN (Basılı)	9783031050701
DOI'lar	https://doi.org/10.1007/978-3-031-05071-8_7
Yayın durumu	Yayınlandı - 1 Oca 2023

Bibliyografik not

Publisher Copyright:
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Belgeye Erişim

10.1007/978-3-031-05071-8_7

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

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keywords = "Artefact, Artificial intelligence, Deep learning, Echocardiography, Machine learning, Magnetic resonance, Motion, Planning, Quality control",

author = "Ilkay Oksuz and Alain Lalande and Esther Puyol-Ant{\'o}n",

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AU - Oksuz, Ilkay

AU - Lalande, Alain

AU - Puyol-Antón, Esther

N1 - Publisher Copyright: © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

PY - 2023/1/1

Y1 - 2023/1/1

N2 - Assessing and controlling the quality of medical data such as images, as well as AI-derived parameters from these data, is an important component of clinical imaging and retrospective population studies. This chapter deals with the issue of how AI can be used to automatically control the quality of its results. The clinical introduction discusses the role of current role and potential of quality control techniques. The technical review summarizes the state-of-the-art in AI-enabled quality control. The main focus is on cardiac MR imaging, but applications in echocardiography are also introduced. Methods to identify motion artefacts, poor planning, missing slices and failed segmentations are discussed. The practical tutorial involves implementing a simple motion artefact detection model. The closing clinical opinion piece discusses the future role of AI in ensuring that the quality of images and their derived measures is maximized.

AB - Assessing and controlling the quality of medical data such as images, as well as AI-derived parameters from these data, is an important component of clinical imaging and retrospective population studies. This chapter deals with the issue of how AI can be used to automatically control the quality of its results. The clinical introduction discusses the role of current role and potential of quality control techniques. The technical review summarizes the state-of-the-art in AI-enabled quality control. The main focus is on cardiac MR imaging, but applications in echocardiography are also introduced. Methods to identify motion artefacts, poor planning, missing slices and failed segmentations are discussed. The practical tutorial involves implementing a simple motion artefact detection model. The closing clinical opinion piece discusses the future role of AI in ensuring that the quality of images and their derived measures is maximized.

KW - Artefact

KW - Artificial intelligence

KW - Deep learning

KW - Echocardiography

KW - Machine learning

KW - Magnetic resonance

KW - Motion

KW - Planning

KW - Quality control

UR - https://www.scopus.com/pages/publications/85173906323

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M3 - Chapter

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SN - 9783031050701

SP - 135

EP - 156

BT - AI and Big Data in Cardiology

PB - Springer International Publishing

ER -

Quality Control

Özet

Bibliyografik not

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap