A machine-learning approach to the automated assessment of joint synovitis activity

Konrad Wojciechowski; Bogdan Smolka; Rafal Cupek; Adam Ziebinski; Karolina Nurzynska; Marek Kulbacki; Jakub Segen; Marcin Fojcik; Pawel Mielnik; Sebastian Hein

doi:10.1007/978-3-319-45246-3_42

A machine-learning approach to the automated assessment of joint synovitis activity

Konrad Wojciechowski
, Bogdan Smolka
, Rafal Cupek
, Adam Ziebinski
, Karolina Nurzynska
, Marek Kulbacki
, Jakub Segen
, Marcin Fojcik
, Pawel Mielnik
, Sebastian Hein

Polish-Japanese Academy of Information Technology
Høgskulen i Sogn og Fjordane
Helse Førde
Institute of Medical Technology and Equipment

Research output: Contribution to journal › Conference article › peer-review

3 Citations (Scopus)

Abstract

Medical ultrasound imaging is an important tool in diagnosing and monitoring synovitis, which is an inflammation of the synovial membrane that surrounds a joint. Ultrasound images are examined by medical experts to assess the presence and progression of synovitis. Automating image analysis reduces the costs and increases the availability of the ultrasound diagnosis of synovitis and diminishes or eliminates subjective discrepancies. This article describes research that is concerned with the problem of the automatic estimation of the state of the activity of finger joint inflammation using the information that is present in ultrasonography imaging.

Original language	English
Pages (from-to)	440-450
Number of pages	11
Journal	Lecture Notes in Computer Science
Volume	9876 LNCS
DOIs	https://doi.org/10.1007/978-3-319-45246-3_42
Publication status	Published - 2016
Event	8th International Conference on Computational Collective Intelligence, ICCCI 2016 - Halkidiki, Greece Duration: 28 Sept 2016 → 30 Sept 2016

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

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10.1007/978-3-319-45246-3_42

Cite this

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title = "A machine-learning approach to the automated assessment of joint synovitis activity",

abstract = "Medical ultrasound imaging is an important tool in diagnosing and monitoring synovitis, which is an inflammation of the synovial membrane that surrounds a joint. Ultrasound images are examined by medical experts to assess the presence and progression of synovitis. Automating image analysis reduces the costs and increases the availability of the ultrasound diagnosis of synovitis and diminishes or eliminates subjective discrepancies. This article describes research that is concerned with the problem of the automatic estimation of the state of the activity of finger joint inflammation using the information that is present in ultrasonography imaging.",

author = "Konrad Wojciechowski and Bogdan Smolka and Rafal Cupek and Adam Ziebinski and Karolina Nurzynska and Marek Kulbacki and Jakub Segen and Marcin Fojcik and Pawel Mielnik and Sebastian Hein",

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doi = "10.1007/978-3-319-45246-3\_42",

language = "English",

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TY - JOUR

T1 - A machine-learning approach to the automated assessment of joint synovitis activity

AU - Wojciechowski, Konrad

AU - Smolka, Bogdan

AU - Cupek, Rafal

AU - Ziebinski, Adam

AU - Nurzynska, Karolina

AU - Kulbacki, Marek

AU - Segen, Jakub

AU - Fojcik, Marcin

AU - Mielnik, Pawel

AU - Hein, Sebastian

PY - 2016

Y1 - 2016

N2 - Medical ultrasound imaging is an important tool in diagnosing and monitoring synovitis, which is an inflammation of the synovial membrane that surrounds a joint. Ultrasound images are examined by medical experts to assess the presence and progression of synovitis. Automating image analysis reduces the costs and increases the availability of the ultrasound diagnosis of synovitis and diminishes or eliminates subjective discrepancies. This article describes research that is concerned with the problem of the automatic estimation of the state of the activity of finger joint inflammation using the information that is present in ultrasonography imaging.

AB - Medical ultrasound imaging is an important tool in diagnosing and monitoring synovitis, which is an inflammation of the synovial membrane that surrounds a joint. Ultrasound images are examined by medical experts to assess the presence and progression of synovitis. Automating image analysis reduces the costs and increases the availability of the ultrasound diagnosis of synovitis and diminishes or eliminates subjective discrepancies. This article describes research that is concerned with the problem of the automatic estimation of the state of the activity of finger joint inflammation using the information that is present in ultrasonography imaging.

UR - https://www.scopus.com/pages/publications/84990047473

U2 - 10.1007/978-3-319-45246-3_42

DO - 10.1007/978-3-319-45246-3_42

M3 - Conference article

AN - SCOPUS:84990047473

SN - 0302-9743

VL - 9876 LNCS

SP - 440

EP - 450

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

T2 - 8th International Conference on Computational Collective Intelligence, ICCCI 2016

Y2 - 28 September 2016 through 30 September 2016

ER -

A machine-learning approach to the automated assessment of joint synovitis activity

Abstract

ASJC Scopus subject areas

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