Zobrazeno 1 - 10
of 189
pro vyhledávání: '"Saiviroonporn, P."'
Publikováno v:
European Journal of Radiology Open, Vol 13, Iss , Pp 100593- (2024)
Background: Artificial intelligence (AI) has been proven useful for the assessment of tubes and lines on chest radiographs of general patients. However, validation on intensive care unit (ICU) patients remains imperative. Methods: This retrospective
Externí odkaz:
https://doaj.org/article/eadeca859e2a46668506a5afc21d4034
Despite much promising research in the area of artificial intelligence for medical image diagnosis, there has been no large-scale validation study done in Thailand to confirm the accuracy and utility of such algorithms when applied to local datasets.
Externí odkaz:
http://arxiv.org/abs/2004.10975
Autor:
Chamveha, Isarun, Promwiset, Treethep, Tongdee, Trongtum, Saiviroonporn, Pairash, Chaisangmongkon, Warasinee
We propose an algorithm for calculating the cardiothoracic ratio (CTR) from chest X-ray films. Our approach applies a deep learning model based on U-Net with VGG16 encoder to extract lung and heart masks from chest X-ray images and calculate CTR from
Externí odkaz:
http://arxiv.org/abs/2002.07468
Autor:
Trongtum Tongdee, Worapan Kusakunniran, Thanongchai Siriapisith, Pairash Saiviroonporn, Thanandon Imaromkul, Pakorn Yodprom
Publikováno v:
ICT Express, Vol 9, Iss 3, Pp 313-319 (2023)
Classifying x-ray images into individual classes of body parts is needed, when they are mixed without proper labels. This paper proposes a hierarchical training of convolutional neural network (CNN)-based framework, for classifying chest posterior–
Externí odkaz:
https://doaj.org/article/c71f55e3c6f64a609bea5a3f809899c4
Autor:
Narumol Sudjai, Palanan Siriwanarangsun, Nittaya Lektrakul, Pairash Saiviroonporn, Sorranart Maungsomboon, Rapin Phimolsarnti, Apichat Asavamongkolkul, Chandhanarat Chandhanayingyong
Publikováno v:
Journal of Orthopaedic Surgery and Research, Vol 18, Iss 1, Pp 1-13 (2023)
Abstract Background To develop a machine learning model based on tumor-to-bone distance and radiomic features derived from preoperative MRI images to distinguish intramuscular (IM) lipomas and atypical lipomatous tumors/well-differentiated liposarcom
Externí odkaz:
https://doaj.org/article/4f01d402946742ac923bb1c069c79697
Autor:
Worapan Kusakunniran, Sarattha Karnjanapreechakorn, Thanongchai Siriapisith, Pairash Saiviroonporn
Publikováno v:
Intelligent Systems with Applications, Vol 18, Iss , Pp 200203- (2023)
One of the main challenges to obtain a high throughput in the MRI process is a slow signal acquisition. This process could be improved using a parallel imaging technique, where fewer raw data with multiple radio frequency (RF) coils are acquired simu
Externí odkaz:
https://doaj.org/article/db58abedac1c47f9aa4de071f4166519
Autor:
Pairash Saiviroonporn, Suwimon Wonglaksanapimon, Warasinee Chaisangmongkon, Isarun Chamveha, Pakorn Yodprom, Krittachat Butnian, Thanogchai Siriapisith, Trongtum Tongdee
Publikováno v:
BMC Medical Imaging, Vol 22, Iss 1, Pp 1-10 (2022)
Abstract Background Artificial intelligence, particularly the deep learning (DL) model, can provide reliable results for automated cardiothoracic ratio (CTR) measurement on chest X-ray (CXR) images. In everyday clinical use, however, this technology
Externí odkaz:
https://doaj.org/article/a6a92c269dce456986863ae07fed0864
Autor:
Kittichai Wantanajittikul, Pairash Saiviroonporn, Suwit Saekho, Rungroj Krittayaphong, Vip Viprakasit
Publikováno v:
BMC Medical Imaging, Vol 21, Iss 1, Pp 1-18 (2021)
Abstract Background To estimate median liver iron concentration (LIC) calculated from magnetic resonance imaging, excluded vessels of the liver parenchyma region were defined manually. Previous works proposed the automated method for excluding vessel
Externí odkaz:
https://doaj.org/article/ceb273e1cb304372a828636fd8b1f997
Autor:
Pairash Saiviroonporn, Kanchanaporn Rodbangyang, Trongtum Tongdee, Warasinee Chaisangmongkon, Pakorn Yodprom, Thanogchai Siriapisith, Suwimon Wonglaksanapimon, Phakphoom Thiravit
Publikováno v:
BMC Medical Imaging, Vol 21, Iss 1, Pp 1-11 (2021)
Abstract Background Artificial Intelligence (AI) is a promising tool for cardiothoracic ratio (CTR) measurement that has been technically validated but not clinically evaluated on a large dataset. We observed and validated AI and manual methods for C
Externí odkaz:
https://doaj.org/article/43771ac3ffc84d769f5417e801d17ea3
Autor:
Warasinee Chaisangmongkon, Isarun Chamveha, Tretap Promwiset, Pairash Saiviroonporn, Trongtum Tongdee
Publikováno v:
IEEE Access, Vol 9, Pp 110287-110298 (2021)
Recent advances in machine learning have made it possible to create automated systems for medical image diagnosis. Cardiothoracic ratio (CTR) measurement, a common procedure for assessing cardiac abnormality in chest radiographs, has been investigate
Externí odkaz:
https://doaj.org/article/a9452a1a17d24e92a083dc705e457fed