Zobrazeno 1 - 10
of 84
pro vyhledávání: '"Bulat, Ibragimov"'
Publikováno v:
BMC Oral Health, Vol 24, Iss 1, Pp 1-10 (2024)
Abstract Integrating artificial intelligence (AI) into medical and dental applications can be challenging due to clinicians’ distrust of computer predictions and the potential risks associated with erroneous outputs. We introduce the idea of using
Externí odkaz:
https://doaj.org/article/a27784d2167a40699b9afba7e167d556
Autor:
Ivan Stebakov, Alexei Kornaev, Elena Kornaeva, Nikita Litvinenko, Yuri Kazakov, Oleg Ivanov, Bulat Ibragimov
Publikováno v:
IEEE Access, Vol 12, Pp 169945-169954 (2024)
Artificial neural networks are a powerful tool for spatial and temporal functions approximation. This study introduces a novel approach for modeling non-Newtonian fluid flows by minimizing a proposed power loss metric, which aligns with the variation
Externí odkaz:
https://doaj.org/article/fd1bc629cefc41bcad6c844f069d0af1
Publikováno v:
IEEE Access, Vol 11, Pp 90358-90366 (2023)
Percutaneous nephrolithotomy (PCNL) is the current standard of care for patients with a total renal stone burden $>$ 20 mm. Gaining access to the kidney is a crucial step, as the position of the percutaneous tract can affect the ability to manipulate
Externí odkaz:
https://doaj.org/article/de29560aba844c339862636e9d969a70
Autor:
Bulat Ibragimov, Kirill Arzamasov, Bulat Maksudov, Semen Kiselev, Alexander Mongolin, Tamerlan Mustafaev, Dilyara Ibragimova, Ksenia Evteeva, Anna Andreychenko, Sergey Morozov
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-14 (2023)
Abstract In 2020, an experiment testing AI solutions for lung X-ray analysis on a multi-hospital network was conducted. The multi-hospital network linked 178 Moscow state healthcare centers, where all chest X-rays from the network were redirected to
Externí odkaz:
https://doaj.org/article/97a405c86d6f4705984b55bde6076b92
Autor:
Espen Jimenez-Solem, Tonny S. Petersen, Casper Hansen, Christian Hansen, Christina Lioma, Christian Igel, Wouter Boomsma, Oswin Krause, Stephan Lorenzen, Raghavendra Selvan, Janne Petersen, Martin Erik Nyeland, Mikkel Zöllner Ankarfeldt, Gert Mehl Virenfeldt, Matilde Winther-Jensen, Allan Linneberg, Mostafa Mehdipour Ghazi, Nicki Detlefsen, Andreas David Lauritzen, Abraham George Smith, Marleen de Bruijne, Bulat Ibragimov, Jens Petersen, Martin Lillholm, Jon Middleton, Stine Hasling Mogensen, Hans-Christian Thorsen-Meyer, Anders Perner, Marie Helleberg, Benjamin Skov Kaas-Hansen, Mikkel Bonde, Alexander Bonde, Akshay Pai, Mads Nielsen, Martin Sillesen
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
Abstract Patients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that machine learning (ML) models could be used to predict risks at different stages of management and thereby provide insights into drivers and pro
Externí odkaz:
https://doaj.org/article/aa3f45fd445f4117b36899935a16679d
Autor:
Imad Eddine Ibrahim Bekkouch, Dragos Constantin Nicolae, Adil Khan, S. M. Ahsan Kazmi, Asad Masood Khattak, Bulat Ibragimov
Publikováno v:
IEEE Access, Vol 9, Pp 42424-42437 (2021)
The biggest fear when deploying machine learning models to the real world is their ability to handle the new data. This problem is significant especially in medicine, where models trained on rich high-quality data extracted from large hospitals do no
Externí odkaz:
https://doaj.org/article/e8abc5007da54b14993d0b1f1fc1bf28
Publikováno v:
Podobnik, G, Strojan, P, Peterlin, P, Ibragimov, B & Vrtovec, T 2023, ' HaN-Seg : The head and neck organ-at-risk CT and MR segmentation dataset ', Medical Physics, vol. 50, no. 3 . https://doi.org/10.1002/mp.16197
Purpose: For the cancer in the head and neck (HaN), radiotherapy (RT) represents an important treatment modality. Segmentation of organs-at-risk (OARs) is the starting point of RT planning, however, existing approaches are focused on either computed
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8d303ce6f18e3971fd6246238972bf1e
https://doi.org/10.1002/mp.16197
https://doi.org/10.1002/mp.16197
Autor:
Danis Alukaev, Semen Kiselev, Tamerlan Mustafaev, Ahatov Ainur, Bulat Ibragimov, Tomaž Vrtovec
Publikováno v:
European Spine Journal. 31:2115-2124
To propose a fully automated deep learning (DL) framework for the vertebral morphometry and Cobb angle measurement from three-dimensional (3D) computed tomography (CT) images of the spine, and validate the proposed framework on an external database.T
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::47cf95e2d03c5d5cb382e30323a4b42f
https://doi.org/10.1007/s00586-022-07245-4
https://doi.org/10.1007/s00586-022-07245-4
Autor:
Bulat Ibragimov, Tomaž Vrtovec
Publikováno v:
European Spine Journal. 31:2031-2045
To summarize and critically evaluate the existing studies for spinopelvic measurements of sagittal balance that are based on deep learning (DL).Three databases (PubMed, WoS and Scopus) were queried for records using keywords related to DL and measure
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8093b02fa09b5b9695a3b42938949f59
https://doi.org/10.1007/s00586-022-07155-5
https://doi.org/10.1007/s00586-022-07155-5
Autor:
Shaqayeq, Ramezanzade, Tudor, Laurentiu, Azam, Bakhshandah, Bulat, Ibragimov, Thomas, Kvist, Lars, Bjørndal
Publikováno v:
Acta odontologica Scandinavica.
To assess the efficiency of AI methods in finding radiographic features in Endodontic treatment considerations.This review was based on the PRISMA guidelines and QUADAS 2 tool. A systematic search was performed of the literature on cases with endodon
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::98f309b294d59669be1e9341ea055b81
https://pubmed.ncbi.nlm.nih.gov/36548872
https://pubmed.ncbi.nlm.nih.gov/36548872