Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Vasantha kumar Venugopal"'
Autor:
Nisha Syed Nasser, Krish Sharma, Parv Mahendra Mehta, Vidur Mahajan, Harsh Mahajan, Vasantha Kumar Venugopal
Publikováno v:
AIMS Neuroscience, Vol 10, Iss 2, Pp 144-153 (2023)
AIM: Synthetic MRI (SyMRI) works on the MDME sequence, which acquires the relaxation properties of the brain and helps to measure the accurate tissue properties in 6 minutes. The aim of this study was to evaluate the synthetic MRI (SyMRI)-generated
Externí odkaz:
https://doaj.org/article/82a22436c0b84edea9e541faee29f33a
Autor:
Prashant Sadashiv Gidde, Shyam Sunder Prasad, Ajay Pratap Singh, Nitin Bhatheja, Satyartha Prakash, Prateek Singh, Aakash Saboo, Rohit Takhar, Salil Gupta, Sumeet Saurav, Raghunandanan M. V., Amritpal Singh, Viren Sardana, Harsh Mahajan, Arjun Kalyanpur, Atanendu Shekhar Mandal, Vidur Mahajan, Anurag Agrawal, Anjali Agrawal, Vasantha Kumar Venugopal, Sanjay Singh, Debasis Dash
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
Abstract SARS-CoV2 pandemic exposed the limitations of artificial intelligence based medical imaging systems. Earlier in the pandemic, the absence of sufficient training data prevented effective deep learning (DL) solutions for the diagnosis of COVID
Externí odkaz:
https://doaj.org/article/c7e55fe884944070b531d1f4f6b42c63
Autor:
Sandeep Panwar Jogi, Rafeek Thaha, Sriram Rajan, Vidur Mahajan, Vasantha Kumar Venugopal, Anup Singh, Amit Mehndiratta
Publikováno v:
Journal of Translational Medicine, Vol 19, Iss 1, Pp 1-13 (2021)
Abstract Background Appropriate structural and material properties are essential for finite-element-modeling (FEM). In knee FEM, structural information could extract through 3D-imaging, but the individual subject’s tissue material properties are in
Externí odkaz:
https://doaj.org/article/4e16a16d10af4d42922fb171df2a0990
Autor:
Surbhi Mittal, Vasantha Kumar Venugopal, Vikash Kumar Agarwal, Manu Malhotra, Jagneet Singh Chatha, Savinay Kapur, Ankur Gupta, Vikas Batra, Puspita Majumdar, Aakarsh Malhotra, Kartik Thakral, Saheb Chhabra, Mayank Vatsa, Richa Singh, Santanu Chaudhury
Publikováno v:
PLoS ONE, Vol 17, Iss 10 (2022)
Consistent clinical observations of characteristic findings of COVID-19 pneumonia on chest X-rays have attracted the research community to strive to provide a fast and reliable method for screening suspected patients. Several machine learning algorit
Externí odkaz:
https://doaj.org/article/37b438f0c41e4c35a941f23316cb78fd
Autor:
Ana Jimenez-Pastor, Rafael Lopez-Gonzalez, Belén Fos-Guarinos, Fabio Garcia-Castro, Mark Wittenberg, Asunción Torregrosa-Andrés, Luis Marti-Bonmati, Margarita Garcia-Fontes, Pablo Duarte, Juan Pablo Gambini, Leonardo Kayat Bittencourt, Felipe Campos Kitamura, Vasantha Kumar Venugopal, Vidur Mahajan, Pablo Ros, Emilio Soria-Olivas, Angel Alberich-Bayarri
Publikováno v:
European Radiology.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bd4acc372405e915ea682688aa848efb
https://doi.org/10.1007/s00330-023-09410-9
https://doi.org/10.1007/s00330-023-09410-9
Autor:
Vasantha Kumar Venugopal, Rafeek Thaha, Vidur Mahajan, Amit Mehndiratta, Anup Singh, Sandeep P Jogi, Sriram Rajan
Publikováno v:
Journal of Translational Medicine, Vol 19, Iss 1, Pp 1-13 (2021)
Journal of Translational Medicine
Journal of Translational Medicine
BackgroundAppropriate structural and material properties are essential for finite-element-modeling (FEM). In knee FEM, structural information could extract through 3D-imaging, but the individual subject’s tissue material properties are inaccessible
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::19ebbc42857f6c883ba80f48e3489f89
https://doaj.org/article/4e16a16d10af4d42922fb171df2a0990
https://doaj.org/article/4e16a16d10af4d42922fb171df2a0990
Autor:
Feiyang Yu, Mark Endo, Rayan Krishnan, Ian Pan, Andy Tsai, Eduardo Pontes Reis, Eduardo Kaiser Ururahy Nunes Fonseca, Henrique Min Ho Lee, Zahra Shakeri Hossein Abad, Andrew Y. Ng, Curtis P. Langlotz, Vasantha Kumar Venugopal, Pranav Rajpurkar
The application of AI to medical image interpretation tasks has largely been limited to the identification of a handful of individual pathologies. In contrast, the generation of complete narrative radiology reports more closely matches how radiologis
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cdf3258edd96b4b838b278be05fd7aca
https://doi.org/10.1101/2022.08.30.22279318
https://doi.org/10.1101/2022.08.30.22279318
Autor:
Sandeep P Jogi, Vidur Mahajan, Vasantha Kumar Venugopal, Amit Mehndiratta, Sriram Rajan, Rafeek Thaha, Anup Singh
Publikováno v:
International Journal of Computer Assisted Radiology and Surgery. 15:403-413
The quantitative analysis of weight-bearing articular cartilage superficial to subchondral abnormality is important in osteoarthritis (OA) progression studies. The current study aimed to address the challenges of a semi-automatic segmentation of tibi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e8a35510491fefd29d900e3ce5a52da8
https://doi.org/10.1007/s11548-020-02116-z
https://doi.org/10.1007/s11548-020-02116-z
Publikováno v:
Academic Radiology. 27:132-135
There is a plethora of Artificial Intelligence (AI) tools that are being developed around the world aiming at either speeding up or improving the accuracy of radiologists. It is essential for radiologists to work with the developers of such algorithm
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f1961fac2fdb3d17d5cd26a95d3b29b1
https://doi.org/10.1016/j.acra.2019.09.009
https://doi.org/10.1016/j.acra.2019.09.009
Publikováno v:
Journal of medical systems. 46(4)
Adoption of Artificial Intelligence (AI) algorithms into the clinical realm will depend on their inherent trustworthiness, which is built not only by robust validation studies but is also deeply linked to the explainability and interpretability of th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::4b11b08abfa9327a8a9d118c64bec520
https://pubmed.ncbi.nlm.nih.gov/35249179
https://pubmed.ncbi.nlm.nih.gov/35249179