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Akademický článek

Effect of natural fractures on determining closure pressure

Autor: S. Nadimi, B. Forbes, J. Moore, J. D. McLennan

Publikováno v: Journal of Petroleum Exploration and Production Technology, Vol 10, Iss 2, Pp 711-728 (2019)

Abstract In Utah FORGE reservoir, eight pressure transient tests (microhydraulic fracturing and DFIT™) show natural fracture/pressure-dependent leakoff. This behavior may lead to misinterpretation of the closure pressure (proxy for minimum principa

Externí odkaz: https://doaj.org/article/398b3f9aeb66419fa796a1620714f1b3

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Akademický článek

Epidemiological description and trajectories of patients with prostate cancer in Denmark: an observational study of 7448 patients

Autor: Victoria Blanes-Vidal, Ashkan Tashk, Manuella Lech Cantuaria, Rasmus Søgaard Hansen, Charlotte A. Poulsen, Mads H. Poulsen, Marie-Louise Krogh, Søren P. Sheikh, Esmaeil S. Nadimi

Publikováno v: BMC Research Notes, Vol 16, Iss 1, Pp 1-6 (2023)

Abstract Objective Identification of patients at high risk of aggressive prostate cancer is a major clinical challenge. With the view of developing artificial intelligence-based methods for identification of these patients, we are constructing a comp

Externí odkaz: https://doaj.org/article/28f4dec0e60745889c888a74da513a90

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Akademický článek

Modeling of Electronic Health Records for Time-Variant Event Learning Beyond Bio-Markers—A Case Study in Prostate Cancer

Autor: J. Herp, Jan-Matthias Braun, M. L. Cantuaria, Ashkan Tashk, T. B. Pedersen, M. H. A. Poulsen, M. Krogh, E. S. Nadimi, S. P. Sheikh

Publikováno v: IEEE Access, Vol 11, Pp 50295-50309 (2023)

Electronic health records (EHR) of large populations constitute a vast untapped resource for data-driven diagnosis and disease progression. We develop a model capable of predicting future steps in a patient’s journey for prostate cancer (PC) and it

Externí odkaz: https://doaj.org/article/fc77d292d62d4752abf4b720c9960f8e

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Akademický článek

Edge artificial intelligence wireless video capsule endoscopy

Autor: A. Sahafi, Y. Wang, C. L. M. Rasmussen, P. Bollen, G. Baatrup, V. Blanes-Vidal, J. Herp, E. S. Nadimi

Publikováno v: Scientific Reports, Vol 12, Iss 1, Pp 1-10 (2022)

Abstract Gastrointestinal (GI) tract diseases are responsible for substantial morbidity and mortality worldwide, including colorectal cancer, which has shown a rising incidence among adults younger than 50. Although this could be alleviated by regula

Externí odkaz: https://doaj.org/article/13b38d4446524593930973c426eba629

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Akademický článek

Artificial intelligence outperforms standard blood-based scores in identifying liver fibrosis patients in primary care

Autor: Victoria Blanes-Vidal, Katrine P. Lindvig, Maja Thiele, Esmaeil S. Nadimi, Aleksander Krag

Publikováno v: Scientific Reports, Vol 12, Iss 1, Pp 1-11 (2022)

Abstract For years, hepatologists have been seeking non-invasive methods able to detect significant liver fibrosis. However, no previous algorithm using routine blood markers has proven to be clinically appropriate in primary care. We present a novel

Externí odkaz: https://doaj.org/article/0624bc8a51a54f4f806983a06612b61d

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Akademický článek

In-vivo Experimental Validation of the Attenuation Path Loss Model for Localization of Wireless Implanted Transmitters at 2.45 GHz

Autor: Yana A. Salchak, Noor M. Albadri, Tracey Bjorkman, Cora Lau, Esmaeil S. Nadimi, Peter Bollen, Hugo G. Espinosa, David V. Thiel

Publikováno v: IEEE Access, Vol 10, Pp 84894-84912 (2022)

Wireless capsule endoscopy (WCE) is a modern, non-invasive method of gastrointestinal examination that can significantly reduce mortality and morbidity. One of the current challenges in WCE is the precise localization of the capsule. An accurate path

Externí odkaz: https://doaj.org/article/01f01c2cf35247c3be46de73d5f33ec5

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Akademický článek

AID-U-Net: An Innovative Deep Convolutional Architecture for Semantic Segmentation of Biomedical Images

Autor: Ashkan Tashk, Jürgen Herp, Thomas Bjørsum-Meyer, Anastasios Koulaouzidis, Esmaeil S. Nadimi

Publikováno v: Diagnostics, Vol 12, Iss 12, p 2952 (2022)

Semantic segmentation of biomedical images found its niche in screening and diagnostic applications. Recent methods based on deep learning convolutional neural networks have been very effective, since they are readily adaptive to biomedical applicati

Externí odkaz: https://doaj.org/article/01133c255267413da31dcfd9fcaf65a5

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