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Your search keyword '"Nadimi, E. S."' showing total 21 results
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21 results on '"Nadimi, E. S."'

4. Colonkapselendoskopi

Author

Bjørsum-Meyer, T., Benedicte Schelde-Olesen, Koulaouzidis, A., Nadimi, E. S., and Baatrup, G.

Subjects
digestive system diseases
Abstract

Colon capsule endoscopy (CCE) was introduced in 2006 as a novel way to visualise the colonic mucosa. Initially, CCE validity was limited by low completion rates (CR) and poor image quality. Through technical progress and improved bowel preparations, CCE now offers an adjunct to diagnostic colonoscopy. As referred in this review, several studies have shown promising results regarding polyp detection rates by the use of CCE. Improvements in CR and quality of bowel preparation are needed for CCE to be on a par with conventional colonoscopy. Research in artificial intelligence is evolving to aid in diagnostics and staging using CCE.

Published
2022

8. An adaptive localization technique for wireless capsule endoscopy

Author

Farhadi, H., Atai, J., Skoglund, Mikael, Nadimi, E. S., Pahlavan, K., Tarokh, V., Farhadi, H., Atai, J., Skoglund, Mikael, Nadimi, E. S., Pahlavan, K., and Tarokh, V.

Abstract

Wireless capsule endoscopy (WCE) is an emerging technique to enhance Gastroenterologists information about the patient's gastrointestinal (G.I.) tract. Localization of capsule inside human body in this case is an active area of research. This can be thought of as a sub-domain of micro and bio-robotics fields. If capsule and micro-robot localization problem in human body is solved, then it may potentially lead to less invasive treatments for G.I. diseases and other micro-robot assisted medical procedures. Several approaches have been investigated by the researchers to estimate capsule location. The proposed solutions are mainly static and thus prone to the changes in the propagation medium. We propose an adaptive algorithm based on expectation maximization technique for capsule localization. The proposed algorithm adaptively updates the estimated location based on the received radio frequency (RF) signal measurements., QC 20161207

Published
2016
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9. Bayesian-based localization in inhomogeneous transmission media:Proceedings of the 2013 Research in Adaptive and Convergent Systems, RACS 2013

Author

Nadimi, E. S., Blanes-Vidal, V., and Johansen, P. M.

Subjects
bayesian inference, bayesian inference heterogeneous RF transmission medium wireless body-area sensor networks, wireless body-area sensor networks, heterogeneous RF transmission medium
Abstract

In this paper, we propose a novel robust probabilistic approach based on the Bayesian inference using received-signal-strength (RSS) measurements with varying path-loss exponent. We derived the probability density function (pdf) of the distance between any two sensors in the network with heterogeneous transmission medium as a function of the given RSS measurements and the characteristics of the heterogeneous medium. The results of this study show that the localization mean square error (MSE) of the Bayesian-based method outperformed all other existing localization approaches. © 2013 ACM. In this paper, we propose a novel robust probabilistic approach based on the Bayesian inference using received-signal-strength (RSS) measurements with varying path-loss exponent. We derived the probability density function (pdf) of the distance between any two sensors in the network with heterogeneous transmission medium as a function of the given RSS measurements and the characteristics of the heterogeneous medium. The results of this study show that the localization mean square error (MSE) of the Bayesian-based method outperformed all other existing localization approaches.

Published
2013
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12. Styret afgræsning

Author

Oudshoorn, Frank W, Nadimi, E S, and Jørgensen, R N

Published
2010

20. Automatisk overvågning af korntørreri

Author

Ole Green, Jakob Juul Larsen, Erik Fløjgaard Kristensen, Nadimi, E. S., Johannes Ravn Jørgensen, Ibrahim Abdel Fattah Abdel Hamid Ibrahim, Dionysis Bochtis, and Claus Grøn Sørensen

21. Colon capsule endoscopy versus CT colonography after incomplete colonoscopy. Application of artificial intelligence algorithms to identify complete colonic investigations.

Author

Deding U, Herp J, Havshoei AL, Kobaek-Larsen M, Buijs MM, Nadimi ES, and Baatrup G

Subjects
Aged, Capsule Endoscopy methods, Colon diagnostic imaging, Colon pathology, Colonic Polyps pathology, Colonoscopy methods, Colonoscopy statistics & numerical data, Colorectal Neoplasms pathology, Early Detection of Cancer, Female, Humans, Intestinal Mucosa diagnostic imaging, Intestinal Mucosa pathology, Male, Middle Aged, Prospective Studies, Sensitivity and Specificity, Artificial Intelligence, Capsule Endoscopy statistics & numerical data, Colonic Polyps diagnosis, Colonography, Computed Tomographic statistics & numerical data, Colorectal Neoplasms diagnosis, Image Processing, Computer-Assisted methods
Abstract

Background: Guidelines suggest computed tomography colonography (CTC) following incomplete optical colonoscopy (OC). Colon capsule endoscopies (CCE) have been suggested as an alternative, although completion rates have been unsatisfactory. Introduction of artificial intelligence (AI)-based localization algorithms of the camera capsules may enable identification of incomplete CCE investigations overlapping with incomplete OCs., Objective: The study aims to investigate relative sensitivity of CCE compared with CTC following incomplete OC, investigate the completion rate when combining results from the incomplete OC and CCE, and develop a forward-tracking algorithm ensuring a safe completeness of combined investigations., Methods: In this prospective paired study, patients with indication for CTC following incomplete OC were included for CCE and CTC. Location of CCE abortion and OC abortion were registered to identify complete combined investigations. AI-based algorithm for localization of capsules were developed reconstructing the passage of the colon., Results: In 237 individuals with CTC indication; 105 were included, of which 97 underwent both a CCE and CTC. CCE was complete in 66 (68%). Including CCEs which reached most oral point of incomplete OC, 73 (75%) had complete colonic investigations; 78 (80%) had conclusive investigations. Relative sensitivity of CCE compared with CTC was 2.67 (95% confidence interval (CI) 1.76;4.04) for polyps >5 mm and 1.91 (95% CI 1.18;3.09) for polyps >9 mm. An AI-based algorithm was developed., Conclusion: Sensitivity of CCE following incomplete OC was superior to CTC. Introducing and improving algorithm-based localization of capsule abortion may increase identification of overall complete investigation rates following incomplete OC.ClinicalTrials.gov identifier: NCT02826993.

Published
2020
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