Contrast-enhanced ultrasonography parameters in neural network diagnosis of liver tumors

Abstract

Neural network analysis of contrast-enhanced ultrasonography - obtained TICs seems a promising field of development for future techniques, providing fast and reliable diagnostic aid for the clinician.

Quantitative contrast-enhanced harmonic EUS in differential diagnosis of focal pancreatic masses (with videos)

“…The choice of a computer diagnostic system for classification of pancreatic lesions complemented the statistical study comparing the different parameters obtained through TIC analysis. The layout of the ANN system has proven efficient in previous studies that we conducted on imaging parameters 19,53,54 ; it is not prone to over-fitting and also provides accurate and timely results. Our system received accurate parameters obtained from the offline VueBox software, while we conducted a pre-test to determine their validity.…”

“…The use of computer-aided diagnostic systems in medicine becomes increasingly popular for the modern management of patients. [17][18][19] Their usefulness is still under review, and insufficient data on their actual clinical significance is currently published. Artificial neural networks (ANNs) emerge as appropriate approaches to classification problems found in routine investigation of medical cases, and their accuracy in complex medical imaging tasks seems promising.…”

“…Artificial neural networks (ANNs) emerge as appropriate approaches to classification problems found in routine investigation of medical cases, and their accuracy in complex medical imaging tasks seems promising. 19 Previous literature describes ANNs as tools that can greatly improve the diagnostic yield of quantitative image interpretation. 20,21 Our multicenter study aimed to statistically validate the usefulness of TIC analysis-derived parameters obtained from post-processing of CEH-EUS recordings with a commercially available software solution in differentiating PC and mass-forming CP; a secondary objective was to validate the usefulness of an ANN approach at classifying medical cases based on different quantitative parameters.…”

A systematic review on the evaluation and characteristics of computer-aided diagnosis systems

“…Finally, for classification tasks, we observed the use of classifiers such as k-nearest neighbor (Filipczuk et al, 2013;Gedik and Atasoy, 2013;Gopinath and Shanthi, 2013;He et al, 2011;Muramatsu et al, 2013;Nava et al, 2014;Odeh et al, 2006;Osman et al, 2009;Raja et al, 2010;Verikas et al, 2006), artificial neural networks (Barhoumi et al, 2007;Geetha et al, 2008;Jasmine et al, 2009;López et al, 2008;Raja et al, 2007;Streba et al, 2012;Verma, 2009;Wu et al, 2006), Bayesian classifiers (Ampeliotis et al, 2007;Bhooshan et al, 2011;Garnavi et al, 2012;Gruszauskas et al, 2008Gruszauskas et al, , 2009Retter et al, 2013;Tolouee et al, 2011) techniques based on linear discriminant analysis (Lee et al, 2009;Muramatsu et al, 2013;Tanner et al, 2006) and logistic regression models (Shen et al, 2007;Tanner et al, 2006).…”

Experimental approbation of a new ultrosound contrast agent based on sulfur geksafluoride in diagnostics of focal liver lesions of inflammatory genesis

“…В отечествен-ной клинической медицине ýхография относится к наиболее широко используемым методам ви-зуализации [1,2]. Использование контрастных препаратов при ультразвуковом исследовании широко распространено в мировой клинической практике [3][4][5][6][7][8][9][10][11][12]. Использование ультразвуковых контрастных препаратов в условиях применения современного оборудования позволяет выявлять патологический очаг размером от 5 мм, значи-тельно повышая уровень диагностической точно-сти ультразвукового исследования [7,12].…”