Positive Pattern Recognition System Using Alanine Aminotransferase, TypeIV Collagen 7S and E Value (Liver Stiffness) for the Diagnosis of Nonalcoholic Steatohepatitis Based on Natural History

Author(s):  
Masayuki Tsujisaki ◽  
Shigeru Sasaki ◽  
Noriyuki Akutsu ◽  
Takenori Takamura ◽  
Tetsuyuki Igarashi ◽  
...  

Abstract Background: A simple diagnostic system for nonalcoholic fatty liver disease (NAFLD) instead of a biopsy is expected. We investigated the possibility of a positive pattern recognition system for evaluation of nonalcoholic fatty liver (NAFL) and the stages of nonalcoholic steatohepatitis (NASH). Methods: 68 Japanese patients with biopsy-proven NAFLD were enrolled. Serological biomarkers and markers obtained by medical imaging were investigated to explore candidates for diagnostic system. After selected markers were statistically evaluated, every patient was distributed in pattern combinations.Results: We selected three markers based on natural history and decided the critical values: alanine aminotransferase/ALT (persistent ≧ 45 IU/L) as hepatitis marker, type Ⅳ collagen 7S (≧ 5.1 ng/ml) as fibrosis one and E value (≧5.5 kPa) as stiffness one. After we statistically evaluated their accuracies, every patient was classified into their combination patterns. Major patterns were limited to four. Comparing relationships between histological classifications and positive patterns , the patients with NAFL were mainly distributed in pattern (ALT, type Ⅳ collagen , E value : -, -, -), those with NASH stage 0-1 in (+, -, +), those with NASH stage 2-3 in (+, +, +), and those with NASH stage 4 in (-, +, +).Conclusion: The positive patters changed with NAFL and NASH conditions. Our results showed a correlation between the positive patterns using three markers and histological results. Positive pattern recognition system based on natural history is useful in a differential diagnosis of NAFLD and for evaluation of the severity of fibrosis in patients with NASH.

2018 ◽  
Vol 159 ◽  
pp. 02048
Author(s):  
Rahayu ◽  
G.T. Anuraga ◽  
H. Prasetia ◽  
Umar Khayam

Partial Discharge (PD) is one of the causes of insulation deteriorisation mode and impacts on the reliability of high voltage equipment. Therefore, PD measurement is used for diagnostic technique of high voltage equipment. Diagnostic output of high voltage equipment contain information about PD type, PD cause, PD location and PD severity. after identification, a proper preventive maintenance pattern can be performed. Therefore PD pattern recognition system is very important on PD diagnostic system to recognize the PD pattern and determine the level of hazard that occurs in specimen object or high voltage equipment‥ In this paper, PD pattern recognition system is designed with fractal geometry approach and support vector machine (SVM) algorithm. The coding and programming of graphical user interface of the application is done. Each PD type and hazard level on various insulating materials (solid, liquid and gas) have the dimensions of the fractal and the lacunarity. The type of PD (void, corona) and its danger level (bad, fair and good) can be identified with the support vector machine (SVM)


2016 ◽  
Vol 67 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Michiie Sakamoto ◽  
Hanako Tsujikawa ◽  
Kathryn Effendi ◽  
Hidenori Ojima ◽  
Kenichi Harada ◽  
...  

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