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- 저자 : Ashok Dahal (검색결과 2 건)
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( Amol Sonyabapu Dahale ),( Amarender Singh Puri ),( Sanjeev Sachdeva ),( Anil K Agarwal ),( Ajay Kumar ),( Ashok Dalal ),( Pritul D Saxena ) 대한소화기학회 2021 대한소화기학회지 Vol.78 No.3
Background/Aims: Although peritoneal tuberculosis (TB) is one of the important differential diagnoses among cirrhotic patients with ascites, a peritoneal biopsy is not always available. High ascitic fluid adenosine deaminase (ADA) has been indicative of peritoneal TB. On the other hand, studies to assess its diagnostic utility based on the confirmation of peritoneal biopsy in cirrhotic patients are scarce. Methods: Patients with new-onset ascites were enrolled prospectively from a tertiary hospital. Peritoneal biopsy was applied according to clinical judgment when required. Based on pathology diagnosis of the peritoneum, the diagnostic efficacy of ascitic fluid ADA for peritoneal TB was evaluated in total and cirrhotic patients, respectively. Results: Among 286 patients enrolled, 78 were diagnosed with peritoneal TB. One hundred and thirty-two patients had cirrhosis, and 30 of those were diagnosed with peritoneal TB. The mean ADA was 72.2 U/L and 22.7 U/L in the peritoneal and non-peritoneal TB group, respectively, among the total study population, and 64.0 U/L and 19.1 U/L in the peritoneal and non-peritoneal TB group, respectively, among the subgroup with cirrhosis. The area under the curve for ADA to diagnose peritoneal TB was 0.96 in the total study population with a cutoff value of 41.1 U/L, and 0.93 in cirrhotic patients with a cutoff value of 39.9 U/L. Conclusions: The ascitic fluid ADA measurements showed high diagnostic performance for peritoneal tuberculosis in patients with ascites regardless of cirrhosis at a similar cutoff value. (Korean J Gastroenterol 2021;78:168-176)
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Methods in the spatial deep learning: current status and future direction
Bhogendra Mishra,Ashok Dahal,Nirajan Luintel,Tej Bahadur Shahi,Saroj Panthi,Shiva Pariyar,Bhoj Raj Ghimire 대한공간정보학회 2022 Spatial Information Research Vol.30 No.2
A deep neural network (DNN), evolved from a traditional artificial neural network, has been seamlessly adapted for the spatial data domain over the years. Deep learning (DL) has been widely applied for a number of applications and a variety of thematic domains. This article reports on a systematic review of methods adapted in major DNN applications with remote sensing data published between 2010 and 2020 aiming to understand the major application area, a framework for model development and the prospect of DL application in spatial data analysis. It has been found that image fusion, change detection, scene classification, image segmentation, and feature detection are the most commonly used application areas. Based on the publication in these thematic areas, a generic framework has been devised to guide a model development using DL based on the methods followed in the past. Finally, recent trends and prospects in terms of data, method, and application of deep learning with remote sensing data are discussed. The review finds that while DL-based approaches have the potential to unfold hidden information, they face challenges in selecting the most appropriate data, methods, and model parameterizations which may hinder the performance. The increasing trend of application of DL in the spatial domain is expected to leverage its strength at its optimum to the research frontiers.
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