High-throughput HBV DNA and HCV RNA detection system using a nucleic acid purification robot and real-time detection PCR: its application to analysis of posttransfusion hepatitis

A highly reproducible and sensitive real-time detection assay based on TaqMan technology was developed for the detection of hepatitis B virus (HBV) DNA and compared with two commercially available assays. The assay was validated with the Viral Quality Control panel, which also includes EUROHEP HBV DNA standards. This real-time PCR detection system had a dynamic range of 373 to 1010 genome copies per ml and showed an excellent correlation with both the commercial HBV Digene Hybrid Capture II microplate assay (Digene Diagnostics) and the HBV MONITOR assay (Roche Diagnostics). To demonstrate its clinical utility, four chronically HBV-infected patients treated with lamuvidine were monitored using the three different assays. From the results we concluded that this assay is an excellent alternative for monitoring of HBV-infected patients in routine diagnostics and clinical practice, enabling the analysis of a large dynamic range of HBV DNA in a single, undiluted sample.

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Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405615666190114151255 ◽

2020 ◽

Vol 16 (5) ◽

pp. 584-591 ◽

Cited By ~ 1

Author(s):

Muhammad Hanif Ahmad Nizar ◽

Chow Khuen Chan ◽

Azira Khalil ◽

Ahmad Khairuddin Mohamed Yusof ◽

Khin Wee Lai

Keyword(s):

Neural Network ◽

Neural Networks ◽

Heart Disease ◽

Aortic Valve ◽

Real Time ◽

Convolutional Neural Networks ◽

Valvular Heart Disease ◽

Detection System ◽

Processing Unit ◽

Real Time Detection

Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.

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Computer-Aided Colon Polyp Detection on High Resolution Colonoscopy Using Transfer Learning Techniques

Sensors ◽

10.3390/s21165315 ◽

2021 ◽

Vol 21 (16) ◽

pp. 5315

Author(s):

Chia-Pei Tang ◽

Kai-Hong Chen ◽

Tu-Liang Lin

Keyword(s):

Real Time ◽

Transfer Learning ◽

Early Recognition ◽

Detection System ◽

Colon Polyp ◽

Colon Polyps ◽

Polyp Detection ◽

Detection Model ◽

Real Time Detection ◽

Polyp Type

Colonoscopies reduce the incidence of colorectal cancer through early recognition and resecting of the colon polyps. However, the colon polyp miss detection rate is as high as 26% in conventional colonoscopy. The search for methods to decrease the polyp miss rate is nowadays a paramount task. A number of algorithms or systems have been developed to enhance polyp detection, but few are suitable for real-time detection or classification due to their limited computational ability. Recent studies indicate that the automated colon polyp detection system is developing at an astonishing speed. Real-time detection with classification is still a yet to be explored field. Newer image pattern recognition algorithms with convolutional neuro-network (CNN) transfer learning has shed light on this topic. We proposed a study using real-time colonoscopies with the CNN transfer learning approach. Several multi-class classifiers were trained and mAP ranged from 38% to 49%. Based on an Inception v2 model, a detector adopting a Faster R-CNN was trained. The mAP of the detector was 77%, which was an improvement of 35% compared to the same type of multi-class classifier. Therefore, our results indicated that the polyp detection model could attain a high accuracy, but the polyp type classification still leaves room for improvement.

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Real-time Detection System of Electrical Disturbances for Remote Communication Stations and Smart Grid

IEEE Latin America Transactions ◽

10.1109/tla.2017.8071233 ◽

2017 ◽

Vol 15 (10) ◽

pp. 1894-1900

Author(s):

Wesley Natanael Gallo ◽

Tales Heimfarth ◽

Danton Diego Ferreira ◽

Thais Martins Mendes

Keyword(s):

Smart Grid ◽

Real Time ◽

Detection System ◽

Remote Communication ◽

Real Time Detection

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Evaluation of Nucleic Acid Purification-Free One-Step Real-Time PCR Kit for Norovirus Infection

Japanese Journal of Food Microbiology ◽

10.5803/jsfm.34.135 ◽

2017 ◽

Vol 34 (2) ◽

pp. 135-139 ◽

Cited By ~ 1

Author(s):

Naomi Sakon

Keyword(s):

Nucleic Acid ◽

Real Time ◽

Real Time Pcr ◽

One Step ◽

Nucleic Acid Purification

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Real-time detection system of cat-eye effect target based on embedded platform

High Power Laser and Particle Beams ◽

10.3788/hplpb20152701.11013 ◽

2015 ◽

Vol 27 (1) ◽

pp. 11013

Author(s):

韩磊 HanLei ◽

张海洋 Zhang Haiyang ◽

马雪松 Ma Xuesong ◽

赵长明 Zhao Changming ◽

杨苏辉 Yang Suhui

Keyword(s):

Real Time ◽

Detection System ◽

Embedded Platform ◽

Real Time Detection

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Robust Real-Time Detection of Laparoscopic Instruments in Robot Surgery Using Convolutional Neural Networks with Motion Vector Prediction

Applied Sciences ◽

10.3390/app9142865 ◽

2019 ◽

Vol 9 (14) ◽

pp. 2865 ◽

Cited By ~ 4

Author(s):

Kyungmin Jo ◽

Yuna Choi ◽

Jaesoon Choi ◽

Jong Woo Chung

Keyword(s):

Neural Networks ◽

Real Time ◽

Convolutional Neural Networks ◽

Detection System ◽

Motion Vector ◽

Detection Algorithm ◽

Surgical Instruments ◽

The Mean ◽

Real Time Detection ◽

Motion Vector Prediction

More than half of post-operative complications can be prevented, and operation performances can be improved based on the feedback gathered from operations or notifications of the risks during operations in real time. However, existing surgical analysis methods are limited, because they involve time-consuming processes and subjective opinions. Therefore, the detection of surgical instruments is necessary for (a) conducting objective analyses, or (b) providing risk notifications associated with a surgical procedure in real time. We propose a new real-time detection algorithm for detection of surgical instruments using convolutional neural networks (CNNs). This algorithm is based on an object detection system YOLO9000 and ensures continuity of detection of the surgical tools in successive imaging frames based on motion vector prediction. This method exhibits a constant performance irrespective of a surgical instrument class, while the mean average precision (mAP) of all the tools is 84.7, with a speed of 38 frames per second (FPS).

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