| 3 | No significant difference in clinically relevant findings between Pillcam~? SB3 and Pillcam~? SB2 capsules in a United States veteran population显示文摘BACKGROUND Capsule endoscopy(CE) allows for a non-invasive small bowel evaluation for a wide range of gastrointestinal(GI) symptoms and diseases. Capsule technology has been rapidly advancing over recent years, often improving image frequency and quality. The Pillcam~? SB3(SB3) capsule is one such technology that offers an adaptive frame rate advantage over the previous versions of the capsule the Pillcam~? SB2(SB2). Some have proposed that this improvement in capsule technology may lead to increased diagnostic yields; however, real world clinical data is currently lacking.AIM To evaluate the clinically relevant findings of SB3 and SB2 capsules in a population of United States veterans.METHODS A retrospective analysis of 260 consecutive CE studies was performed including130 SB3 and 130 SB2 capsule studies. Recorded variables included: age, gender,type of capsule, body mass index, exam completion, inpatient status, opioid use,diabetes, quality of preparation, gastric transit time, small bowel transit time,indication, finding, and if the exam resulted in a change in clinical management.The primary outcome measured was the detection of clinically relevant findings between SB3 and SB2 capsules.RESULTS Mean age of the study population was 67.1 ± 10.4 years and 94.2% of patients were male. Of these 28.1% were on opioid users. The most common indications for capsule procedure were occult GI bleeding(74.6%) and overt GI bleeding(14.6%). Rates of incomplete exam were similar between SB3 and SB2 groups(16.9% vs 9.2%, P = 0.066). The overall rate of clinically relevant finding was48.9% in our study. No significant difference was observed in SB3 vs SB2 capsules for clinically relevant findings(46.2% vs 51.5%, P = 0.385) or change in clinical management(40.8% vs 50.0%, P = 0.135).CONCLUSION Our study found no significant difference in clinically relevant findings between SB3 and SB2 capsules. | Tyler D Aasen David Wilhoite Aynur Rahman Kalpit Devani Mark Young James Swenson | 2019 | World Journal of Gastrointestinal Endoscopy2019，11，2： | 0 |
| 4 | Disease detection,severity prediction,and crop loss estimation in MaizeCrop using deep learning显示文摘The increasing gap between the demand and productivity of maize crop is a point of concern for the food industry,and farmers.Its'susceptibility to diseases such as Turcicum Leaf Blight,and Rust is a major cause for reducing its production.Manual detection,and classification of these diseases,calculation of disease severity,and crop loss estimation is a time-consuming task.Also,it requires expertise in disease detection.Thus,there is a need to find an alternative for automatic disease detection,severity prediction,and crop loss estimation.The promising results of machine learning,and deep learning algorithms in pattern recognition,object detection,and data analysis motivate researchers to employ these techniques for disease detection,classification,and crop loss estimation in maize crop.The research works available in literature,have proven their potential in automatic disease detection using machine learning,and deep learning models.But,there is a lack none of these works a reliable and real-life labelled dataset for training these models.Also,none of the existing works focus on severity prediction,and crop loss estimation.The authors in this manuscript collect the real-life dataset labelled by plant pathologists.They propose a deep learning-based framework for pre-processing of dataset,automatic disease detection,severity prediction,and crop loss estimation.It uses the K-Means clustering algorithm for extracting the region of interest.Next,they employ the customized deep learning model‘MaizeNet’for disease detection,severity prediction,and crop loss estimation.The model reports the highest accuracy of 98.50%.Also,the authors perform the feature visualization using the Grad-CAM.Now,the proposed model is integrated with a web application to provide a userfriendly interface.The efficacy of the model in extracting the relevant features,a smaller number of parameters,low training time,high accuracy favors its importance as an assisting tool for plant pathology experts.The copyright for the associated web application‘Maize-Disease-Detector’is filed with diary number:17006/2021-CO/SW. | Nidhi Kundu Geeta Rani Vijaypal Singh Dhaka Kalpit Gupta Siddaiah Chandra Nayaka Eugenio Vocaturo Ester Zumpano | 2022 | Artificial Intelligence in Agriculture2022，，1： | 0 |
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