Reports in Medical Imaging最新文献_第2页

Artificial Intelligence in Predicting Clinical Outcome in COVID-19 Patients from Clinical, Biochemical and a Qualitative Chest X-Ray Scoring System 从临床、生化和定性胸片评分系统预测COVID-19患者临床结局的人工智能研究

Reports in Medical Imaging Pub Date : 2021-03-12 DOI: 10.2147/RMI.S292314

Andrea Esposito, E. Casiraghi, F. Chiaraviglio, A. Scarabelli, Elvira Stellato, G. Plensich, Giulia Lastella, Letizia Di Meglio, Stefano Fusco, E. Avola, A. Jachetti, C. Giannitto, D. Malchiodi, Marco Frasca, Afshin Beheshti, Peter N Robinson, Giorgio Valentini, Laura Forzenigo, G. Carrafiello

{"title":"Artificial Intelligence in Predicting Clinical Outcome in COVID-19 Patients from Clinical, Biochemical and a Qualitative Chest X-Ray Scoring System","authors":"Andrea Esposito, E. Casiraghi, F. Chiaraviglio, A. Scarabelli, Elvira Stellato, G. Plensich, Giulia Lastella, Letizia Di Meglio, Stefano Fusco, E. Avola, A. Jachetti, C. Giannitto, D. Malchiodi, Marco Frasca, Afshin Beheshti, Peter N Robinson, Giorgio Valentini, Laura Forzenigo, G. Carrafiello","doi":"10.2147/RMI.S292314","DOIUrl":"https://doi.org/10.2147/RMI.S292314","url":null,"abstract":"Purpose: To determine the performance of a chest radiograph (CXR) severity scoring system combined with clinical and laboratory data in predicting the outcome of COVID-19 patients Materials and Methods: We retrospectively enrolled 301 patients who had reverse transcriptase-polymerase chain reaction (RT-PCR) positive results for COVID-19 CXRs, clinical and laboratory data were collected A CXR severity scoring system based on a qualitative evaluation by two expert thoracic radiologists was defined Based on the clinical outcome, the patients were divided into two classes: moderate/mild (patients who did not die or were not intubated) and severe (patients who were intubated and/or died) ROC curve analysis was applied to identify the cut-off point maximizing the Youden index in the prediction of the outcome Clinical and laboratory data were analyzed through Boruta and Random Forest classifiers Results: The agreement between the two radiologist scores was substantial (kappa = 0 76) A radiological score ≥ 9 predicted a severe class: sensitivity = 0 67, specificity = 0 58, accuracy = 0 61, PPV = 0 40, NPV = 0 81, F1 score = 0 50, AUC = 0 65 Such performance was improved to sensitivity = 0 80, specificity = 0 86, accuracy = 0 84, PPV = 0 73, NPV = 0 90, F1 score = 0 76, AUC= 0 82, combining two clinical variables (oxygen saturation [SpO2]), the ratio of arterial oxygen partial pressure to fractional inspired oxygen [P/F ratio] and three laboratory test results (C-reactive protein, lymphocytes [%], hemoglobin) Conclusion: Our CXR severity score assigned by the two radiologists, who read the CXRs combined with some specific clinical data and laboratory results, has the potential role in predicting the outcome of COVID-19 patients","PeriodicalId":39053,"journal":{"name":"Reports in Medical Imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45235866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Overview of Current Trends, Techniques, Prospects, and Pitfalls of Artificial Intelligence in Breast Imaging 乳房成像中人工智能的当前趋势、技术、前景和缺陷综述

Reports in Medical Imaging Pub Date : 2021-03-11 DOI: 10.2147/RMI.S295205

S. Goyal

{"title":"An Overview of Current Trends, Techniques, Prospects, and Pitfalls of Artificial Intelligence in Breast Imaging","authors":"S. Goyal","doi":"10.2147/RMI.S295205","DOIUrl":"https://doi.org/10.2147/RMI.S295205","url":null,"abstract":": This review article aims to discuss current trends, techniques, and promising uses of artificial intelligence (AI) in breast imaging, apart from the pitfalls that may hinder its progress. It includes only the commonly used and basic terminology imperative for physicians to know. AI is not just a computerized approach but an interface between humans and machines. Apart from reducing workload and improved diagnostic accuracy, radiologists get more time for patient care or clinical work by using various machine learning techniques that augment their productivity. Inadequate data input with suboptimal pattern recognition, data extraction challenges, legal implications, and exorbitant costs are a few pitfalls that AI algorithms still face while analyzing and giving appropriate outcomes. Various machine learning approaches are used to construct prediction models for clinical decision support and ameliorating patient management. Since AI is still in its fledgling state, with many limitations for clinical implementation, clinical support and feedback are needed to avoid algorithmic errors. Hence, both machine learning and human insight complement each other in revolutionizing breast imaging.","PeriodicalId":39053,"journal":{"name":"Reports in Medical Imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45370859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Multiplanar Computed Tomographic Analysis of Frontal Cells According to International Frontal Sinus Anatomy Classification and Their Relation to Frontal Sinusitis 国际额窦解剖分类的额细胞多平面ct分析及其与额鼻窦炎的关系

Reports in Medical Imaging Pub Date : 2021-02-01 DOI: 10.2147/RMI.S291339

H. Pham, T. Tran, Thanh Van Nguyen, T. T. Thai

引用次数: 0

Aventriculy: A Rare Case Report 肺脏:一例罕见病例报告

Reports in Medical Imaging Pub Date : 2021-02-01 DOI: 10.2147/RMI.S281603

Abdi Dandena, S. Sisay, Abebe Mekonnen, K. Beza

引用次数: 2

Distinguishing Radiation Pneumonitis from Local Tumour Recurrence Following SBRT for Lung Cancer 癌症SBRT术后放射性肺炎与局部肿瘤复发的鉴别

Reports in Medical Imaging Pub Date : 2020-06-01 DOI: 10.2147/rmi.s176901

B. Frerker, G. Hildebrandt

{"title":"Distinguishing Radiation Pneumonitis from Local Tumour Recurrence Following SBRT for Lung Cancer","authors":"B. Frerker, G. Hildebrandt","doi":"10.2147/rmi.s176901","DOIUrl":"https://doi.org/10.2147/rmi.s176901","url":null,"abstract":": Radiation pneumonitis is one of the most common toxicities following SBRT for lung cancer. Although local control rates are good, a recurrent tumour is dif ﬁ cult to distinguish from radiation pneumonitis due to similar size and morphology. Therefore, early detection of a recurrent tumour is challenging, and moreover, it is crucial for affected patients, as early detection enables curative salvage therapy. Promising data exists to solve these challenges for late recurrences, for example, the analysis of high-risk CT features allows prediction of recurrence after 12 months. But particularly in cases of early recurrences and radiation pneumonitis, comprehensive data are lacking. Therefore, the aim of this study was to review the existing literature with special regard to radiological response assessment after stereotactic body radiotherapy and risk factors for predicting radiation pneumonitis or local recurrence. (PET) is encouraging. Huang et al developed a follow-up algorithm for response-assessment after SBRT, in which a PET is recommended in some cases, and Dong et al demonstrated that patients with high metabolic activity (described as SUVmax) before treatment had a worse overall survival. Nevertheless, the interpretation of a PET-scan should be done carefully as there is no optimal SUVmax threshold for predicting local recurrence or radiation pneumonitis. Another approach is to analyse of dosimetric parameters before performing SBRT, and indeed, some parameters seem to be associated with radiation pneumonitis, but again no speci ﬁ c dose constraints are found yet. We found promising data in the literature, but the results are controversial, and a conclusion could not be drawn.","PeriodicalId":39053,"journal":{"name":"Reports in Medical Imaging","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2147/rmi.s176901","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46508214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Shedding Light On The Human Auditory Cortex: A Review Of The Advances In Near Infrared Spectroscopy (NIRS) 揭示人类听觉皮层:近红外光谱研究进展综述

Reports in Medical Imaging Pub Date : 2019-10-01 DOI: 10.2147/rmi.s174633

Samantha C Harrison, D. Hartley

引用次数: 7

Neurite orientation and dispersion density imaging: clinical utility, efficacy, and role in therapy 神经突定向和弥散密度成像:临床应用、疗效和治疗中的作用

Reports in Medical Imaging Pub Date : 2019-08-01 DOI: 10.2147/RMI.S194083

Daichi Sone

引用次数: 0

Identification of patterns of abnormalities seen on DaTscan™ SPECT imaging in patients with non-Parkinson’s movement disorders 在非帕金森运动障碍患者的DaTscan™SPECT成像上看到的异常模式的识别

Reports in Medical Imaging Pub Date : 2019-07-23 DOI: 10.2147/RMI.S201890

S. Calle, L. Dawood, Niroj R Tripathee, Chunyan C Cai, Harleen Kaur, David Wan, Henry I. Ibekwe, I. Gayed

引用次数: 4

Components and implementation of a picture archiving and communication system in a prototype application 一个图片存档和通信系统的组件和实现的原型应用程序

Reports in Medical Imaging Pub Date : 2018-12-01 DOI: 10.2147/RMI.S179268

H. H. Khaleel, R. Rahmat, D. M. Zamrin

引用次数: 1

Stereology volume analysis to evaluate teeth’s root using CBCT images 利用CBCT图像对牙根进行立体体积分析

Reports in Medical Imaging Pub Date : 2018-11-01 DOI: 10.2147/RMI.S153169

A. Fadili, Abdelali Halimi, H. Benyahia, F. Zaoui

{"title":"Stereology volume analysis to evaluate teeth’s root using CBCT images","authors":"A. Fadili, Abdelali Halimi, H. Benyahia, F. Zaoui","doi":"10.2147/RMI.S153169","DOIUrl":"https://doi.org/10.2147/RMI.S153169","url":null,"abstract":"Purpose: The purpose of the present study was to evaluate a semiautomated segmentation tool designated for the volume measurements of teeth root; this method is based on three-dimensional and focused to follow-up the root’s volume when an orthodontic treatment occurred. Materials and methods: In order to evaluate a semiautomated segmentation tool, a cone beam computed tomography (CBCT) was selected with 20 intact teeth (10 maxillary and 10 mandibular teeth), CBCT images were imported into the ImageJ software, and the root volumes were measured using two methods (the semiautomated segmentation and the manual segmenta-tion). Both segmentations are carried out by two experts; the manual segmentation served as a reference method and considered as the “gold standard”. The data were analyzed using the Bland–Altman analysis to compare the agreement between methods, and the intraclass correlation coefficient (ICC) was used to assess the interobserver reliability. Results: The Bland–Altman analysis revealed the agreement between measurements on semiautomated segmentation and manual segmentation, with a mean bias of –2.09 mm 3 and the 95% limits of agreement of –11.38 to 7.21 mm 3 . The ICC was 0.999 for semiautomated segmentation method and 0.999 for manual segmentation method. Conclusion: The use of stereology employing the ImageJ software and CBCT images provide an accurate and reliable semiautomated segmentation, leading to an approach of volume quantitative analysis to evaluate and follow-up the root’s volume when orthodontic treatment occurred. Further clinical studies are necessary to explore this method.","PeriodicalId":39053,"journal":{"name":"Reports in Medical Imaging","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2147/RMI.S153169","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41720093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3