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
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
{"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}
: 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.
{"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}
{"title":"Multiplanar Computed Tomographic Analysis of Frontal Cells According to International Frontal Sinus Anatomy Classification and Their Relation to Frontal Sinusitis","authors":"H. Pham, T. Tran, Thanh Van Nguyen, T. T. Thai","doi":"10.2147/RMI.S291339","DOIUrl":"https://doi.org/10.2147/RMI.S291339","url":null,"abstract":"","PeriodicalId":39053,"journal":{"name":"Reports in Medical Imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45110907","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}
: In this paper, we will describe a week old neonate who was referred to Black Lion Referral Hospital with a diagnosis of ambiguous genitalia and was screened for associated congenital anomalies. The neonate was evaluated with Trans-fontanel Ultrasound and Brain MRI, which showed the absence of the lateral and third ventricles and associated Intracranial multiple anomalies.
{"title":"Aventriculy: A Rare Case Report","authors":"Abdi Dandena, S. Sisay, Abebe Mekonnen, K. Beza","doi":"10.2147/RMI.S281603","DOIUrl":"https://doi.org/10.2147/RMI.S281603","url":null,"abstract":": In this paper, we will describe a week old neonate who was referred to Black Lion Referral Hospital with a diagnosis of ambiguous genitalia and was screened for associated congenital anomalies. The neonate was evaluated with Trans-fontanel Ultrasound and Brain MRI, which showed the absence of the lateral and third ventricles and associated Intracranial multiple anomalies.","PeriodicalId":39053,"journal":{"name":"Reports in Medical Imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48571051","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}
: 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.
{"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}
Imaging the auditory cortex can prove challenging using neuroimaging methodologies due to interfering noise from the scanner in fMRI and the low spatial resolution of EEG. Optical imaging provides a new and exciting option for exploring this key cortical area. This review presents a brief history of optical imaging, followed by an exploration of how advances in optical imaging technologies have increased the understanding of the functions and processes within the auditory cortex. In particular, the benefits and limitations of using functional near infrared spectroscopy (fNIRS) on complex populations such as infants and individuals with hearing loss are explored, along with suggestions for future research developments.
{"title":"Shedding Light On The Human Auditory Cortex: A Review Of The Advances In Near Infrared Spectroscopy (NIRS)","authors":"Samantha C Harrison, D. Hartley","doi":"10.2147/rmi.s174633","DOIUrl":"https://doi.org/10.2147/rmi.s174633","url":null,"abstract":"Imaging the auditory cortex can prove challenging using neuroimaging methodologies due to interfering noise from the scanner in fMRI and the low spatial resolution of EEG. Optical imaging provides a new and exciting option for exploring this key cortical area. This review presents a brief history of optical imaging, followed by an exploration of how advances in optical imaging technologies have increased the understanding of the functions and processes within the auditory cortex. In particular, the benefits and limitations of using functional near infrared spectroscopy (fNIRS) on complex populations such as infants and individuals with hearing loss are explored, along with suggestions for future research developments.","PeriodicalId":39053,"journal":{"name":"Reports in Medical Imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2147/rmi.s174633","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46353151","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}
Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan Abstract: In the field of diffusion magnetic resonance imaging (MRI) for neuroimaging, white matter tracts have traditionally been analyzed using diffusion tensor imaging (DTI) measures, such as fractional anisotropy. However, recent advances in diffusion MRI have provided further information on brain microstructures using multi-shell protocols of diffusion MRI. Neurite orientation dispersion and density imaging (NODDI) is one such emerging advanced diffusion MRI method that enables investigation of the neurite density and neurite orientation dispersion of brain microstructures. NODDI was developed as a practical and clinically feasible diffusion MRI technique to evaluate the microstructural complexity of dendrites and axons. This review shed light on recent studies on the use of NODDI in human brain. Indeed, a growing number of studies are using NODDI to examine neurological and psychiatric disorders, with most reporting its clinical utility. The time has thus come, for us to seriously consider the clinical use of NODDI.
{"title":"Neurite orientation and dispersion density imaging: clinical utility, efficacy, and role in therapy","authors":"Daichi Sone","doi":"10.2147/RMI.S194083","DOIUrl":"https://doi.org/10.2147/RMI.S194083","url":null,"abstract":"Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan Abstract: In the field of diffusion magnetic resonance imaging (MRI) for neuroimaging, white matter tracts have traditionally been analyzed using diffusion tensor imaging (DTI) measures, such as fractional anisotropy. However, recent advances in diffusion MRI have provided further information on brain microstructures using multi-shell protocols of diffusion MRI. Neurite orientation dispersion and density imaging (NODDI) is one such emerging advanced diffusion MRI method that enables investigation of the neurite density and neurite orientation dispersion of brain microstructures. NODDI was developed as a practical and clinically feasible diffusion MRI technique to evaluate the microstructural complexity of dendrites and axons. This review shed light on recent studies on the use of NODDI in human brain. Indeed, a growing number of studies are using NODDI to examine neurological and psychiatric disorders, with most reporting its clinical utility. The time has thus come, for us to seriously consider the clinical use of NODDI.","PeriodicalId":39053,"journal":{"name":"Reports in Medical Imaging","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2147/RMI.S194083","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42753362","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}
S. Calle, L. Dawood, Niroj R Tripathee, Chunyan C Cai, Harleen Kaur, David Wan, Henry I. Ibekwe, I. Gayed
1Neuroradiology Section, Department of Diagnostic and Interventional Imaging, The University of Texas Health Science Center at Houston, Houston, TX, USA; 2Department of Surgery, Baylor College of Medicine, Houston, TX, USA; 3Body Imaging Section, Department of Diagnostic and Interventional Imaging, The University of Texas Health Science Center at Houston, Houston, TX, USA; 4Clinical and Translational Sciences Section, The University of Texas Health Science Center at Houston, Houston, TX, USA; 5Nuclear Medicine Section, Department of Diagnostic and Interventional Imaging, The University of Texas Health Science Center at Houston, Houston, TX, USA; 6Memorial Hermann Hospital, Texas Medical Center, Houston, TX, USA Purpose: To identify patterns of abnormalities using DaTscan single photon emission-
{"title":"Identification of patterns of abnormalities seen on DaTscan™ SPECT imaging in patients with non-Parkinson’s movement disorders","authors":"S. Calle, L. Dawood, Niroj R Tripathee, Chunyan C Cai, Harleen Kaur, David Wan, Henry I. Ibekwe, I. Gayed","doi":"10.2147/RMI.S201890","DOIUrl":"https://doi.org/10.2147/RMI.S201890","url":null,"abstract":"1Neuroradiology Section, Department of Diagnostic and Interventional Imaging, The University of Texas Health Science Center at Houston, Houston, TX, USA; 2Department of Surgery, Baylor College of Medicine, Houston, TX, USA; 3Body Imaging Section, Department of Diagnostic and Interventional Imaging, The University of Texas Health Science Center at Houston, Houston, TX, USA; 4Clinical and Translational Sciences Section, The University of Texas Health Science Center at Houston, Houston, TX, USA; 5Nuclear Medicine Section, Department of Diagnostic and Interventional Imaging, The University of Texas Health Science Center at Houston, Houston, TX, USA; 6Memorial Hermann Hospital, Texas Medical Center, Houston, TX, USA Purpose: To identify patterns of abnormalities using DaTscan single photon emission-","PeriodicalId":39053,"journal":{"name":"Reports in Medical Imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2147/RMI.S201890","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47778650","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}
php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). Reports in Medical Imaging 2019:12 1–8 Reports in Medical Imaging Dovepress
{"title":"Components and implementation of a picture archiving and communication system in a prototype application","authors":"H. H. Khaleel, R. Rahmat, D. M. Zamrin","doi":"10.2147/RMI.S179268","DOIUrl":"https://doi.org/10.2147/RMI.S179268","url":null,"abstract":"php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). Reports in Medical Imaging 2019:12 1–8 Reports in Medical Imaging Dovepress","PeriodicalId":39053,"journal":{"name":"Reports in Medical Imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2147/RMI.S179268","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44939356","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}
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.
目的：本研究的目的是评估一种用于牙根体积测量的半自动分割工具；这种方法是基于三维的，重点是在进行正畸治疗时跟踪牙根的体积。材料和方法：为了评估半自动分割工具，选择20颗完整牙齿（10颗上颌和10颗下颌牙齿）的锥形束计算机断层扫描（CBCT），将CBCT图像导入ImageJ软件，并使用两种方法（半自动分割和手动分割）测量根体积。这两个细分都由两名专家进行；手工分割是一种参考方法，被认为是黄金标准。使用Bland–Altman分析对数据进行分析，以比较方法之间的一致性，并使用组内相关系数（ICC）评估观察者间的可靠性。结果：Bland–Altman分析显示，半自动分割和手动分割的测量结果一致，平均偏差为–2.09 mm 3，95%的一致性极限为–11.38至7.21 mm 3。半自动分割方法和手动分割方法的ICC分别为0.999和0.999。结论：使用ImageJ软件和CBCT图像的体视学提供了准确可靠的半自动分割，从而提供了一种体积定量分析的方法来评估和跟踪正畸治疗时牙根的体积。需要进一步的临床研究来探索这种方法。
{"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}
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