Publications by authors named "Ivan Nesic"

Aim: Two-dimensional speckle tracking echocardiography (2D-STE) and three-dimensional echocardiography (3DE) may overcome many limitations of the conventional 2D echocardiography (2DE) in assessing right ventricular (RV) function. We sought to determine whether characteristics of the right atrium and right ventricle as measured by 2D-STE and 3DE are associated with cardiac mortality in patients with ischemic heart failure, over a 6-year follow-up.

Materials And Methods: The inclusion criteria were ischemic cardiomyopathy with left ventricular ejection fraction of <40% diagnosed using standard 2DE, 2D-STE, and 3DE examination.

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Objectives: To investigate how a transition from free text to structured reporting affects reporting language with regard to standardization and distinguishability.

Methods: A total of 747,393 radiology reports dictated between January 2011 and June 2020 were retrospectively analyzed. The body and cardiothoracic imaging divisions introduced a reporting concept using standardized language and structured reporting templates in January 2016.

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: There is a lack of data about the survival of patients after the implantation of sutureless relative to stented bioprostheses in middle-income settings. The objective of this study was to compare the survival of people with isolated severe aortic stenosis after the implantation of sutureless and stented bioprostheses in a tertiary referral center in Serbia. : This retrospective cohort study included all people treated for isolated severe aortic stenosis with sutureless and stented bioprostheses from 1 January 2018 to 1 July 2021 at the Institute for Cardiovascular Diseases "Dedinje".

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Article Synopsis
  • - The study proposes a natural language processing (NLP) algorithm to automatically extract temporal referrals from unstructured radiology reports, enhancing readability and patient management by connecting current and past exams.
  • - A convolutional neural network was developed and tested on 149 reports, achieving high precision (0.93), recall (0.95), and F1-score (0.94) for extracting the dates of referrals from a decade's worth of reports.
  • - Out of 1.68 million analyzed reports, 53.3% included temporal references, while 21% explicitly stated they were unavailable and 25.7% omitted them. The study also introduced a graphical representation to visualize these connections among imaging reports.
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Pancreatic cystic lesions (PCL) are a frequent and underreported incidental finding on CT scans and can transform into neoplasms with devastating consequences. We developed and evaluated an algorithm based on a two-step nnU-Net architecture for automated detection of PCL on CTs. A total of 543 cysts on 221 abdominal CTs were manually segmented in 3D by a radiology resident in consensus with a board-certified radiologist specialized in abdominal radiology.

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We presented a case of a 56-year-old man with giant pulmonary artery aneurysm caused by a misdiagnosed patent ductus arteriosus, severe multivalvular disease and active aortic valve endocarditis successfully treated by surgery. The correct diagnosis was missed despite preoperative diagnostics because the small patent ductus arteriosus was located at the distal part of common pulmonary trunk and a huge regurgitant signal overlapped its Doppler signal. Thorough evaluation of every patient, regardless of age, is necessary to recognize and treat this congenital anomaly.

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Objectives: To investigate the most common errors in residents' preliminary reports, if structured reporting impacts error types and frequencies, and to identify possible implications for resident education and patient safety.

Material And Methods: Changes in report content were tracked by a report comparison tool on a word level and extracted for 78,625 radiology reports dictated from September 2017 to December 2018 in our department. Following data aggregation according to word stems and stratification by subspecialty (e.

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Purpose: During the emerging COVID-19 pandemic, radiology departments faced a substantial increase in chest CT admissions coupled with the novel demand for quantification of pulmonary opacities. This article describes how our clinic implemented an automated software solution for this purpose into an established software platform in 10 days. The underlying hypothesis was that modern academic centers in radiology are capable of developing and implementing such tools by their own efforts and fast enough to meet the rapidly increasing clinical needs in the wake of a pandemic.

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Introduction: Technical improvement and new operative strategies significantly influence survival and outcomes after the treatment of acute aortic dissection type A (AADA). However, postoperative complications and particularly neurological dysfunctions (ND) are still very common.

Aim: To identify preoperative and intraoperative factors as well as immediate postoperative conditions with an influence on the occurrence of neurological complications of surgical treatment of AADA and accordingly take action to reduce them.

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Article Synopsis
  • The study aimed to develop and assess a self-training NLP method for classifying unstructured radiology reports, demonstrated through CT pulmonary angiogram (CTPA) reports in German.
  • Researchers extracted impressions from 4,397 CTPA reports to train three different NLP models (CNN, SVM, RF), using a subset of labeled data for training while reserving some for performance evaluation.
  • The models achieved high classification accuracy (97%-99%) and required only a small dataset for effective training, showcasing the potential for automated analysis of non-English radiology reports.
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The use of artificial intelligence (AI) is a powerful tool for image analysis that is increasingly being evaluated by radiology professionals. However, due to the fact that these methods have been developed for the analysis of nonmedical image data and data structure in radiology departments is not "AI ready", implementing AI in radiology is not straightforward. The purpose of this review is to guide the reader through the pipeline of an AI project for automated image analysis in radiology and thereby encourage its implementation in radiology departments.

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