Publications by authors named "Daniel Duma"

The aim of this study was to evaluate the feasibility of a noninvasive and low-operator-dependent imaging method for carotid-artery-stenosis diagnosis. A previously developed prototype for 3D ultrasound scans based on a standard ultrasound machine and a pose reading sensor was used for this study. Working in a 3D space and processing data using automatic segmentation lowers operator dependency.

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Article Synopsis
  • - The systematic review examines the use of natural language processing (NLP) in analyzing radiology reports, emphasizing the need for transparent methodologies to enable comparisons and reproducibility across studies.
  • - It analyzed 164 studies published between January 2015 and October 2019, finding that most focused on disease classification (28%) and diagnostic surveillance (27.4%), primarily using English reports from various imaging modalities, with oncology being the most common disease area.
  • - The review highlights issues such as inadequate reporting on essential factors like dataset preparation and validation, with only a small percentage providing details on external validation and data/code availability, suggesting a need for improved reporting standards in NLP research.
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Background: Patient-based analysis of social media is a growing research field with the aim of delivering precision medicine but it requires accurate classification of posts relating to patients' experiences. We motivate the need for this type of classification as a pre-processing step for further analysis of social media data in the context of related work in this area. In this paper we present experiments for a three-way document classification by patient voice, professional voice or other.

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Article Synopsis
  • NLP is crucial for extracting structured information from radiology reports, but comprehensive reviews on its application are lacking.
  • A systematic literature search identified 164 publications, revealing a significant increase in usage since 2015, with a shift towards deep learning despite ongoing challenges in clinical adoption and data availability.
  • Enhancing the reproducibility and explainability of NLP models is essential for their integration into clinical practice, and there is a need for improved sharing of data and methodologies to facilitate comparison across studies.
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The classic ultrasonographic differentiation between benign and malignant adnexal masses encounters several limitations. Ultrasonography-based texture analysis (USTA) offers a new perspective, but its role has been incompletely evaluated. This study aimed to further investigate USTA's capacity in differentiating benign from malignant adnexal tumors, as well as comparing the workflow and the results with previously-published research.

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Background And Aims: While there is an increasing emphasis on the value of interdisciplinarity in scholarship in the medical humanities, it is unknown to what extent there is joint working between historians and clinicians in medical history. We aimed to quantify evidence of joint working in authorship of medical history papers.

Methods: Observational survey of authorship.

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How does scientific research affect the world around us? Being able to answer this question is of great importance in order to appropriately channel efforts and resources in science. The impact by scientists in academia is currently measured by citation based metrics such as h-index, i-index and citation counts. These academic metrics aim to represent the dissemination of knowledge among scientists rather than the impact of the research on the wider world.

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