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Diagnostics
Special Issues
Advanced Intelligent Data Analysis for Medical Diagnosis
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Advanced Intelligent Data Analysis for Medical Diagnosis

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 903

Special Issue Editors

Prof. Dr. Shang-Ming Zhou

E-Mail Website
Guest Editor
Centre for Health Technology, University of Plymouth, Devon PL4 8AA, UK
Interests: e-health; AI for healthcare and medicine; health data science; health informatics
Special Issues, Collections and Topics in MDPI journals
Dr. Honghan Wu

E-Mail Website
Guest Editor
Institute of Health Informatics, University College London, London NW1 2DA, UK
Interests: using text technologies and knowledge graph techniques to analyse electronic health records
Special Issues, Collections and Topics in MDPI journals
Dr. Haider Raza

E-Mail Website
Guest Editor
School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK
Interests: big data and analytics; brain–computer interface; deep learning; transfer learning; non-stationary learning and domain adaptation; artificial intelligence (AI) and eXplainable AI (XAI); EEG and MEG signal processing; AI in decision making for healthcare
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Making an accurate diagnosis is a major challenge for global healthcare systems since it requires complicated and coordinated information collection and clinical reasoning operations. On the other hand, developments in information technology, particularly the pervasiveness of mobile technologies adopted in the biomedical and health sciences, have generated mountains of data about health and wellbeing from a variety of sources, including electronic health records in primary care and secondary care, genome-wide studies, demographics, doctors' notes, clinical images, laboratory results, genetic tests, wearable sensors, etc. Improving diagnostic accuracy needs the co-design and co-creative activities by data scientists and healthcare professionals to develop efficient ways of analyzing such a scale of data from multiple sources.

The purpose of this Special Issue is to investigate how advanced intelligent data analysis techniques, such as machine learning/deep learning, responsible AI (including ethical, trustworthy, interpretable/explainable, transparent AI techniques), natural language processing, data mining, statistical learning, etc., hold their promises to more efficiently analyze data in order to extract useful information and improve clinical decision making and medical diagnosis.

Prof. Dr. Shang-Ming Zhou
Dr. Honghan Wu
Dr. Haider Raza
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • medical diagnosis
  • healthcare informatics
  • electronic health records
  • responsible AI
  • ethical AI
  • trustworthy AI
  • interpretable/explainable/transparent AI (XAI)
  • machine learning
  • deep learning
  • big data analytics
  • predictive modeling in healthcare
  • omics data
  • imaging data
  • sensor data
  • clinical notes
  • natural language processing
  • data mining
  • statistical learning

Published Papers (1 paper)

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Research

12 pages, 1405 KiB  
Article
Radiomics-Based Machine Learning Model for Diagnosis of Acute Pancreatitis Using Computed Tomography
by Stefanie Bette, Luca Canalini, Laura-Marie Feitelson, Piotr Woźnicki, Franka Risch, Adrian Huber, Josua A. Decker, Kartikay Tehlan, Judith Becker, Claudia Wollny, Christian Scheurig-Münkler, Thomas Wendler, Florian Schwarz and Thomas Kroencke
Diagnostics 2024, 14(7), 718; https://doi.org/10.3390/diagnostics14070718 - 28 Mar 2024
Viewed by 644
Abstract
In the early diagnostic workup of acute pancreatitis (AP), the role of contrast-enhanced CT is to establish the diagnosis in uncertain cases, assess severity, and detect potential complications like necrosis, fluid collections, bleeding or portal vein thrombosis. The value of texture analysis/radiomics of [...] Read more.
In the early diagnostic workup of acute pancreatitis (AP), the role of contrast-enhanced CT is to establish the diagnosis in uncertain cases, assess severity, and detect potential complications like necrosis, fluid collections, bleeding or portal vein thrombosis. The value of texture analysis/radiomics of medical images has rapidly increased during the past decade, and the main focus has been on oncological imaging and tumor classification. Previous studies assessed the value of radiomics for differentiating between malignancies and inflammatory diseases of the pancreas as well as for prediction of AP severity. The aim of our study was to evaluate an automatic machine learning model for AP detection using radiomics analysis. Patients with abdominal pain and contrast-enhanced CT of the abdomen in an emergency setting were retrospectively included in this single-center study. The pancreas was automatically segmented using TotalSegmentator and radiomics features were extracted using PyRadiomics. We performed unsupervised hierarchical clustering and applied the random-forest based Boruta model to select the most important radiomics features. Important features and lipase levels were included in a logistic regression model with AP as the dependent variable. The model was established in a training cohort using fivefold cross-validation and applied to the test cohort (80/20 split). From a total of 1012 patients, 137 patients with AP and 138 patients without AP were included in the final study cohort. Feature selection confirmed 28 important features (mainly shape and first-order features) for the differentiation between AP and controls. The logistic regression model showed excellent diagnostic accuracy of radiomics features for the detection of AP, with an area under the curve (AUC) of 0.932. Using lipase levels only, an AUC of 0.946 was observed. Using both radiomics features and lipase levels, we showed an excellent AUC of 0.933 for the detection of AP. Automated segmentation of the pancreas and consecutive radiomics analysis almost achieved the high diagnostic accuracy of lipase levels, a well-established predictor of AP, and might be considered an additional diagnostic tool in unclear cases. This study provides scientific evidence that automated image analysis of the pancreas achieves comparable diagnostic accuracy to lipase levels and might therefore be used in the future in the rapidly growing era of AI-based image analysis. Full article
(This article belongs to the Special Issue Advanced Intelligent Data Analysis for Medical Diagnosis)
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