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Novel Strategies for Diagnosis and Treatment of Autoimmune Diseases
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Novel Strategies for Diagnosis and Treatment of Autoimmune Diseases

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Immunology".

Deadline for manuscript submissions: 25 September 2024 | Viewed by 1093

Special Issue Editor

Dr. Kassem Sharif

E-Mail Website
Guest Editor
Department of Gastroenterology, Chaim Sheba Medical Center, Affiliated to Tel Aviv University, Tel Aviv, Israel
Interests: gastroenterology; inflammatory bowel disease; celiac disease
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Autoimmune diseases are a group of complex disorders where the immune system mistakenly attacks healthy cells. Diagnosing and treating these conditions has long posed significant challenges. However, recent advances in medical research and technology are ushering in an era of novel strategies that promise to revolutionize patient care in this field. This Special Issue, titled "Novel Strategies for Diagnosis and Treatment of Autoimmune Diseases," is dedicated to exploring these groundbreaking developments. We delve into innovative diagnostic techniques, drawing on molecular biology, genetics, and bioinformatics that allow for early, accurate detection. This Special Issue also explores emerging therapeutic approaches, emphasizing precision medicine and personalized treatments tailored to individual genetic and molecular profiles. Finally, we examine new immunomodulatory treatments and advancements in biomarker discovery. Our goal is to foster dialogue, inspire further research, and inform clinical practice, moving us closer to effectively managing autoimmune diseases.

Dr. Kassem Sharif
Guest Editor

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. Journal of Clinical Medicine 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

  • autoimmunity
  • diagnosis
  • therapy
  • immunomodulation
  • biomarkers
  • genetics
  • bioinformatics
  • personalized
  • precision
  • innovation

Published Papers (1 paper)

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13 pages, 1096 KiB  
Systematic Review
Deep Learning in Coeliac Disease: A Systematic Review on Novel Diagnostic Approaches to Disease Diagnosis
by Kassem Sharif, Paula David, Mahmud Omar, Yousra Sharif, Yonatan Shneor Patt, Eyal Klang and Adi Lahat
J. Clin. Med. 2023, 12(23), 7386; https://doi.org/10.3390/jcm12237386 - 29 Nov 2023
Viewed by 852
Abstract
Background: Coeliac disease affects approximately 1% of the global population with the diagnosis often relying on invasive and time-demanding methods. Deep learning, a powerful tool in medical science, shows potential for non-invasive, accurate coeliac disease diagnosis, though challenges remain. Objective: This systematic review [...] Read more.
Background: Coeliac disease affects approximately 1% of the global population with the diagnosis often relying on invasive and time-demanding methods. Deep learning, a powerful tool in medical science, shows potential for non-invasive, accurate coeliac disease diagnosis, though challenges remain. Objective: This systematic review aimed to evaluate the current state of deep-learning applications in coeliac disease diagnosis and identify potential areas for future research that could enhance diagnostic accuracy, sensitivity, and specificity. Methods: A systematic review was conducted using the following databases: PubMed, Embase, Web of Science, and Scopus. PRISMA guidelines were applied. Two independent reviewers identified research articles using deep learning for coeliac disease diagnosis and severity assessment. Only original research articles with performance metrics data were included. The quality of the diagnostic accuracy studies was assessed using the QUADAS-2 tool, categorizing studies based on risk of bias and concerns about applicability. Due to heterogeneity, a narrative synthesis was conducted to describe the applications and efficacy of the deep-learning techniques (DLT) in coeliac disease diagnosis. Results: The initial search across four databases yielded 417 studies with 195 being removed due to duplicity. Finally, eight studies were found to be suitable for inclusion after rigorous evaluation. They were all published between 2017 and 2023 and focused on using DLT for coeliac disease diagnosis or assessing disease severity. Different deep-learning architectures were applied. Accuracy levels ranged from 84% to 95.94% with the GoogLeNet model achieving 100% sensitivity and specificity for video capsule endoscopy images. Conclusions: DLT hold substantial potential in coeliac disease diagnosis. They offer improved accuracy and the prospect of mitigating clinician bias. However, key challenges persist, notably the requirement for more extensive and diverse datasets, especially to detect milder forms of coeliac disease. These methods are in their nascent stages, underscoring the need of integrating multiple data sources to achieve comprehensive coeliac disease diagnosis. Full article
(This article belongs to the Special Issue Novel Strategies for Diagnosis and Treatment of Autoimmune Diseases)
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