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Diagnostics
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Latest News in Digital Pathology
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Latest News in Digital Pathology

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Pathology and Molecular Diagnostics".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 2981

Special Issue Editor

Dr. Tahir Mahmood

E-Mail Website
Guest Editor
Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea
Interests: digital pathology and AI integration; cancer diagnosis and prognosis; histopathology and tissue analysis

Special Issue Information

Dear Colleagues,

The field of digital pathology is rapidly evolving with the introduction of advanced technologies and novel imaging modalities. These innovations are significantly enhancing the accuracy and efficiency of cancer diagnosis and prognosis. This Special Issue, "Recent News in Digital Pathology," aims to explore the latest developments in this field, including the application of generative AI, advancements in cancer diagnosis and prognosis, and novel methods for cancer grading. By bringing together cutting-edge research and expert insights, this issue will provide a comprehensive overview of the current trends and future directions in digital pathology.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  1. Digital pathology;
  2. Generative AI;
  3. Cancer diagnosis;
  4. Cancer prognosis;
  5. Imaging modalities;
  6. Cancer grading;
  7. Artificial intelligence in pathology;
  8. Histopathology;
  9. Image analysis;
  10. Medical imaging.

Dr. Tahir Mahmood
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. 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

  • digital pathology
  • generative AI
  • cancer diagnosis
  • cancer prognosis
  • imaging modalities
  • cancer grading
  • artificial intelligence in pathology
  • histopathology
  • image analysis
  • medical imaging

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

11 pages, 2813 KiB  
Article
Perspectives on Reducing Barriers to the Adoption of Digital and Computational Pathology Technology by Clinical Labs
by Jeffrey L. Bessen, Melissa Alexander, Olivia Foroughi, Roderick Brathwaite, Emre Baser, Liam C. Lee, Omar Perez and Gary Gustavsen
Diagnostics 2025, 15(7), 794; https://doi.org/10.3390/diagnostics15070794 - 21 Mar 2025
Viewed by 1096
Abstract
Background/Objectives: Digital and computational pathology (DP/CP) tools have the potential to improve the efficiency and accuracy of the anatomic pathology workflow; however, current adoption among US hospital and reference labs remains low. Methods: To better understand the current utilization of DP/CP technology and [...] Read more.
Background/Objectives: Digital and computational pathology (DP/CP) tools have the potential to improve the efficiency and accuracy of the anatomic pathology workflow; however, current adoption among US hospital and reference labs remains low. Methods: To better understand the current utilization of DP/CP technology and barriers to widespread adoption, we conducted a survey among 63 anatomic pathologists and lab directors within the US health system. Results: The survey results indicated that current use cases for DP/CP involve streamlining traditional manual pathology and that labs would have substantial difficulty providing AI-guided image analysis if it were required by physicians today. Among potential catalysts for the broader adoption of DP/CP, pathologists identified clinical guidelines as a key resource for anatomic pathology, whose endorsement of DP/CP would be highly impactful for reducing current barriers. Conclusions: Expanded access to DP/CP may ultimately benefit all major stakeholders—patients, physicians, clinical laboratory professionals, care settings, and payers—and will therefore require collaboration across these groups. Full article
(This article belongs to the Special Issue Latest News in Digital Pathology)
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16 pages, 2500 KiB  
Article
Computer-Aided Diagnosis in Spontaneous Abortion: A Histopathology Dataset and Benchmark for Products of Conception
by Tahir Mahmood, Zeeshan Ullah, Atif Latif, Binish Arif Sultan, Muhammad Zubair, Zahid Ullah, AbuZar Ansari, Talat Zehra, Shahzad Ahmed and Naqqash Dilshad
Diagnostics 2024, 14(24), 2877; https://doi.org/10.3390/diagnostics14242877 - 21 Dec 2024
Viewed by 1035
Abstract
Spontaneous abortion, commonly known as miscarriage, is a significant concern during early pregnancy. Histopathological examination of tissue samples is a widely used method to diagnose and classify tissue phenotypes found in products of conception (POC) after spontaneous abortion. Background: Histopathological examination is subjective [...] Read more.
Spontaneous abortion, commonly known as miscarriage, is a significant concern during early pregnancy. Histopathological examination of tissue samples is a widely used method to diagnose and classify tissue phenotypes found in products of conception (POC) after spontaneous abortion. Background: Histopathological examination is subjective and dependent on the skill and experience of the examiner. In recent years, artificial intelligence (AI)-based techniques have emerged as a promising tool in medical imaging, offering the potential to revolutionize tissue phenotyping and improve the accuracy and reliability of the histopathological examination process. The goal of this study was to investigate the use of AI techniques for the detection of various tissue phenotypes in POC after spontaneous abortion and evaluate the accuracy and reliability of these techniques compared to traditional manual methods. Methods: We present a novel publicly available dataset named HistoPoC, which is believed to be the first of its kind, focusing on spontaneous abortion (miscarriage) in early pregnancy. A diverse dataset of 5666 annotated images was prepared from previously diagnosed cases of POC from Atia General Hospital, Karachi, Pakistan, for this purpose. The digital images were prepared at 10× through a camera-connected microscope by a consultant histopathologist. Results: The dataset’s effectiveness was validated using several deep learning-based models, demonstrating its applicability and supporting its use in intelligent diagnostic systems. Conclusions: The insights gained from this study could illuminate the causes of spontaneous abortion and guide the development of novel treatments. Additionally, this study could contribute to advancements in the field of tissue phenotyping and the wider application of deep learning techniques in medical diagnostics and treatment. Full article
(This article belongs to the Special Issue Latest News in Digital Pathology)
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