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Automation in the Pathology Laboratory
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Automation in the Pathology Laboratory

A special issue of Journal of Molecular Pathology (ISSN 2673-5261).

Deadline for manuscript submissions: 20 July 2025 | Viewed by 1390

Special Issue Editors

Prof. Dr. Albino Eccher

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Guest Editor
Department of Pathology, University Hospital Policlinico di Modena, University of Modena and Reggio Emilia, Via del Pozzo 71, 41125 Modena, Italy
Interests: immunohistochemistry; transplantation pathology; digital pathology; cytopathology; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Stefano Marletta

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Guest Editor
Division of Pathology, Humanitas Istituto Clinico Catanese, 95045 Catania, Italy
Interests: digital pathology; artificial intelligence; automation in pathology; immunohistochemistry; uropathology; breast pathology
Dr. Viscardo Paolo Fabbri

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Guest Editor
Department of Laboratory Medicine and Anatomical Pathology, Pathology Unit, AOU Policlinico di Modena, 41125 Modena, MO, Italy
Interests: digital pathology; automation in pathology; immunohistochemistry; neuropathology; head and neck pathology; breast pathology

Special Issue Information

Dear Colleagues,

Currently, pathology laboratories are dealing with a constant increase in their workflow, which is estimated to grow by about 5% annually. Along with the current shortage of dedicated professional figures, such as pathologists and laboratory technicians, and the overwhelming amount of meticulous data required in the modern era of personalized medicine, the likelihood of human-driven errors continues to threaten physicians’ daily routines. Standardization in the pre-analytical, analytical, and post-analytical phases is a major concern in pathology, with the aim of homogenizing and improving tissue management to enhance the reproducibility of the diagnostic process. In this context, automation, defined as the employment of devices intended to supplement or replace human efforts in a process, is pivotal. Owing to its intrinsic ability to integrate with novel technologies, including digital pathology and artificial intelligence, automation in pathology has a remarkable potential to enhance sample management, lessen human-based mistakes, and increase timesaving and the productivity of operators. In this Special Issue, we will gather the available and yet-to-come state-of-the-art automatic solutions that can revolutionize all the critical processes of a pathology laboratory, ranging from sampling collection to traceability, digitalization, hardware, software, and specimen storage (block and glass/virtual slides), among others. By providing the readers with a cutting-edge scenario of the most disruptive but still customizable novelties, this Special Issue seeks to address some of the most compelling current issues of pathology laboratories, ultimately reducing diagnostic turn-around times and improving the clinical management of patients.

Prof. Dr. Albino Eccher
Dr. Stefano Marletta
Dr. Viscardo Paolo Fabbri
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. Journal of Molecular Pathology is an international peer-reviewed open access quarterly 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 1000 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

  • automation in pathology
  • traceability
  • laboratory workflow
  • standardization
  • digital pathology
  • pathology archive

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Published Papers (2 papers)

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14 pages, 13402 KiB  
Article
Kolmogorov–Arnold Networks for Automated Diagnosis of Urinary Tract Infections
by Anurag Dutta, A. Ramamoorthy, M. Gayathri Lakshmi and Pijush Kanti Kumar
J. Mol. Pathol. 2025, 6(1), 6; https://doi.org/10.3390/jmp6010006 - 5 Mar 2025
Viewed by 694
Abstract
Medical diagnostics is an important step in the identification and detection of any disease. Generally, diagnosis requires expert supervision, but in recent times, the evolving emergence of machine intelligence and its widespread applications has necessitated the integration of machine intelligence with pathological expert [...] Read more.
Medical diagnostics is an important step in the identification and detection of any disease. Generally, diagnosis requires expert supervision, but in recent times, the evolving emergence of machine intelligence and its widespread applications has necessitated the integration of machine intelligence with pathological expert supervision. This research aims to mitigate the diagnostics of urinary tract infections (UTIs) by visual recognition of Colony-Forming Units (CFUs) in urine culture. Recognizing the patterns specific to positive, negative, or uncertain UTI suspicion has been complemented with several neural networks inheriting the Multi-Layered Perceptron (MLP) architecture, like Vision Transformer, Class-Attention in Vision Transformers, etc., to name a few. In contrast to the fixed model edge weights of MLPs, the novel Kolmogorov–Arnold Network (KAN) architecture considers a set of trainable activation functions on the edges, therefore enabling better extraction of features. Inheriting the novel KAN architecture, this research proposes a set of three deep learning models, namely, K2AN, KAN-C-Norm, and KAN-C-MLP. These models, experimented on an open-source pathological dataset, outperforms the state-of-the-art deep learning models (particularly those inheriting the MLP architecture) by nearly 7.8361%. By rapid UTI detection, the proposed methodology reduces diagnostic delays, minimizes human error, and streamlines laboratory workflows. Further, preliminary results can complement (expert-supervised) molecular testing by enabling them to focus only on clinically important cases, reducing stress on traditional approaches. Full article
(This article belongs to the Special Issue Automation in the Pathology Laboratory)
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Review

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15 pages, 615 KiB  
Review
Current Topics on the Integration of Artificial Intelligence in the Histopathological and Molecular Diagnosis of Uveal Melanoma
by Serena Salzano, Giuseppe Broggi, Andrea Russo, Teresio Avitabile, Antonio Longo, Rosario Caltabiano and Manuel Mazzucchelli
J. Mol. Pathol. 2025, 6(2), 7; https://doi.org/10.3390/jmp6020007 - 17 Apr 2025
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Abstract
Background: This review examines the expanding influence of artificial intelligence (AI) in the detection and management of uveal melanoma (UM). Methods: This work delves into the application of AI technologies such as machine learning (ML), deep learning (DL), and convolutional neural networks (CNNs) [...] Read more.
Background: This review examines the expanding influence of artificial intelligence (AI) in the detection and management of uveal melanoma (UM). Methods: This work delves into the application of AI technologies such as machine learning (ML), deep learning (DL), and convolutional neural networks (CNNs) in various diagnostic procedures, molecular profiling, and predictive analysis. Results: The discussion underscores AI’s potential to enhance diagnostic precision and efficiency. Particular focus is placed on its role in histopathological assessments of UM, where algorithms facilitate the analysis of whole-slide images (WSIs). AI contributes to more accurate tumor classification, assists in planning treatments, and improves the prediction of the prognostic indicators and molecular characteristics of the tumor. Conclusions: Despite these promising developments, this review acknowledges existing hurdles to AI implementation, including issues with data standardization and the interpretability of AI models. It emphasizes the need for further research to fully integrate AI into clinical workflows, ultimately aiming to improve patient care and outcomes. Full article
(This article belongs to the Special Issue Automation in the Pathology Laboratory)
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