Cutting-edge technology and automation in the pathology laboratory

Munari, Enrico; Scarpa, Aldo; Cima, Luca; Pozzi, Matteo; Pagni, Fabio; Vasuri, Francesco; Marletta, Stefano; Dei Tos, Angelo Paolo; Eccher, Albino

doi:10.1007/s00428-023-03637-z

Cited by 4 publications

(1 citation statement)

References 50 publications

(48 reference statements)

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“…In these instances, the initial cases could have been finished about an hour earlier. The standardization and automation of pipeline workflow in data acquisition, analysis, and reporting are the key elements in increasing the accuracy and effectiveness of diagnostic testing, ultimately striving to provide the best patient care [46].…”

Section: Discussionmentioning

confidence: 99%

Validation of Artificial Intelligence (AI)-Assisted Flow Cytometry Analysis for Immunological Disorders

Lu,

Morita,

Yeager

et al. 2024

Diagnostics

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Flow cytometry is a vital diagnostic tool for hematologic and immunologic disorders, but manual analysis is prone to variation and time-consuming. Over the last decade, artificial intelligence (AI) has advanced significantly. In this study, we developed and validated an AI-assisted flow cytometry workflow using 379 clinical cases from 2021, employing a 3-tube, 10-color flow panel with 21 antibodies for primary immunodeficiency diseases and related immunological disorders. The AI software (DeepFlow™, version 2.1.1) is fully automated, reducing analysis time to under 5 min per case. It interacts with hematopatholoists for manual gating adjustments when necessary. Using proprietary multidimensional density–phenotype coupling algorithm, the AI model accurately classifies and enumerates T, B, and NK cells, along with important immune cell subsets, including CD4+ helper T cells, CD8+ cytotoxic T cells, CD3+/CD4−/CD8− double-negative T cells, and class-switched or non-switched B cells. Compared to manual analysis with hematopathologist-determined lymphocyte subset percentages as the gold standard, the AI model exhibited a strong correlation (r > 0.9) across lymphocyte subsets. This study highlights the accuracy and efficiency of AI-assisted flow cytometry in diagnosing immunological disorders in a clinical setting, providing a transformative approach within a concise timeframe.

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Section: Discussionmentioning

confidence: 99%

Validation of Artificial Intelligence (AI)-Assisted Flow Cytometry Analysis for Immunological Disorders

Lu,

Morita,

Yeager

et al. 2024

Diagnostics

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Digital pathology structure and deployment in Veneto: a proof-of-concept study

Eccher,

Marletta,

Sbaraglia

et al. 2024

Virchows Arch

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Nowadays pathology laboratories are worldwide facing a digital revolution, with an increasing number of institutions adopting digital pathology (DP) and whole slide imaging solutions. Despite indeed providing novel and helpful advantages, embracing a whole DP workflow is still challenging, especially for wide healthcare networks. The Azienda Zero of the Veneto Italian region has begun a process of a fully digital transformation of an integrated network of 12 hospitals producing nearly 3 million slides per year. In the present article, we describe the planning stages and the operative phases needed to support such a disruptive transition, along with the initial preliminary results emerging from the project. The ultimate goal of the DP program in the Veneto Italian region is to improve patients’ clinical care through a safe and standardized process, encompassing a total digital management of pathology samples, easy file sharing with experienced colleagues, and automatic support by artificial intelligence tools.

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Necropsy as an Important Diagnostic Step in Veterinary Pathology: The Past, Present, and Future Perspectives

Orakpoghenor,

Terfa

2024

RVSM

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Necropsy, or animal autopsy, is a cornerstone of veterinary pathology that serves as a critical tool for diagnosing the causes of death and understanding disease processes in animals. Historically, necropsy practices can be traced back to ancient civilizations, and the Renaissance period marked significant advancements with detailed anatomical studies. The 19th century further revolutionized veterinary pathology with systematic necropsy techniques and the emphasis on cellular pathology. In contemporary veterinary practice, necropsy remains a gold standard for diagnosing unexplained deaths and diseases. It plays a key role in public health by identifying zoonotic pathogens and preventing disease outbreaks. Necropsy is also crucial for wildlife conservation, helping to investigate mortality events and assess ecosystem health. In veterinary education, it provides essential hands-on experience for students, while in research, it offers specimens for studying disease mechanisms and developing new diagnostic methods. Future advancements in necropsy include the integration of digital imaging technologies such as computed tomography and magnetic resonance imaging, molecular and genetic techniques such as polymerase chain reaction and next-generation sequencing, and utilization of artificial intelligence. These innovations present potential toward transformation of the practice, thus making it more efficient and precise. Furthermore, ethical considerations and the standardization of necropsy protocols are essential to ensure the humane handling of animals remains and consistency in findings. In conclusion, as technology and scientific understanding evolve, necropsy will continue to be an invaluable asset in veterinary medicine, thus contributing significantly to animal health, public health, and educational and research advancements.

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Cutting-edge technology and automation in the pathology laboratory

Cited by 4 publications

References 50 publications

Validation of Artificial Intelligence (AI)-Assisted Flow Cytometry Analysis for Immunological Disorders

Validation of Artificial Intelligence (AI)-Assisted Flow Cytometry Analysis for Immunological Disorders

Digital pathology structure and deployment in Veneto: a proof-of-concept study

Necropsy as an Important Diagnostic Step in Veterinary Pathology: The Past, Present, and Future Perspectives

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