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Chlipala E, Morken T, Thornton C, Chiriboga L. What will it take for histologists to be recognized under CLIA? J Histotechnol 2024; 47:93-96. [PMID: 39212518 DOI: 10.1080/01478885.2024.2391636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Affiliation(s)
| | - Tim Morken
- Retired, University of California San Francisco Medical Center
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Eccher A, Marletta S, Sbaraglia M, Guerriero A, Rossi M, Gambaro G, Scarpa A, Dei Tos AP. Digital pathology structure and deployment in Veneto: a proof-of-concept study. Virchows Arch 2024:10.1007/s00428-024-03823-7. [PMID: 38744690 DOI: 10.1007/s00428-024-03823-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 04/16/2024] [Accepted: 05/08/2024] [Indexed: 05/16/2024]
Abstract
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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Affiliation(s)
- Albino Eccher
- Department of Medical and Sciences for Children and Adults, University of Modena and Reggio Emilia, University Hospital of Modena, Modena, Italy
| | - Stefano Marletta
- Department of Diagnostic and Public Health, Section of Pathology, University of Verona, P.Leee L.A. Scuro N. 10, 37134, Verona, Italy.
- Division of Pathology, Humanitas Istituto Clinico Catanese, Catania, Italy.
| | - Marta Sbaraglia
- Surgical Pathology and Cytopathology Unit, Department of Medicine-DIMED, University of Padua School of Medicine, Padua, Italy
| | - Angela Guerriero
- Surgical Pathology and Cytopathology Unit, Department of Medicine-DIMED, University of Padua School of Medicine, Padua, Italy
| | - Mattia Rossi
- Division of Nephrology, Department of Medicine, University of Verona, Verona, Italy
| | - Giovanni Gambaro
- Division of Nephrology, Department of Medicine, University of Verona, Verona, Italy
| | - Aldo Scarpa
- Department of Diagnostic and Public Health, Section of Pathology, University of Verona, P.Leee L.A. Scuro N. 10, 37134, Verona, Italy
| | - Angelo Paolo Dei Tos
- Surgical Pathology and Cytopathology Unit, Department of Medicine-DIMED, University of Padua School of Medicine, Padua, Italy
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Lu Z, Morita M, Yeager TS, Lyu Y, Wang SY, Wang Z, Fan G. Validation of Artificial Intelligence (AI)-Assisted Flow Cytometry Analysis for Immunological Disorders. Diagnostics (Basel) 2024; 14:420. [PMID: 38396459 PMCID: PMC10888253 DOI: 10.3390/diagnostics14040420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 02/07/2024] [Accepted: 02/13/2024] [Indexed: 02/25/2024] Open
Abstract
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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Affiliation(s)
- Zhengchun Lu
- Department of Pathology and Laboratory Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA; (Z.L.); (M.M.); (T.S.Y.); (Y.L.); (S.Y.W.)
| | - Mayu Morita
- Department of Pathology and Laboratory Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA; (Z.L.); (M.M.); (T.S.Y.); (Y.L.); (S.Y.W.)
| | - Tyler S. Yeager
- Department of Pathology and Laboratory Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA; (Z.L.); (M.M.); (T.S.Y.); (Y.L.); (S.Y.W.)
| | - Yunpeng Lyu
- Department of Pathology and Laboratory Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA; (Z.L.); (M.M.); (T.S.Y.); (Y.L.); (S.Y.W.)
| | - Sophia Y. Wang
- Department of Pathology and Laboratory Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA; (Z.L.); (M.M.); (T.S.Y.); (Y.L.); (S.Y.W.)
| | | | - Guang Fan
- Department of Pathology and Laboratory Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA; (Z.L.); (M.M.); (T.S.Y.); (Y.L.); (S.Y.W.)
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Cazzaniga G, Eccher A, Munari E, Marletta S, Bonoldi E, Della Mea V, Cadei M, Sbaraglia M, Guerriero A, Dei Tos AP, Pagni F, L’Imperio V. Natural Language Processing to extract SNOMED-CT codes from pathological reports. Pathologica 2023; 115:318-324. [PMID: 38180139 PMCID: PMC10767798 DOI: 10.32074/1591-951x-952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 11/17/2023] [Indexed: 01/06/2024] Open
Abstract
Objective The use of standardized structured reports (SSR) and suitable terminologies like SNOMED-CT can enhance data retrieval and analysis, fostering large-scale studies and collaboration. However, the still large prevalence of narrative reports in our laboratories warrants alternative and automated labeling approaches. In this project, natural language processing (NLP) methods were used to associate SNOMED-CT codes to structured and unstructured reports from an Italian Digital Pathology Department. Methods Two NLP-based automatic coding systems (support vector machine, SVM, and long-short term memory, LSTM) were trained and applied to a series of narrative reports. Results The 1163 cases were tested with both algorithms, showing good performances in terms of accuracy, precision, recall, and F1 score, with SVM showing slightly better performances as compared to LSTM (0.84, 0.87, 0.83, 0.82 vs 0.83, 0.85, 0.83, 0.82, respectively). The integration of an explainability allowed identification of terms and groups of words of importance, enabling fine-tuning, balancing semantic meaning and model performance. Conclusions AI tools allow the automatic SNOMED-CT labeling of the pathology archives, providing a retrospective fix to the large lack of organization of narrative reports.
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Affiliation(s)
- Giorgio Cazzaniga
- Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo dei Tintori, University of Milano-Bicocca, Italy
| | - Albino Eccher
- Section of Pathology, Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, University Hospital of Modena, Modena, Italy
| | - Enrico Munari
- Department of Diagnostic and Public Health, Section of Pathology, University of Verona, Verona, Italy
| | - Stefano Marletta
- Department of Diagnostic and Public Health, Section of Pathology, University of Verona, Verona, Italy
| | - Emanuela Bonoldi
- Unit of Surgical Pathology and Cytogenetics, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Vincenzo Della Mea
- Department of Mathematics, Computer Science and Physics, University of Udine, Udine, Italy
| | - Moris Cadei
- Pathology Unit, ASST Spedali Civili di Brescia, Brescia, Italy
| | - Marta Sbaraglia
- Surgical Pathology and Cytopathology Unit, Department of Medicine-DIMED, University of Padua School of Medicine, Padua, Italy
| | - Angela Guerriero
- Surgical Pathology and Cytopathology Unit, Department of Medicine-DIMED, University of Padua School of Medicine, Padua, Italy
| | - Angelo Paolo Dei Tos
- Surgical Pathology and Cytopathology Unit, Department of Medicine-DIMED, University of Padua School of Medicine, Padua, Italy
| | - Fabio Pagni
- Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo dei Tintori, University of Milano-Bicocca, Italy
| | - Vincenzo L’Imperio
- Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo dei Tintori, University of Milano-Bicocca, Italy
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