Rastreio de tecnologias digitais em saúde (DHTs) em Laboratórios de Anatomia Patológica
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Publicado
Jul 23, 2024
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Resumo
Objetivo: rastrear as tecnologias digitais em saúde (DHTs) em Laboratórios de Anatomia Patológica e descrever as que foram citadas entre 2016 e 2021. Métodos: neste estudo, foi realizada uma revisão integrativa de literatura para identificar artigos sobre softwares, scanners, aplicativos e sistemas de informações laboratoriais aplicados à laboratórios de patologia. A pesquisa abrangeu artigos publicados até julho de 2021. Resultados: foram identificados 71 estudos, sendo 26 analisados detalhadamente. Estes artigos apresentaram 78 DHTs, categorizados em cinco funcionalidades principais: Softwares de macroscopia, Digitalização de lâminas, Gerenciamento e Visualização de imagens, Análise de imagens e Sistema de Informação Laboratorial (LIS). Os benefícios das DHTs incluem maior precisão diagnóstica, compartilhamento remoto de dados e imagens, redução de tempo no diagnóstico, além de auxiliar na padronização e gestão de informações. Considerações finais: as tecnologias mencionadas neste estudo promovem avanços significativos na área da Anatomia Patológica, oferecendo melhorias na precisão dos exames, diagnóstico e tratamento.
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Como Citar
PereiraF. S., SilvaM. V. B., LimaS. C. R. de, MachadoJ. da R., & PereiraR. M. (2024). Rastreio de tecnologias digitais em saúde (DHTs) em Laboratórios de Anatomia Patológica. Revista Eletrônica Acervo Saúde, 24(7), e16084. https://doi.org/10.25248/reas.e16084.2024
Seção
Revisão Bibliográfica
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Referências
1. AEFFNER F, et al. Introduction to Digital Image Analysis in Whole-slide Imaging: A White Paper from the Digital Pathology Association. Journal of Pathology Informatics, 2019; 10 (1): 9.
2. ALVES MCFB, et al. Uso de scanner de lâminas histológicas no processo de aprendizado de patologia entre acadêmicos de medicina / Use of histological blade scanner in the pathology learning process among medicine students. Brazilian Journal of Health Review, 2021; 4 (5): 22098–22103.
3. ARAÚJO ALD, et al. Fully digital pathology laboratory routine and remote reporting of oral and maxillofacial diagnosis during the COVID-19 pandemic: a validation study. Virchows Archiv, 2021; 479 (3): 585-595.
4. BANKHEAD P, et al. Integrated tumor identification and automated scoring minimizes pathologist involvement and provides new insights to key biomarkers in breast cancer. Laboratory Investigation, 2017a; 98 (1): 15-26.
5. BANKHEAD P, et al. QuPath: Open source software for digital pathology image analysis. Scientific Reports, 2017b; 7 (1): 16878.
6. BOIS MC, et al. Three-Dimensional Surface Imaging and Printing in Anatomic Pathology. Journal of Pathology Informatics, 2021; 12 (1): 22.
7. CHEA V, et al. Optimized workflow for digitalized FISH analysis in pathology. Diagnostic Pathology, 2021; 16 (1): 42.
8. CORIOLANO-MARINUS MW DE L, et al. Comunicação nas práticas em saúde: revisão integrativa da literatura. Saúde e Sociedade, 2014; 23 (4): 1356–1369.
9. CUI M e ZHANG DY. Artificial intelligence and computational pathology. Laboratory Investigation, 2021; 101 (4): 412–422.
10. DUNDAR B, et al. Anatomic pathology quality assurance: Developing an LIS based tracking and documentation module for intradepartmental consultations. Journal of Pathology Informatics, 2022; 13: 100109.
11. EVANS AJ, et al. Implementation of Whole Slide Imaging for Clinical Purposes: Issues to Consider From the Perspective of Early Adopters. Archives of Pathology & Laboratory Medicine, 2017; 141 (7): 944–959.
12. FERREIRA I, et al. Digital pathology implementation in a private laboratory: The CEDAP experience. Journal of Pathology Informatics, 2023; 14: 100180.
13. FRAGGETTA F, et al. Routine digital pathology workflow: The Catania experience. Journal of Pathology Informatics, 2017; 8 (1): 51.
14. GHOSH A, et al. Telepathology at the Armed Forces Institute of Pathology: A Retrospective Review of Consultations From 1996 to 1997. Archives of Pathology & Laboratory Medicine, 2018; 142 (2): 248–252.
15. GUO H, et al. Digital pathology and anatomic pathology laboratory information system integration to support digital pathology sign-out. Journal of Pathology Informatics, 2016; 7 (1): 23.
16. GURGEL BCM DOS S, et al. Neoplasias orofaciais em crianças e adolescentes. Arq. odontol, 2020; 56: 1–9.
17. HANNA MG, et al. Validation of a digital pathology system including remote review during the COVID-19 pandemic. Modern Pathology, 2020; 33 (11): 2115–2127.
18. HANNA MG e ARDON O. Digital pathology systems enabling quality patient care. Genes, Chromosomes and Cancer, 2023; 62 (11): 685–697.
19. HANNA MG e PANTANOWITZ L. The role of informatics in patient‐centered care and personalized medicine. Cancer Cytopathology, 2017; 125 (S6): 494–501.
20. HERRMANN MD, et al. Implementing the DICOM Standard for Digital Pathology. Journal of Pathology Informatics, 2018; 9 (1): 37.
21. HOLZER TR, et al. Cross-Platform Comparison of Computer-assisted Image Analysis Quantification of In Situ mRNA Hybridization in Investigative Pathology. Applied Immunohistochemistry & Molecular Morphology, 2019; 27 (1): 15–26.
22. JAHN SW, et al. Digital Pathology: Advantages, Limitations and Emerging Perspectives. Journal of Clinical Medicine, 2020; 9 (11): 3697.
23. KOSOWICZ L, et al. Lessons for Vietnam on the Use of Digital Technologies to Support Patient-Centered Care in Low- and Middle-Income Countries in the Asia-Pacific Region: Scoping Review. Journal of Medical Internet Research, 2023; 25: e43224.
24. KULAK O, et al. Smart Glasses as a Surgical Pathology Grossing Tool. Archives of Pathology & Laboratory Medicine, 2021; 145 (4): 457–460.
25. KUMAR N, et al. Whole Slide Imaging (WSI) in Pathology: Current Perspectives and Future Directions. Journal of Digital Imaging, 2020; 33 (4): 1034–1040.
26. LAM H, et al. UniTwain: A Cost-Effective Solution for Lean Gross Imaging. Journal of Pathology Informatics, 2020; 11 (1): 31.
27. LINO-SILVA LS e XINAXTLE D L. Artificial intelligence technology applications in the pathologic diagnosis of the gastrointestinal tract. Future Oncology, 2020; 16 (34) 2845–2851.
28. LISCIA DS, et al. Whole-Slide Imaging Allows Pathologists to Work Remotely in Regions with Severe Logistical Constraints Due to Covid-19 Pandemic. Journal of Pathology Informatics, 2020; 11 (1): 20.
29. LOUGHREY MB, et al. Validation of the systematic scoring of immunohistochemically stained tumour tissue microarrays using QuPath digital image analysis. Histopathology, 2018; 73 (2): 327–338.
30. LUJAN G, et al. Dissecting the Business Case for Adoption and Implementation of Digital Pathology: A White Paper from the Digital Pathology Association. Journal of Pathology Informatics, 2021; 12 (1) 17.
31. MADKE B e GARDNER JM. Enhanced Worldwide Dermatology–Pathology Interaction via Facebook, Twitter, and Other Social Media Platforms. The American Journal of Dermatopathology, 2018; 40 (3): 168–172.
32. MARTEL AL, et al. An Image Analysis Resource for Cancer Research: PIIP—Pathology Image Informatics Platform for Visualization, Analysis, and Management. Cancer Research, 2017; 77 (21): e83–e86.
33. NALISNIK M, et al. Interactive phenotyping of large-scale histology imaging data with HistomicsML. Scientific Reports, 2017; 7 (1): 14588.
34. NAVANI S. Manual evaluation of tissue microarrays in a high‐throughput research project: The contribution of Indian surgical pathology to the Human Protein Atlas (HPA) project. PROTEOMICS, 2016; 16 (8): 1266–1270.
35. ORAH N e ROTIMI O. Telepathology in Low Resource African Settings. Frontiers in Public Health, 2019; 7.
36. PALLUA JD, et al. The future of pathology is digital. Pathology - Research and Practice, 2020; 216 (9): 153040.
37. PANTANOWITZ L, et al. Twenty Years of Digital Pathology: An Overview of the Road Travelled, What is on the Horizon, and the Emergence of Vendor-Neutral Archives. Journal of Pathology Informatics, 2018; 9 (1): 40.
38. PELL R, et al. The use of digital pathology and image analysis in clinical trials. The Journal of Pathology: Clinical Research, 2019; 5 (2): 81–90.
39. PEREIRA FS, et al. Desenvolvimento e validação de aplicativo para macroscopia do aparelho genital feminino. Revista Eletrônica Acervo Saúde, 2023; 23 (9): e13164.
40. PEREZ NA, et al. Ex vivo 3D scanning and specimen mapping in anatomic pathology. Journal of Pathology Informatics, 2023; 14: 100186.
41. PODSZUN MC, et al. 4-HNE Immunohistochemistry and Image Analysis for Detection of Lipid Peroxidation in Human Liver Samples Using Vitamin E Treatment in NAFLD as a Proof of Concept. Journal of Histochemistry & Cytochemistry, 2020; 68 (9): 635–643.
42. SAYED S, et al. Improving pathology and laboratory medicine in low-income and middle-income countries: roadmap to solutions. The Lancet, 2018; 391 (10133): 1939–1952.
43. SIKANDAR A. Histopathology: An Old Yet Important Technique in Modern Science. In: Histopathology - An Update. [s.l.] InTech, 2018.
44. STATHONIKOS N, et al. Rocky road to digital diagnostics: implementation issues and exhilarating experiences. Journal of Clinical Pathology, 2021; 74 (7): 415–420.
45. WILSON ML, et al. Access to pathology and laboratory medicine services: a crucial gap. The Lancet, 2018; 391 (10133): 1927–1938.
46. YE JJ e TAN MR. Computational Algorithms that Effectively Reduce Report Defects in Surgical Pathology. Journal of Pathology Informatics, 2019; 10 (1): 20.
2. ALVES MCFB, et al. Uso de scanner de lâminas histológicas no processo de aprendizado de patologia entre acadêmicos de medicina / Use of histological blade scanner in the pathology learning process among medicine students. Brazilian Journal of Health Review, 2021; 4 (5): 22098–22103.
3. ARAÚJO ALD, et al. Fully digital pathology laboratory routine and remote reporting of oral and maxillofacial diagnosis during the COVID-19 pandemic: a validation study. Virchows Archiv, 2021; 479 (3): 585-595.
4. BANKHEAD P, et al. Integrated tumor identification and automated scoring minimizes pathologist involvement and provides new insights to key biomarkers in breast cancer. Laboratory Investigation, 2017a; 98 (1): 15-26.
5. BANKHEAD P, et al. QuPath: Open source software for digital pathology image analysis. Scientific Reports, 2017b; 7 (1): 16878.
6. BOIS MC, et al. Three-Dimensional Surface Imaging and Printing in Anatomic Pathology. Journal of Pathology Informatics, 2021; 12 (1): 22.
7. CHEA V, et al. Optimized workflow for digitalized FISH analysis in pathology. Diagnostic Pathology, 2021; 16 (1): 42.
8. CORIOLANO-MARINUS MW DE L, et al. Comunicação nas práticas em saúde: revisão integrativa da literatura. Saúde e Sociedade, 2014; 23 (4): 1356–1369.
9. CUI M e ZHANG DY. Artificial intelligence and computational pathology. Laboratory Investigation, 2021; 101 (4): 412–422.
10. DUNDAR B, et al. Anatomic pathology quality assurance: Developing an LIS based tracking and documentation module for intradepartmental consultations. Journal of Pathology Informatics, 2022; 13: 100109.
11. EVANS AJ, et al. Implementation of Whole Slide Imaging for Clinical Purposes: Issues to Consider From the Perspective of Early Adopters. Archives of Pathology & Laboratory Medicine, 2017; 141 (7): 944–959.
12. FERREIRA I, et al. Digital pathology implementation in a private laboratory: The CEDAP experience. Journal of Pathology Informatics, 2023; 14: 100180.
13. FRAGGETTA F, et al. Routine digital pathology workflow: The Catania experience. Journal of Pathology Informatics, 2017; 8 (1): 51.
14. GHOSH A, et al. Telepathology at the Armed Forces Institute of Pathology: A Retrospective Review of Consultations From 1996 to 1997. Archives of Pathology & Laboratory Medicine, 2018; 142 (2): 248–252.
15. GUO H, et al. Digital pathology and anatomic pathology laboratory information system integration to support digital pathology sign-out. Journal of Pathology Informatics, 2016; 7 (1): 23.
16. GURGEL BCM DOS S, et al. Neoplasias orofaciais em crianças e adolescentes. Arq. odontol, 2020; 56: 1–9.
17. HANNA MG, et al. Validation of a digital pathology system including remote review during the COVID-19 pandemic. Modern Pathology, 2020; 33 (11): 2115–2127.
18. HANNA MG e ARDON O. Digital pathology systems enabling quality patient care. Genes, Chromosomes and Cancer, 2023; 62 (11): 685–697.
19. HANNA MG e PANTANOWITZ L. The role of informatics in patient‐centered care and personalized medicine. Cancer Cytopathology, 2017; 125 (S6): 494–501.
20. HERRMANN MD, et al. Implementing the DICOM Standard for Digital Pathology. Journal of Pathology Informatics, 2018; 9 (1): 37.
21. HOLZER TR, et al. Cross-Platform Comparison of Computer-assisted Image Analysis Quantification of In Situ mRNA Hybridization in Investigative Pathology. Applied Immunohistochemistry & Molecular Morphology, 2019; 27 (1): 15–26.
22. JAHN SW, et al. Digital Pathology: Advantages, Limitations and Emerging Perspectives. Journal of Clinical Medicine, 2020; 9 (11): 3697.
23. KOSOWICZ L, et al. Lessons for Vietnam on the Use of Digital Technologies to Support Patient-Centered Care in Low- and Middle-Income Countries in the Asia-Pacific Region: Scoping Review. Journal of Medical Internet Research, 2023; 25: e43224.
24. KULAK O, et al. Smart Glasses as a Surgical Pathology Grossing Tool. Archives of Pathology & Laboratory Medicine, 2021; 145 (4): 457–460.
25. KUMAR N, et al. Whole Slide Imaging (WSI) in Pathology: Current Perspectives and Future Directions. Journal of Digital Imaging, 2020; 33 (4): 1034–1040.
26. LAM H, et al. UniTwain: A Cost-Effective Solution for Lean Gross Imaging. Journal of Pathology Informatics, 2020; 11 (1): 31.
27. LINO-SILVA LS e XINAXTLE D L. Artificial intelligence technology applications in the pathologic diagnosis of the gastrointestinal tract. Future Oncology, 2020; 16 (34) 2845–2851.
28. LISCIA DS, et al. Whole-Slide Imaging Allows Pathologists to Work Remotely in Regions with Severe Logistical Constraints Due to Covid-19 Pandemic. Journal of Pathology Informatics, 2020; 11 (1): 20.
29. LOUGHREY MB, et al. Validation of the systematic scoring of immunohistochemically stained tumour tissue microarrays using QuPath digital image analysis. Histopathology, 2018; 73 (2): 327–338.
30. LUJAN G, et al. Dissecting the Business Case for Adoption and Implementation of Digital Pathology: A White Paper from the Digital Pathology Association. Journal of Pathology Informatics, 2021; 12 (1) 17.
31. MADKE B e GARDNER JM. Enhanced Worldwide Dermatology–Pathology Interaction via Facebook, Twitter, and Other Social Media Platforms. The American Journal of Dermatopathology, 2018; 40 (3): 168–172.
32. MARTEL AL, et al. An Image Analysis Resource for Cancer Research: PIIP—Pathology Image Informatics Platform for Visualization, Analysis, and Management. Cancer Research, 2017; 77 (21): e83–e86.
33. NALISNIK M, et al. Interactive phenotyping of large-scale histology imaging data with HistomicsML. Scientific Reports, 2017; 7 (1): 14588.
34. NAVANI S. Manual evaluation of tissue microarrays in a high‐throughput research project: The contribution of Indian surgical pathology to the Human Protein Atlas (HPA) project. PROTEOMICS, 2016; 16 (8): 1266–1270.
35. ORAH N e ROTIMI O. Telepathology in Low Resource African Settings. Frontiers in Public Health, 2019; 7.
36. PALLUA JD, et al. The future of pathology is digital. Pathology - Research and Practice, 2020; 216 (9): 153040.
37. PANTANOWITZ L, et al. Twenty Years of Digital Pathology: An Overview of the Road Travelled, What is on the Horizon, and the Emergence of Vendor-Neutral Archives. Journal of Pathology Informatics, 2018; 9 (1): 40.
38. PELL R, et al. The use of digital pathology and image analysis in clinical trials. The Journal of Pathology: Clinical Research, 2019; 5 (2): 81–90.
39. PEREIRA FS, et al. Desenvolvimento e validação de aplicativo para macroscopia do aparelho genital feminino. Revista Eletrônica Acervo Saúde, 2023; 23 (9): e13164.
40. PEREZ NA, et al. Ex vivo 3D scanning and specimen mapping in anatomic pathology. Journal of Pathology Informatics, 2023; 14: 100186.
41. PODSZUN MC, et al. 4-HNE Immunohistochemistry and Image Analysis for Detection of Lipid Peroxidation in Human Liver Samples Using Vitamin E Treatment in NAFLD as a Proof of Concept. Journal of Histochemistry & Cytochemistry, 2020; 68 (9): 635–643.
42. SAYED S, et al. Improving pathology and laboratory medicine in low-income and middle-income countries: roadmap to solutions. The Lancet, 2018; 391 (10133): 1939–1952.
43. SIKANDAR A. Histopathology: An Old Yet Important Technique in Modern Science. In: Histopathology - An Update. [s.l.] InTech, 2018.
44. STATHONIKOS N, et al. Rocky road to digital diagnostics: implementation issues and exhilarating experiences. Journal of Clinical Pathology, 2021; 74 (7): 415–420.
45. WILSON ML, et al. Access to pathology and laboratory medicine services: a crucial gap. The Lancet, 2018; 391 (10133): 1927–1938.
46. YE JJ e TAN MR. Computational Algorithms that Effectively Reduce Report Defects in Surgical Pathology. Journal of Pathology Informatics, 2019; 10 (1): 20.