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Histopathological Images Analysis and Predictive Modeling Implemented in Digital Pathology-Current Affairs and Perspectives.
Moscalu, Mihaela; Moscalu, Roxana; Dascalu, Cristina Gena; Țarca, Viorel; Cojocaru, Elena; Costin, Ioana Madalina; Țarca, Elena; Șerban, Ionela Lacramioara.
Afiliación
  • Moscalu M; Department of Preventive Medicine and Interdisciplinarity, "Grigore T. Popa" University of Medicine and Pharmacy, 700115 Iassy, Romania.
  • Moscalu R; Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M139PT, UK.
  • Dascalu CG; Department of Preventive Medicine and Interdisciplinarity, "Grigore T. Popa" University of Medicine and Pharmacy, 700115 Iassy, Romania.
  • Țarca V; Department of Preventive Medicine and Interdisciplinarity, "Grigore T. Popa" University of Medicine and Pharmacy, 700115 Iassy, Romania.
  • Cojocaru E; Department of Morphofunctional Sciences I, "Grigore T. Popa" University of Medicine and Pharmacy, 700115 Iassy, Romania.
  • Costin IM; Faculty of Medicine, "Grigore T. Popa" University of Medicine and Pharmacy, 700115 Iassy, Romania.
  • Țarca E; Department of Surgery II-Pediatric Surgery, "Grigore T. Popa" University of Medicine and Pharmacy, 700115 Iassy, Romania.
  • Șerban IL; Department of Morpho-Functional Sciences II, Faculty of Medicine, "Grigore T. Popa" University of Medicine and Pharmacy, 700115 Iassy, Romania.
Diagnostics (Basel) ; 13(14)2023 Jul 14.
Article en En | MEDLINE | ID: mdl-37510122
In modern clinical practice, digital pathology has an essential role, being a technological necessity for the activity in the pathological anatomy laboratories. The development of information technology has majorly facilitated the management of digital images and their sharing for clinical use; the methods to analyze digital histopathological images, based on artificial intelligence techniques and specific models, quantify the required information with significantly higher consistency and precision compared to that provided by optical microscopy. In parallel, the unprecedented advances in machine learning facilitate, through the synergy of artificial intelligence and digital pathology, the possibility of diagnosis based on image analysis, previously limited only to certain specialties. Therefore, the integration of digital images into the study of pathology, combined with advanced algorithms and computer-assisted diagnostic techniques, extends the boundaries of the pathologist's vision beyond the microscopic image and allows the specialist to use and integrate his knowledge and experience adequately. We conducted a search in PubMed on the topic of digital pathology and its applications, to quantify the current state of knowledge. We found that computer-aided image analysis has a superior potential to identify, extract and quantify features in more detail compared to the human pathologist's evaluating possibilities; it performs tasks that exceed its manual capacity, and can produce new diagnostic algorithms and prediction models applicable in translational research that are able to identify new characteristics of diseases based on changes at the cellular and molecular level.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Guideline / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Diagnostics (Basel) Año: 2023 Tipo del documento: Article País de afiliación: Rumanía Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Guideline / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Diagnostics (Basel) Año: 2023 Tipo del documento: Article País de afiliación: Rumanía Pais de publicación: Suiza