Digital pathology in cardiac transplant diagnostics: from biopsies to algorithms

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023001%3A_____%2F24%3A00084436" target="_blank" >RIV/00023001:_____/24:00084436 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00064190:_____/23:10001081 RIV/00216208:11120/24:43926137 RIV/00216208:11130/24:10470659

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S1054880723000716?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1054880723000716?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.carpath.2023.107587" target="_blank" >10.1016/j.carpath.2023.107587</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Digital pathology in cardiac transplant diagnostics: from biopsies to algorithms

Popis výsledku v původním jazyce

In the field of heart transplantation, the ability to accurately and promptly diagnose cardiac allograft rejection is crucial. This comprehensive review explores the transformative role of digital pathology and computational pathology, especially through machine learning, in this critical domain. These methodologies harness large datasets to extract subtle patterns and valuable information that extend beyond human perceptual capabilities, potentially enhancing diagnostic outcomes. Current research indicates that these computer-based systems could offer accuracy and performance matching, or even exceeding, that of expert pathologists, thereby introducing more objectivity and reducing observer variability. Despite promising results, several challenges such as limited sample sizes, diverse data sources, and the absence of standardized protocols pose significant barriers to the widespread adoption of these techniques. The future of digital pathology in heart transplantation diagnostics depends on utilizing larger, more diverse patient cohorts, standardizing data collection, processing, and evaluation protocols, and fostering collaborative research efforts. The integration of various data types, including clinical, demographic, and imaging information, could further refine diagnostic precision. As researchers address these challenges and promote collaborative efforts, digital pathology has the potential to become an integral part of clinical practice, ultimately improving patient care in heart transplantation.

Název v anglickém jazyce

Digital pathology in cardiac transplant diagnostics: from biopsies to algorithms

Popis výsledku anglicky

In the field of heart transplantation, the ability to accurately and promptly diagnose cardiac allograft rejection is crucial. This comprehensive review explores the transformative role of digital pathology and computational pathology, especially through machine learning, in this critical domain. These methodologies harness large datasets to extract subtle patterns and valuable information that extend beyond human perceptual capabilities, potentially enhancing diagnostic outcomes. Current research indicates that these computer-based systems could offer accuracy and performance matching, or even exceeding, that of expert pathologists, thereby introducing more objectivity and reducing observer variability. Despite promising results, several challenges such as limited sample sizes, diverse data sources, and the absence of standardized protocols pose significant barriers to the widespread adoption of these techniques. The future of digital pathology in heart transplantation diagnostics depends on utilizing larger, more diverse patient cohorts, standardizing data collection, processing, and evaluation protocols, and fostering collaborative research efforts. The integration of various data types, including clinical, demographic, and imaging information, could further refine diagnostic precision. As researchers address these challenges and promote collaborative efforts, digital pathology has the potential to become an integral part of clinical practice, ultimately improving patient care in heart transplantation.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30109 - Pathology

Projekt

—

Návaznosti

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Cardiovascular pathology

ISSN

1054-8807

e-ISSN

1879-1336

Svazek periodika

68

Číslo periodika v rámci svazku

January–February 2024

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

8

Strana od-do

"art. no. 107587"

Kód UT WoS článku

001125337700001

EID výsledku v databázi Scopus

2-s2.0-85178279854