Digital pathology in cardiac transplant diagnostics: from biopsies to algorithms
The result's identifiers
Result code in 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>
Alternative codes found
RIV/00064190:_____/23:10001081 RIV/00216208:11120/24:43926137 RIV/00216208:11130/24:10470659
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Digital pathology in cardiac transplant diagnostics: from biopsies to algorithms
Original language description
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.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
30109 - Pathology
Result continuities
Project
—
Continuities
N - Vyzkumna aktivita podporovana z neverejnych zdroju
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Cardiovascular pathology
ISSN
1054-8807
e-ISSN
1879-1336
Volume of the periodical
68
Issue of the periodical within the volume
January–February 2024
Country of publishing house
US - UNITED STATES
Number of pages
8
Pages from-to
"art. no. 107587"
UT code for WoS article
001125337700001
EID of the result in the Scopus database
2-s2.0-85178279854