Cargando…
Cultivating Clinical Clarity through Computer Vision: A Current Perspective on Whole Slide Imaging and Artificial Intelligence
Diagnostic devices, methodological approaches, and traditional constructs of clinical pathology practice, cultivated throughout centuries, have transformed radically in the wake of explosive technological growth and other, e.g., environmental, catalysts of change. Ushered into the fray of modern lab...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9332710/ https://www.ncbi.nlm.nih.gov/pubmed/35892487 http://dx.doi.org/10.3390/diagnostics12081778 |
_version_ | 1784758714927939584 |
---|---|
author | Patel, Ankush U. Shaker, Nada Mohanty, Sambit Sharma, Shivani Gangal, Shivam Eloy, Catarina Parwani, Anil V. |
author_facet | Patel, Ankush U. Shaker, Nada Mohanty, Sambit Sharma, Shivani Gangal, Shivam Eloy, Catarina Parwani, Anil V. |
author_sort | Patel, Ankush U. |
collection | PubMed |
description | Diagnostic devices, methodological approaches, and traditional constructs of clinical pathology practice, cultivated throughout centuries, have transformed radically in the wake of explosive technological growth and other, e.g., environmental, catalysts of change. Ushered into the fray of modern laboratory medicine are digital imaging devices and machine-learning (ML) software fashioned to mitigate challenges, e.g., practitioner shortage while preparing clinicians for emerging interconnectivity of environments and diagnostic information in the era of big data. As computer vision shapes new constructs for the modern world and intertwines with clinical medicine, cultivating clarity of our new terrain through examining the trajectory and current scope of computational pathology and its pertinence to clinical practice is vital. Through review of numerous studies, we find developmental efforts for ML migrating from research to standardized clinical frameworks while overcoming obstacles that have formerly curtailed adoption of these tools, e.g., generalizability, data availability, and user-friendly accessibility. Groundbreaking validatory efforts have facilitated the clinical deployment of ML tools demonstrating the capacity to effectively aid in distinguishing tumor subtype and grade, classify early vs. advanced cancer stages, and assist in quality control and primary diagnosis applications. Case studies have demonstrated the benefits of streamlined, digitized workflows for practitioners alleviated by decreased burdens. |
format | Online Article Text |
id | pubmed-9332710 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93327102022-07-29 Cultivating Clinical Clarity through Computer Vision: A Current Perspective on Whole Slide Imaging and Artificial Intelligence Patel, Ankush U. Shaker, Nada Mohanty, Sambit Sharma, Shivani Gangal, Shivam Eloy, Catarina Parwani, Anil V. Diagnostics (Basel) Review Diagnostic devices, methodological approaches, and traditional constructs of clinical pathology practice, cultivated throughout centuries, have transformed radically in the wake of explosive technological growth and other, e.g., environmental, catalysts of change. Ushered into the fray of modern laboratory medicine are digital imaging devices and machine-learning (ML) software fashioned to mitigate challenges, e.g., practitioner shortage while preparing clinicians for emerging interconnectivity of environments and diagnostic information in the era of big data. As computer vision shapes new constructs for the modern world and intertwines with clinical medicine, cultivating clarity of our new terrain through examining the trajectory and current scope of computational pathology and its pertinence to clinical practice is vital. Through review of numerous studies, we find developmental efforts for ML migrating from research to standardized clinical frameworks while overcoming obstacles that have formerly curtailed adoption of these tools, e.g., generalizability, data availability, and user-friendly accessibility. Groundbreaking validatory efforts have facilitated the clinical deployment of ML tools demonstrating the capacity to effectively aid in distinguishing tumor subtype and grade, classify early vs. advanced cancer stages, and assist in quality control and primary diagnosis applications. Case studies have demonstrated the benefits of streamlined, digitized workflows for practitioners alleviated by decreased burdens. MDPI 2022-07-22 /pmc/articles/PMC9332710/ /pubmed/35892487 http://dx.doi.org/10.3390/diagnostics12081778 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Patel, Ankush U. Shaker, Nada Mohanty, Sambit Sharma, Shivani Gangal, Shivam Eloy, Catarina Parwani, Anil V. Cultivating Clinical Clarity through Computer Vision: A Current Perspective on Whole Slide Imaging and Artificial Intelligence |
title | Cultivating Clinical Clarity through Computer Vision: A Current Perspective on Whole Slide Imaging and Artificial Intelligence |
title_full | Cultivating Clinical Clarity through Computer Vision: A Current Perspective on Whole Slide Imaging and Artificial Intelligence |
title_fullStr | Cultivating Clinical Clarity through Computer Vision: A Current Perspective on Whole Slide Imaging and Artificial Intelligence |
title_full_unstemmed | Cultivating Clinical Clarity through Computer Vision: A Current Perspective on Whole Slide Imaging and Artificial Intelligence |
title_short | Cultivating Clinical Clarity through Computer Vision: A Current Perspective on Whole Slide Imaging and Artificial Intelligence |
title_sort | cultivating clinical clarity through computer vision: a current perspective on whole slide imaging and artificial intelligence |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9332710/ https://www.ncbi.nlm.nih.gov/pubmed/35892487 http://dx.doi.org/10.3390/diagnostics12081778 |
work_keys_str_mv | AT patelankushu cultivatingclinicalclaritythroughcomputervisionacurrentperspectiveonwholeslideimagingandartificialintelligence AT shakernada cultivatingclinicalclaritythroughcomputervisionacurrentperspectiveonwholeslideimagingandartificialintelligence AT mohantysambit cultivatingclinicalclaritythroughcomputervisionacurrentperspectiveonwholeslideimagingandartificialintelligence AT sharmashivani cultivatingclinicalclaritythroughcomputervisionacurrentperspectiveonwholeslideimagingandartificialintelligence AT gangalshivam cultivatingclinicalclaritythroughcomputervisionacurrentperspectiveonwholeslideimagingandartificialintelligence AT eloycatarina cultivatingclinicalclaritythroughcomputervisionacurrentperspectiveonwholeslideimagingandartificialintelligence AT parwanianilv cultivatingclinicalclaritythroughcomputervisionacurrentperspectiveonwholeslideimagingandartificialintelligence |