ComPRePS: An Automated Cloud-based Image Analysis tool to democratize AI in Digital Pathology.

AI AI in healthcare Automatic feature extraction Cloud platform Digital and Computational Pathology FAIR Functional tissue units Image segmentation Whole slide images

Journal

Proceedings of SPIE--the International Society for Optical Engineering
ISSN: 0277-786X
Titre abrégé: Proc SPIE Int Soc Opt Eng
Pays: United States
ID NLM: 101524122

Informations de publication

Date de publication:
Feb 2024
Historique:
medline: 30 5 2024
pubmed: 30 5 2024
entrez: 30 5 2024
Statut: ppublish

Résumé

Artificial intelligence (AI) has extensive applications in a wide range of disciplines including healthcare and clinical practice. Advances in high-resolution whole-slide brightfield microscopy allow for the digitization of histologically stained tissue sections, producing gigapixel-scale whole-slide images (WSI). The significant improvement in computing and revolution of deep neural network (DNN)-based AI technologies over the last decade allow us to integrate massively parallelized computational power, cutting-edge AI algorithms, and big data storage, management, and processing. Applied to WSIs, AI has created opportunities for improved disease diagnostics and prognostics with the ultimate goal of enhancing precision medicine and resulting patient care. The National Institutes of Health (NIH) has recognized the importance of developing standardized principles for data management and discovery for the advancement of science and proposed the Findable, Accessible, Interoperable, Reusable, (FAIR) Data Principles

Identifiants

pubmed: 38813089
doi: 10.1117/12.3008469
pmc: PMC11136532
pii:
doi:

Types de publication

Journal Article

Langues

eng

Auteurs

Sayat Mimar (S)

Quantitative Health Section, Division of Nephrology, Hypertension, and Renal Transplantation, Department of Medicine, University of Florida, Gainesville, FL.

Anindya S Paul (AS)

Quantitative Health Section, Division of Nephrology, Hypertension, and Renal Transplantation, Department of Medicine, University of Florida, Gainesville, FL.

Nicholas Lucarelli (N)

Quantitative Health Section, Division of Nephrology, Hypertension, and Renal Transplantation, Department of Medicine, University of Florida, Gainesville, FL.

Samuel Border (S)

Quantitative Health Section, Division of Nephrology, Hypertension, and Renal Transplantation, Department of Medicine, University of Florida, Gainesville, FL.

Ahmed Naglah (A)

Quantitative Health Section, Division of Nephrology, Hypertension, and Renal Transplantation, Department of Medicine, University of Florida, Gainesville, FL.

Laura Barisoni (L)

Duke University, Department of Pathology, Division of AI & Computational Pathology, Durham, NC.

Jeffrey Hodgin (J)

Department of Pathology, University of Michigan, Ann Arbor, MI.

Avi Z Rosenberg (AZ)

Department of Pathology, Johns Hopkins University, Baltimore, MD.

William Clapp (W)

Department of Pathology, Immunology and Laboratory Medicine, University of Florida College of Medicine, Gainesville, FL.

Pinaki Sarder (P)

Quantitative Health Section, Division of Nephrology, Hypertension, and Renal Transplantation, Department of Medicine, University of Florida, Gainesville, FL.

Classifications MeSH