Digital Pathology Image Analysis
The pathology ecosystem is changing as a result of the development of digital pathology, an image-based environment for the acquisition, management, and interpretation of pathology information backed by computer approaches for data extraction and analysis. With the introduction of whole slide digital imaging systems, pathology education has benefited from new interactive models, clinical workflows have been sped up, and opportunities for the creation of new image analysis tools and investigative methods have arisen. Although it is still developing, this technology has given the clinical and scientific community new options that must be incorporated with current care paradigms.
Key Points
The use of digital pathology in clinical research, clinical trials, and clinical practise has sparked the creation of novel machine-learning models for tissue interrogation that have the potential to advance our understanding of disease mechanisms, identify comprehensive, patient-specific phenotypes, classify kidney patients into categories that are clinically relevant, predict the course of the disease, and ultimately lead to the development of more targeted therapies.
Pathology is now at the forefront of this effort to redefine renal illnesses thanks to the advent of computational image analysis methods for tissue interrogation.
In the end, the use of artificial intelligence tools and the development of synergistic human-machine protocols that combine digital pathology data with clinical and molecular data for personalised nephrology will greatly influence our ability to treat kidney diseases.
Computational Image Analysis
Pathologists can now work with technology experts like data scientists, computational engineers, and imaging physicists to explore the potential of a recently established branch in the field of pathology thanks to advancements in scanning technology and the growing accessibility of large digital image datasets.
