Table 1.

Advantages of digital pathology

Digital pathology: advantages
Work flow	Remote access by multiple users simultaneously
	Application of multiple scoring systems in different studies
	Targeted adjudication process of annotated structures
	Limits multiple mailing of unreplaceable material
Long term investment	Permanent library of data/images available to multiple investigators, studies, and to test different approaches
	Cyber space storage
	Abatement of costs in long term
Transparency	For intra and interworking groups/consortia collaborations in clinical research
	For regulatory agencies in clinical trials
Standardization	Definition: ‘The process of implementing and developing technical standards can help to maximize compatibility, interoperability, repeatability and quality and to facilitate commoditization of formerly custom processes’
Globalization of the renal biopsy	Achieved by implementing, across different studies worldwide, sharable standardized digital platforms and protocols for disease material acquisition, uploading, organization in the digital pathology repositories, and analysis
Accuracy	‘Degree of closeness of measurements of a quantity to that quantity’s true value’. For example quantification of annotated (enumerated) structures
Reproducibility	One of main principles of scientific methods – facilitated by remote access to same images by multiple observers assessing the same structures
Quantitative disease	Term used for stereology and morphometry or when visual assessment is based on (digital) quantitative metrics. ‘Quantitative disease’ arises from a demand for quantitation, objectivity, awareness that parameters detectable with quantitative analysis would otherwise escape observation, consistency, reproducibility and standardization
Modalities of assessment	Visual morphologic Detection Counting Visual morphometric Counting Measuring Computer aided (computational imaging) Detection Counting Segmentation Tissue classification Feature extraction
Computational nephropathology	Quantitative disease generating biologically and clinically relevant information using mathematical models at the individual and population levels, leading to diagnostic and outcome predicting algorithms
	Goals: Contribute with structural information to a hub for data-related research Characterize patients according to standardized structural parameters Advance precision medicine Increase ability to predict likelihood of progression (predictive medicine)