ArticleJournal of pathology informatics2023
Analysis of application of digital image analysis in histopathology quality control.
Article in Journal of pathology informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- An AI framework for automated quality control of paraffin block and slide consistency: a clinical evaluation and human-machine comparison study.Virchows Archiv : an international journal of pathology · 2026Article
- Histopathological Alterations inInsects · 2024Article
- Artificial Intelligence-Based Quality Assessment of Histopathology Whole-Slide Images within a Clinical Workflow: Assessment of 'PathProfiler' in a Diagnostic Pathology Setting.Diagnostics (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: A correct histopathological diagnosis is dependent on an array of technical variables. The quality and completeness of a histological section on a slide is extremely prudent for correct interpretation. However, this is mostly done manually and depends largely on the expertise of histotechnician. In this study, we analysed the application of digital image analysis for quality control of histological section as a proof-of-concept. Material and methods: Images of 1000 histological sections and their corresponding blocks were captured. Area of the section was measured from these digital images of tissue block (Digiblock) and slide (Digislide). The data was analysed to calculate DigislideQC score, dividing the area of tissue on the slide by the tissue area on the block and it was compared with the number of recuts done for incomplete section. Results: Digislide QC score ranged from 0.1 to 0.99. It showed an area under curve (AUC) of 98.8%. A cut-off value of 0.65 had a sensitivity of 99.6% and a specificity of 96.7%. Conclusion: Digiblock and Digislide images can provide information about quality of sections. DigislideQC score can correctly identify the slides which require recuts before it is sent for reporting and potentially reduce histopathologists' slide screening effort and ultimately turnaround time. These can be incorporated in routine histopathology workflows and lab information systems. This simple technology can also improve future digital pathology and telepathology workflows.
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