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TRUVACE RECORD VERSION
record: TRV-2026-1087
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-14T06:56:20.688143Z
status: published
lens: trace
sector: health
headline: Current quality challenges in H&E preparation: The critical foundation for increased use of digital pathology and AI emergence
dek: Hematoxylin and Eosin (H&E) glass slide preparation is fundamental to histopathology. Pathologists' expertise routinely mitigates potential issues due to quality variability into or across laboratories, ensuring diagnostic accuracy. However, as artificial intelligence (AI) induced by whole slide imaging (WSI) emerges as a future advancement in pathology, a shift toward greater technical standardisation of the preparations or adaptive algorithms deserves consideration. This study examined longitudinal data (2019-…
gain_title: Standardizing pre-analytical H&E preparation before AI deployment can reduce computational burden and deployment costs while supporting robust AI performance in pathology.
problem_title: Up to a quarter of H&E slides in French labs showed technical preparation imperfections and up to 23.8% showed suboptimal staining, creating inconsistent inputs that undermine robust AI performance from whole slide imaging.
trace_subject: quality of H&E slide preparation as input for AI-enabled digital pathology
gain_reading: Standardizing pre-analytical H&E preparation before AI deployment can reduce computational burden and deployment costs while supporting robust AI performance in pathology.
gain_evidence: Prioritising quality at the source reduces computational burden, environmental impact, and deployment costs while enhancing ethical transparency | optimising pre-analytical H&E preparation quality before AI deployment is critical
problem_reading: Up to a quarter of H&E slides in French labs showed technical preparation imperfections and up to 23.8% showed suboptimal staining, creating inconsistent inputs that undermine robust AI performance from whole slide imaging.
problem_evidence: up to 25,5% of H&E slides, varying by tissue type and assessment year, exhibited technical preparation imperfection | 8,3% to 23,8% of slides showed suboptimal staining
quick_read: A peer-reviewed analysis of French national external quality assessment data from 2019-2024 and a 2024 survey found frequent H&E preparation issues: up to 25.5% of slides had technical imperfections such as sectioning, thickness, and stretching problems, and 8.3% to 23.8% had suboptimal staining with poor nucleo-cytoplasmic contrast or intensity fluctuations.

The findings matter because AI induced by whole slide imaging depends on consistent high-quality slides; without pre-analytical standardization, AI deployment faces higher computational costs, environmental impact, and ethical transparency risks, and it remains uncertain whether adaptive algorithms can compensate or whether labs outside France show similar defect rates.
limitation: Findings are bounded to the French EQA system and vary by tissue type and year, limiting generalizability to other countries or lab networks.
tag: Dual reading
key_points: Analysis used longitudinal data (2019-2024) from the French national external quality assessment programme plus a 2024 national survey. | Technical imperfections included issues from microtomic sectioning, excessive thickness, and tissue stretching. | Staining problems were mainly insufficient nucleo-cytoplasmic contrast or intensity fluctuations. | Authors argue against reliance on hypothetical post-hoc algorithmic compensation for poor inputs.
rundown: The study quantified defects across French laboratories using EQA assessments, finding preparation imperfections linked to microtomy, thickness, and stretching, and staining defects linked to contrast and intensity.

Pathologists currently mitigate variability to ensure diagnostic accuracy, but the authors warn this human compensation will not scale to AI workflows that need standardized inputs.

The authors advocate sustained quality improvement at the source before large-scale AI integration, rather than expecting algorithms to correct poor slides after scanning.
sources:
- peer_reviewed | Virchows Archiv | https://doi.org/10.1007/s00428-026-04714-9 | 2026-09-12
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