+ HEALTH Background Discharge letters are essential for continuity of care, yet patients often misunderstand their con…± HEALTH Hematoxylin and Eosin (H&E) glass slide preparation is fundamental to histopathology. Pathologists' expertise…+ CLIMATE Traditional soil heavy metal risk assessments suffer from fragmented indices and poor logical integration. Co…± HEALTH Background Neonatal pneumothorax can progress rapidly, yet detection on supine chest radiographs remains chal…+ HEALTH T cell exhaustion has widespread implications for the progression and treatment of chronic diseases including…+ HEALTH Background Standard pulmonary arterial hypertension (PAH) risk models may not fully apply to PAH associated w…+ HEALTH Objectives To map and synthesize the scientific evidence regarding the use of Augmented Reality (AR) and Mixe…± HEALTH Objective To examine whether the outputs of machine learning algorithms designed to predict risk of cardiovas…
TruaceTracing the truth around AIMonday, September 14, 2026
Subscribe
Health·The Trace·Dual reading·Published 2026-09-14

quality of H&E slide preparation as input for AI-enabled digital pathology

Source article: Current quality challenges in H&E preparation: The critical foundation for increased use of digital pathology and AI emergence

Abstract: 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-…

TRV-2026-1087Peer-reviewedPermanent record — cite & verify
Trace impact reading

Contested: both sides are scored from claims and sources, not community votes.

P 68The P score combines the specificity and measured human impact of the grounded problem claim with the strength of this Trace’s cited sources.G 72The G score combines the specificity and measured human impact of the grounded gain claim with the strength of this Trace’s cited sources.
Current quality challenges in H&E preparation: The critical foundation for increased use of digital pathology and AI emergence

"Respiratory bronchiolitis" by Pulmonary Pathology is licensed under CC BY-SA 2.0. To view a copy of this license, visit https://creativecommons.org/licenses/by-sa/2.0/.

The 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.

Main 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.
Gain

Standardizing pre-analytical H&E preparation before AI deployment can reduce computational burden and deployment costs while supporting robust AI performance in pathology.

Problem

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.

The 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.

What this doesn’t fix

Findings are bounded to the French EQA system and vary by tissue type and year, limiting generalizability to other countries or lab networks.

Sources

Reader signal

How should this claim be treated?

The debate