Kidney fibrosis evaluation using dual-mode emission and transmission microscopy
Year: 2026
Type: article
Abstract: PurposeInterstitial fibrosis is a critical histopathologic feature used in the diagnosis and prognosis of kidney diseases. However, conventional assessment using trichrome-stained slides is limited by interobserver variability and inconsistent stain reproducibility. This study evaluates whether dual-mode emission and transmission (DUET) microscopy, which generates virtual trichrome images from hematoxylin and eosin (H&E) slides, can provide a practical, reproducible, and quantitative alternative for assessing interstitial fibrosis in kidney transplant biopsies.ApproachThirty-two kidney transplant biopsies were imaged using DUET to acquire pixel-registered brightfield and fluorescence whole-slide images from H&E-stained slides. A deep learning pipeline was trained on DUET-acquired images to extract collagen-positive signal from both the brightfield and fluorescence channels, which was used to generate virtual trichrome images and to compute quantitative fibrosis scores. Virtual trichrome images were compared with corresponding conventional trichrome slides. Four renal pathologists independently estimated interstitial fibrosis on both modalities to assess interobserver agreement, and computational fibrosis scores were correlated with pathologists’ estimates.ResultsPathologist assessments of fibrosis on DUET-derived virtual trichrome images demonstrated interobserver agreement comparable to traditional trichrome slides, trending higher for virtual trichrome (ICC 0.536 versus 0.477, p=0.376). Agreement of each pathologist with the other three readers was higher for virtual trichrome in three of the four pathologists and significantly higher in one. Fibrosis estimates were systematically lower on virtual trichrome than on real trichrome, by a mean of 7.6 percentage points. The deep learning model for collagen extraction achieved high segmentation performance (AUC 0.994, Dice 0.903). Computational fibrosis quantitation showed moderate correlation with pathologist estimates and demonstrated stronger association with virtual trichrome than real trichrome (r 0.67 versus 0.54, R2 0.44 versus 0.29).ConclusionsDUET microscopy enables the generation of pixel-registered virtual trichrome images from standard H&E slides with interobserver agreement comparable to conventional trichrome. By extracting collagen signal from pixel-registered fluorescence data on the same tissue section, DUET supports reproducible visual assessment and quantitative fibrosis measurement. This approach provides a scalable alternative to conventional trichrome staining and a foundation for integrated digital pathology workflows.
Source: Journal of medical imaging
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7225255135
FWCI:
Cites:
Cited by:
Related to: