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Related Experiment Video

Updated: Jun 7, 2025

Multimodal Quantitative Phase Imaging with Digital Holographic Microscopy Accurately Assesses Intestinal Inflammation and Epithelial Wound Healing

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Published on: September 13, 2016

Development of a deep learning algorithm for Paneth cell density quantification for inflammatory bowel disease.

Liang-I Kang¹, Kathryn Sarullo¹, Jon N Marsh¹

¹Department of Pathology & Immunology, Washington University in St. Louis School of Medicine, 660 South Euclid Avenue, Campus Box 8118, Saint Louis, MO, 63110, United States.

|November 13, 2024

Summary

This summary is machine-generated.

Deep learning accurately quantifies Paneth cell (PC) density in ileal tissue, identifying it as a predictive biomarker for Crohn's disease recurrence. This AI tool streamlines analysis for clinical applications.

Keywords:

Crohn's disease Pathology Prognosis Whole slide images

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Fluorescence-mediated Tomography for the Detection and Quantification of Macrophage-related Murine Intestinal Inflammation

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Area of Science:

Gastroenterology
Computational Pathology
Biomarker Discovery

Background:

Ileal Paneth cell (PC) density alterations are linked to inflammatory bowel diseases like Crohn's disease (CD).
Quantifying PC density is crucial for disease prognosis but is currently time-intensive, hindering clinical workflow.
Deep learning (DL) offers a potential solution for accurate and efficient image evaluation.

Purpose of the Study:

To develop and validate a DL-based tool for quantifying PC density in ileal tissue.
To assess the utility of DL-quantified PC density as a predictive biomarker for CD recurrence.

Main Methods:

A U-net two-stage DL model was trained on pathologist-annotated whole slide images (WSI) of ileal tissue.
The DL model quantified PC number, crypt number, and PC density.
Model performance was validated against manual quantification by expert pathologists using RMSE and r² metrics.
PC density was analyzed in cohorts of patients with and without CD, and its association with CD recurrence was evaluated.

Main Results:

The two-stage DL model achieved improved accuracy (RMSE = 0.802, r² = 0.748) compared to a one-stage model.
The DL algorithm demonstrated good performance in validation (RMSE = 1.148, r² = 0.708) against expert pathologists.
Significantly lower PC density was observed in patients with CD compared to non-IBD controls (2.99 vs. 4.04 PC/crypt).
Patients with CD in the lowest quartile of PC density exhibited a significantly shorter recurrence-free interval (p=0.0399).

Conclusions:

The developed DL model is feasible for measuring PC density as a predictive biomarker.
This AI-driven approach has the potential to enhance future clinical practice for managing inflammatory bowel diseases.