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BACKGROUND: Alterations in ileal Paneth cell (PC) density have been described in gut inflammatory diseases such as Crohns disease (CD) and could be used as a biomarker for disease prognosis. However, quantifying PCs is time-intensive, a barrier for clinical workflow. Deep learning (DL) has transformed the development of robust and accurate tools for complex image evaluation. Our aim was to use DL to quantify PCs for use as a quantitative biomarker. METHODS: A retrospective cohort of whole slide images (WSI) of ileal tissue samples from patients with/without inflammatory bowel disease (IBD) was used for the study. A pathologist-annotated training set of WSI were used to train a U-net two-stage DL model to quantify PC number, crypt number, and PC density. For validation, a cohort of 48 WSIs were manually quantified by study pathologists and compared to the DL algorithm, using root mean square error (RMSE) and the coefficient of determination (r2) as metrics. To test the value of PC quantification as a biomarker, resection specimens from patients with CD (n = 142) and without IBD (n = 48) patients were analysed with the DL model. Finally, we compared time to disease recurrence in patients with CD with low versus high DL-quantified PC density using Log-rank test. FINDINGS: Initial one-stage DL model showed moderate accuracy in predicting PC density in cross-validation tests (RMSE = 1.880, r2 = 0.641), but adding a second stage significantly improved accuracy (RMSE = 0.802, r2 = 0.748). In the validation of the two-stage model compared to expert pathologists, the algorithm showed good performance up to RMSE = 1.148, r2 = 0.708. The retrospective cross-sectional cohort had mean ages of 62.1 years in the patients without IBD and 38.6 years for the patients with CD. In the non-IBD cohort, 43.75% of the patients were male, compared to 49.3% of the patients with CD. Analysis by the DL model showed significantly higher PC density in non-IBD controls compared to the patients with CD (4.04 versus 2.99 PC/crypt). Finally, the algorithm quantification of PCs density in patients with CD showed patients with the lowest 25% PC density (Quartile 1) have significantly shorter recurrence-free interval (p = 0.0399). INTERPRETATION: The current model performance demonstrates the feasibility of developing a DL-based tool to measure PC density as a predictive biomarker for future clinical practice. FUNDING: This study was funded by the National Institutes of Health (NIH).

Background: Alterations in ileal Paneth cell (PC) density have been described in gut inflammatory diseases such as Crohn's disease (CD) and could be used as a biomarker for disease prognosis. However, quantifying PCs is time-intensive, a barrier for clinical workflow. Deep learning (DL) has transformed the development of robust and accurate tools for complex image evaluation. Our aim was to use DL to quantify PCs for use as a quantitative biomarker., Methods: A retrospective cohort of whole slide images (WSI) of ileal tissue samples from patients with/without inflammatory bowel disease (IBD) was used for the study. A pathologist-annotated training set of WSI were used to train a U-net two-stage DL model to quantify PC number, crypt number, and PC density. For validation, a cohort of 48 WSIs were manually quantified by study pathologists and compared to the DL algorithm, using root mean square error (RMSE) and the coefficient of determination (r 2 ) as metrics. To test the value of PC quantification as a biomarker, resection specimens from patients with CD (n = 142) and without IBD (n = 48) patients were analysed with the DL model. Finally, we compared time to disease recurrence in patients with CD with low versus high DL-quantified PC density using Log-rank test., Findings: Initial one-stage DL model showed moderate accuracy in predicting PC density in cross-validation tests (RMSE = 1.880, r 2  = 0.641), but adding a second stage significantly improved accuracy (RMSE = 0.802, r 2  = 0.748). In the validation of the two-stage model compared to expert pathologists, the algorithm showed good performance up to RMSE = 1.148, r 2  = 0.708. The retrospective cross-sectional cohort had mean ages of 62.1 years in the patients without IBD and 38.6 years for the patients with CD. In the non-IBD cohort, 43.75% of the patients were male, compared to 49.3% of the patients with CD. Analysis by the DL model showed significantly higher PC density in non-IBD controls compared to the patients with CD (4.04 versus 2.99 PC/crypt). Finally, the algorithm quantification of PCs density in patients with CD showed patients with the lowest 25% PC density (Quartile 1) have significantly shorter recurrence-free interval (p = 0.0399)., Interpretation: The current model performance demonstrates the feasibility of developing a DL-based tool to measure PC density as a predictive biomarker for future clinical practice., Funding: This study was funded by the National Institutes of Health (NIH)., Competing Interests: Declaration of interests The authors have no conflict of interest to declare in relation to the work presented., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Race is commonly used as a proxy for multiple features including socioeconomic status. It is critical to dissociate these factors, to identify mechanisms that affect infant outcomes, such as birth weight, gestational age, and brain development, and to direct appropriate interventions and shape public policy., Methods: Demographic, socioeconomic, and clinical variables were used to model infant outcomes. There were 351 participants included in the analysis for birth weight and gestational age. For the analysis using brain volumes, 280 participants were included after removing participants with missing magnetic resonance imaging scans and those matching our exclusion criteria. We modeled these three different infant outcomes, including infant brain, birth weight, and gestational age, with both linear and nonlinear models., Results: Nonlinear models were better predictors of infant birth weight than linear models ( R 2  = 0.172 vs. R 2  = 0.145, p  = .005). In contrast to linear models, nonlinear models ranked income, neighborhood disadvantage, and experiences of discrimination higher in importance than race while modeling birth weight. Race was not an important predictor for either gestational age or structural brain volumes., Conclusions: Consistent with the extant social science literature, the findings related to birth weight suggest that race is a linear proxy for nonlinear factors related to structural racism. Methods that can disentangle factors often correlated with race are important for policy in that they may better identify and rank the modifiable factors that influence outcomes., (© 2023 The Authors.)