ADAM: automated digital phenotyping and morphological texture analysis of bone biopsy images using deep learning.

Histomorphometric analysis of undecalcified bone biopsy images provides quantitative assessment of bone turnover, volume, and mineralization using static and dynamic parameters. Traditionally, quantification has relied on manual annotation and tracing of relevant tissue structures, a process that is time-intensive and subject to inter-operator variability. We developed ADAM, an automated pipeline for digital phenotyping, to quantify tissue and cellular components pertinent to static histomorphometric parameters such as bone and osteoid area, osteoclast and osteoblast count, and bone marrow adipose tissue (BMAT) area. The pipeline allowed rapid generation of delineated tissue and cell maps for up to 20 images in less than a minute. Comparing deep learning-generated annotation pixels with manual annotations, we observed Spearman correlation coefficients of ρ = 0.99 for both mineralized bone and osteoid, and ρ = 0.94 for BMAT. For osteoclast and osteoblast cell counts, which are subject to morphologic heterogeneity, using only brightfield microscopic images and without additional staining, we noted ρ = 0.60 and 0.69, respectively (inter-operator correlation was ρ = 0.62 for osteoclast and 0.84 for osteoblast count). The study also explored the application of morphological texture analysis (MTA), measuring relative pixel patterns that potentially vary with diverse tissue conditions. Notably, MTA from mineralized bone, osteoid, and BMAT showed differentiating potential to identify common pixel characteristics between images labeled as low or high bone turnover based upon the final diagnostic report of the bone biopsy. The Area Under the Receiver Operating Characteristic Curve (AUC-ROC) obtained for BMAT MTA features as a classifier for bone turnover, was 0.87, suggesting that computer-extracted features, not discernable to the human eye, hold potential in classifying tissue states. With additional evaluation, ADAM could be potentially integrated into existing clinical routines to improve pathology workflows and contribute to diagnostic insights into bone biopsy evaluation and reporting.