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

ABSTRACT: 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 ma

SUBMITTER: Bharadwaj S 

PROVIDER: S-EPMC11931614 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature