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Artificial intelligence-assisted identification and quantification of osteoclasts

Thomas Emmanuel, Annemarie Brüel, Jesper Skovhus Thomsen, Torben Steiniche, Mikkel Bo Brent
MethodsX 2021 v.8 pp. 101272
artificial intelligence, bone resorption, cathepsin K, color, computer software, microscopy, osteoclasts
Quantification of osteoclasts to assess bone resorption is a time-consuming and tedious process. Since the inception of bone histomorphometry and manual counting of osteoclasts using bright-field microscopy, several approaches have been proposed to accelerate the counting process using both free and commercially available software. However, most of the present alternatives depend on manual or semi-automatic color segmentation and do not take advantage of artificial intelligence (AI). The present study directly compare estimates of osteoclast-covered surfaces (Oc.S/BS) obtained by the conventional manual method using a bright-field microscope to that obtained by a new AI-assisted method. We present a detailed step-by-step guide for the AI-based method. Tibiae from Wistar rats were either enzymatically stained for TRAP or immunostained for cathepsin K to identify osteoclasts. We found that estimation of Oc.S/BS by the new AI-assisted method was considerably less time-consuming, while still providing similar results to the conventional manual method. In addition, the retrainable AI-module used in the present study allows for fully automated overnight batch processing of multiple annotated sections.•Bone histomorphometry•AI-assisted osteoclast identification•TRAP and cathepsin K