ArticleMethodsX2021
Artificial intelligence-assisted identification and quantification of osteoclasts.
Article in MethodsX, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- NOISe: Nuclei-Aware Osteoclast Instance Segmentation for Mouse-to-Human Domain Transfer.Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops · 2024Article
- Impact of functional electrical stimulation on nerve-damaged muscles by quantifying fat infiltration using deep learning.Scientific reports · 2024Article
- A Machine Learning-Based Image Segmentation Method to Quantify In Vitro Osteoclast Culture Endpoints.Calcified tissue international · 2023Article
- Quantification of Immunohistochemically Stained Cells in Skin Biopsies.Dermatopathology (Basel, Switzerland) · 2022Article
- Effect of Acetazolamide and Zoledronate on Simulated High Altitude-Induced Bone Loss.Frontiers in endocrinology · 2022Article
- OC_Finder: Osteoclast segmentation, counting, and classification using watershed and deep learning.Frontiers in bioinformatics · 2022Article
- Automated Quantification of Human Osteoclasts Using Object Detection.Frontiers in cell and developmental biology · 2022Article
- Real-time quantification of soluble tartrate-resistant acid phosphatase as a measure of osteoclastogenesis.BioTechniquesArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.