Contemporary Advances in Computer-Assisted Bone Histomorphometry and Identification of Bone Cells in Culture.
Artificial intelligence
Bone cell culture
Deep learning
Dynamic bone histomorphometry
Microscopy
Static bone histomorphometry
Journal
Calcified tissue international
ISSN: 1432-0827
Titre abrégé: Calcif Tissue Int
Pays: United States
ID NLM: 7905481
Informations de publication
Date de publication:
01 2023
01 2023
Historique:
received:
29
06
2022
accepted:
13
10
2022
pubmed:
31
10
2022
medline:
7
1
2023
entrez:
30
10
2022
Statut:
ppublish
Résumé
Static and dynamic bone histomorphometry and identification of bone cells in culture are labor-intensive and highly repetitive tasks. Several computer-assisted methods have been proposed to ease these tasks and to take advantage of the increased computational power available today. The present review aimed to provide an overview of contemporary methods utilizing specialized computer software to perform bone histomorphometry or identification of bone cells in culture. In addition, a brief historical perspective on bone histomorphometry is included. We identified ten publications using five different computer-assisted approaches (1) ImageJ and BoneJ; (2) Histomorph: OsteoidHisto, CalceinHisto, and TrapHisto; (3) Fiji/ImageJ2 and Trainable Weka Segmentation (TWS); (4) Visiopharm and artificial intelligence (AI); and (5) Osteoclast identification using deep learning with Single Shot Detection (SSD) architecture, Darknet and You Only Look Once (YOLO), or watershed algorithm (OC_Finder). The review also highlighted a substantial need for more validation studies that evaluate the accuracy of the new computational methods to the manual and conventional analyses of histological bone specimens and cells in culture using microscopy. However, a substantial evolution has occurred during the last decade to identify and separate bone cells and structures of interest. Most early studies have used simple image segmentation to separate structures of interest, whereas the most recent studies have utilized AI and deep learning. AI has been proposed to substantially decrease the amount of time needed for analyses and enable unbiased assessments. Despite the clear advantages of highly sophisticated computational methods, the limited nature of existing validation studies, particularly those that assess the accuracy of the third-generation methods compared to the second-generation methods, appears to be an important reason that these techniques have failed to gain wide acceptance.
Identifiants
pubmed: 36309622
doi: 10.1007/s00223-022-01035-2
pii: 10.1007/s00223-022-01035-2
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
1-12Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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