ArticleHuman cell2018
Classification of C2C12 cells at differentiation by convolutional neural network of deep learning using phase contrast images.
Article in Human cell, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed, 55 citations in OpenAlex.
- Cancer Cell-Derived Small Extracellular Vesicles Are Associated with Impaired C2C12 Myoblast Differentiation.International journal of molecular sciences · 2026Article
- From pixels to patterns: the AI revolution in stem cell-derived models.Nature methods · 2026Review
- Cell-MICS: Detecting Immune Cells With Label-Free Two-Photon Autofluorescence and Deep Learning.Journal of biophotonics · 2026Article
- Early and non-destructive prediction of the differentiation efficiency of human induced pluripotent stem cells using imaging and machine learning.Scientific reports · 2025Article
- Riemannian Manifolds for Biological Imaging Applications Based on Unsupervised Learning.Journal of imaging · 2025Article
- The role of primary cilia in myoblast proliferation and cell cycle regulation during myogenesis.Cell structure and function · 2025Review
- Ultrastructural insights into early myoblast differentiation induced by shockwave stimulation.Frontiers in physiology · 2025Article
- Microscopic image-based classification of adipocyte differentiation by machine learning.Histochemistry and cell biology · 2023Article
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- Induced Pluripotent Stem Cell-Based Drug Screening by Use of Artificial Intelligence.Pharmaceuticals (Basel, Switzerland) · 2022Review
- Fully automated platelet differential interference contrast image analysis via deep learning.Scientific reports · 2022Article
- Human Dystrophin Dp71ab Enhances the Proliferation of Myoblasts Across Species But Not Human Nonmyoblast Cells.Frontiers in cell and developmental biology · 2022Article
- In silico-labeled ghost cytometry.eLife · 2021Article
- Insights into the Interaction of Lysosomal Amino Acid Transporters SLC38A9 and SLC36A1 Involved in mTORC1 Signaling in C2C12 Cells.Biomolecules · 2021Article
- Diagnostic performance for pulmonary adenocarcinoma on CT: comparison of radiologists with and without three-dimensional convolutional neural network.European radiology · 2021Article
- Human embryonic stem cell classification: random network with autoencoded feature extractor.Journal of biomedical optics · 2021Article
- Nonmechanical parfocal and autofocus features based on wave propagation distribution in lensfree holographic microscopy.Scientific reports · 2021Article
- The importance of scoring recognition fitness in spheroid morphological analysis for robust label-free quality evaluation.Regenerative therapy · 2020Article
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Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
In the field of regenerative medicine, tremendous numbers of cells are necessary for tissue/organ regeneration. Today automatic cell-culturing system has been developed. The next step is constructing a non-invasive method to monitor the conditions of cells automatically. As an image analysis method, convolutional neural network (CNN), one of the deep learning method, is approaching human recognition level. We constructed and applied the CNN algorithm for automatic cellular differentiation recognition of myogenic C2C12 cell line. Phase-contrast images of cultured C2C12 are prepared as input dataset. In differentiation process from myoblasts to myotubes, cellular morphology changes from round shape to elongated tubular shape due to fusion of the cells. CNN abstract the features of the shape of the cells and classify the cells depending on the culturing days from when differentiation is induced. Changes in cellular shape depending on the number of days of culture (Day 0, Day 3, Day 6) are classified with 91.3% accuracy. Image analysis with CNN has a potential to realize regenerative medicine industry.
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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.