Medizinische Universität Graz - Research portal
Selected Publication:
SHR Neuro Cancer Cardio Lipid Metab Microb
Gramatiuk, S; Kryvoruchko, IA; Ivanova, YV; Hubbard, E; Noebauer-Babenko, M; Sargsyan, K.
Artificial Intelligence-Based Quality Control of Cell Lines
BIOPRESERV BIOBANK. 2025;
Doi: 10.1177/19475535251367317
Web of Science
PubMed
FullText
FullText_MUG
- Co-authors Med Uni Graz
- Sargsyan Karine
- Altmetrics:
- Dimensions Citations:
- Plum Analytics:
- Scite (citation analytics):
- Abstract:
- Introduction: This study is part of the broader Stem Line project Mito-Cell-UAB073, specifically focusing on "Stem Cell Lines-Quality Control," and aims to innovate in the field of Quality Control (QC) through a unique, artificial intelligence (AI)-powered model known as Life Cell AI UAB. This model utilizes deep learning algorithms and computer vision, allowing it to make accurate viability assessments of cell and stem cell lines based solely on static images captured through standard optical microscopes. Aim: The aim of this study was to develop and validate an AI-driven, image-based model that reliably predicts cell line viability. Methods: Our methodology involved training the Life Cell AI UAB model on single static images of cell lines using advanced computer vision and deep learning techniques. Performance evaluation was conducted on three independent blind test sets sourced from various biotechnology laboratories, allowing for assessment across diverse environments. Results: The Life Cell AI UAB model achieved a sensitivity of 82.1% in identifying viable cell lines and a specificity of 67.5% for non-viable lines across the test sets. Each blind test set exhibited a weighted accuracy above 63%, with a combined accuracy of 64.3%. Notably, predictions showed a clear distinction between correctly and incorrectly classified cells. The model outperformed traditional QC methods by improving accuracy in binary classification tasks by 21.9% (p = 0.042) and demonstrated a 42.0% enhancement over conventional Standard Operation Procedure (SOP) procedures (p = 0.026). Conclusion: The Life Cell AI UAB model represents a notable advancement in biobanking QC, offering a precise, standardized, and non-invasive method for assessing cell line viability. This model has the potential to streamline QC processes across laboratories, minimizing the need for time-lapse imaging and promoting uniformity in QC practices for both cell and stem cells.
- Find related publications in this database (Keywords)
- quality control
- artificial intelligence
- cryopreserved stem cells
- cell line