ArticleScientific reports2020
Automated spheroid generation, drug application and efficacy screening using a deep learning classification: a feasibility study.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- From microtissues to macro solutions - The future of scalable and automated cartilage tissue engineering.Journal of orthopaedic translation · 2026Review
- High-throughput spheroid-based assay for functional breast cancer precision medicine facilitated by deep learning.Communications medicine · 2026Article
- Label-Free Machine Learning Prediction of Chemotherapy on Tumor Spheroids Using a Microfluidics Droplet Platform.Small science · 2025Article
- High-Throughput Chemotherapeutic Drug Screening System for Gastric Cancer (Cure-GA).Annals of surgical oncology · 2025Article
- AI-driven 3D bioprinting for regenerative medicine: From bench to bedside.Bioactive materials · 2025Review
- Review
- Deep Learning for Predicting Spheroid Viability: Novel Convolutional Neural Network Model for Automating Quality Control for Three-Dimensional Bioprinting.Bioengineering (Basel, Switzerland) · 2025Article
- Article
- Non-small cell lung cancer organoids: Advances and challenges in current applications.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2024Article
- Automated Uniform Spheroid Generation Platform for High Throughput Drug Screening Process.Biosensors · 2024Article
- Vascularized organoid-on-a-chip: design, imaging, and analysis.Angiogenesis · 2024Review
- Optical coherence tomography for multicellular tumor spheroid category recognition and drug screening classification via multi-spatial-superficial-parameter and machine learning.Biomedical optics express · 2024Article
- Automated Nanodroplet Dispensing for Large-Scale Spheroid Generation via Hanging Drop and Parallelized Lossless Spheroid Harvesting.Micromachines · 2024Article
- High-throughput platform for label-free sorting of 3D spheroids using deep learning.Frontiers in bioengineering and biotechnology · 2024Article
- A multiparametric calcium signal screening platform using iPSC-derived cortical neural spheroids.SLAS discovery : advancing life sciences R & D · 2022Article
- Advances in and the Applicability of Machine Learning-Based Screening and Early Detection Approaches for Cancer: A Primer.Cancers · 2022Review
- Deep Learning-Assisted Nephrotoxicity Testing with Bioprinted Renal Spheroids.International journal of bioprinting · 2022Article
- Applications of machine learning in computer-aided drug discovery.QRB discovery · 2022Review
- From cell spheroids to vascularized cancer organoids: Microfluidic tumor-on-a-chip models for preclinical drug evaluations.Biomicrofluidics · 2021Review
- Recent advances in 3D models of tumor invasion.Current opinion in biomedical engineering · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The last two decades saw the establishment of three-dimensional (3D) cell cultures as an acknowledged tool to investigate cell behaviour in a tissue-like environment. Cells growing in spheroids differentiate and develop different characteristics in comparison to their two-dimensionally grown counterparts and are hence seen to exhibit a more in vivo-like phenotype. However, generating, treating and analysing spheroids in high quantities remains labour intensive and therefore limits its applicability in drugs and compound research. Here we present a fully automated pipetting robot that is able to (a) seed hanging drops from single cell suspensions, (b) treat the spheroids formed in these hanging drops with drugs and (c) analyse the viability of the spheroids by an image-based deep learning based convolutional neuronal network (CNN). The model is trained to classify between 'unaffected', 'mildly affected' and 'affected' spheroids after drug exposure. All corresponding spheroids are initially analysed by viability flow cytometry analysis to build a labelled training set for the CNN to subsequently reduce the number of misclassifications. Hence, this approach allows to efficiently examine the efficacy of drug combinatorics or new compounds in 3D cell culture. Additionally, it may provide a valuable instrument to screen for new and individualized systemic therapeutic strategies in second and third line treatment of solid malignancies using patient derived primary cells.
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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 Socratic graph.