Evidence map›Paper›PMID 41762954›Full record

ArticleCytotherapy2026

Foundation model cascades enable zero-shot microscopy image analysis for cell therapy manufacturing.

Rui Qi Chen, Yeonju Lee, Benjamin Joffe, Caroline E Serafini, Paloma Casteleiro Costa, Bryan Wang, Bharat Kanwar, Stephen Balakirsky, Aaron D Silva Trenkle, Linda E Kippner and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cytotherapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Rui Qi ChenH. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.
Yeonju LeeH. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.
Benjamin JoffeGeorgia Tech Research Institute, Georgia Institute of Technology, Atlanta, Georgia, USA.
Caroline E SerafiniWallace H. Coulter Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, Georgia, USA.
Paloma Casteleiro CostaWallace H. Coulter Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, Georgia, USA.
Bryan WangWallace H. Coulter Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, Georgia, USA.
Bharat KanwarGeorgia Tech Research Institute, Georgia Institute of Technology, Atlanta, Georgia, USA.
Stephen BalakirskyGeorgia Tech Research Institute, Georgia Institute of Technology, Atlanta, Georgia, USA.
Aaron D Silva TrenkleWallace H. Coulter Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, Georgia, USA.
Linda E KippnerThe Marcus Center of Excellence for Biomanufacturing, Georgia Institute of Technology, Atlanta, Georgia, USA; The Parker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia, USA.
Isaac LeCompteWallace H. Coulter Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, Georgia, USA.
Ye LiThe Marcus Center of Excellence for Biomanufacturing, Georgia Institute of Technology, Atlanta, Georgia, USA; The Parker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia, USA.
Christine E BrownDepartment of Hematology & Hematopoietic Cell Transplantation (T Cell Therapeutics Research Laboratories), City of Hope Beckman Research Institute and Medical Center, Duarte, California, USA.
Gabriel A KwongWallace H. Coulter Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, Georgia, USA.
Francisco E RoblesWallace H. Coulter Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, Georgia, USA.
Krishnendu RoySchool of Engineering, Vanderbilt University, Nashville, Tennessee, USA.
Jing LiH. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA. Electronic address: jli3175@gatech.edu.

Funding

T32 CTEng (Cellular and Tissue Engineering) Training ProgramT32GM145735 · NIGMS · GEORGIA INSTITUTE OF TECHNOLOGY · PI Edward A. Botchwey, Andres J Garcia · 2022 to 2026
$2.3M
DNA-gated cytometry for multiplexed sorting of antigen-specific CD8 T cellsR01AI171892 · NIAID · GEORGIA INSTITUTE OF TECHNOLOGY · PI KWONG, GABRIEL A · 2022 to 2025
$2.0M
Accessible label-free optical microscopy with quantitative molecular and functional contrastR35GM147437 · NIGMS · ST. JUDE CHILDREN'S RESEARCH HOSPITAL · PI Francisco E Robles · 2022 to 2026
$1.9M
NIAID NIH HHS R01 AI171892NIGMS NIH HHS R35 GM147437NIGMS NIH HHS T32 GM145735
6 · The paper itself

Abstract

BACKGROUND

aimsThe scalable manufacturing of cell therapies creates a significant need for robust process analytical technologies, where automated analysis of noninvasive microscopy images offers a powerful method for monitoring critical quality attributes. However, conventional machine-learning models are often bottlenecked by extensive data labeling and poor generalizability across different batch effects. To overcome these limitations, we introduce a foundational model cascade for the zero-shot analysis of microscopy images.

methodsIn the first stage, a multimodal large language model (LLM) detects anomalies, and anomalous images immediately trigger an alert. Otherwise, the segment anything model performs exhaustive instance segmentation, and the detected objects are classified by the LLM to estimate cell counts and viability.

resultsThis unified, zero-shot approach delivers robust anomaly detection together with quantitative measures of cell count and health, without any task-specific fine-tuning.

conclusionsBy combining pre-trained foundation models in a complementary cascade, our method provides a generalizable solution for real-time process monitoring and feedback control, paving the way for more scalable and automated cell therapy manufacturing.

Indexed as

Cell- and Tissue-Based TherapyImage Processing, Computer-AssistedMicroscopyCell CountHumansLarge Language Modelsanomaly detectioncell countingcell therapy manufacturingcell viability estimationfoundation modelslarge language modelsmicroscopy image analysis

Identifiers

PMID41762954
PMCPMC13325518

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Registered trials

None linked

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.