# Using Deep Learning to Predict Induced Pluripotent Stem Cell-Derived Cardiomyocyte (iPSC-CM) Differentiation Outcomes

> **NIH NIH F30** · STANFORD UNIVERSITY · 2022 · $39,523

## Abstract

ABSTRACT
Human induced pluripotent stem cell derived cardiomyocytes (iPSC-CMs) provide transformative new avenues
to combat heart diseases. They have been used extensively to model disease mechanisms and predict drug
responses. Despite significant advancements in iPSC-CMs differentiation, differentiation outcomes still vary
across batches, cell lines, and protocols, resulting in significant experimental variability. As a result,
characterizing differentiation outcomes is essential. The current standards to characterize iPSC-CM
differentiation outcomes involve monitoring for functional or genetic attributes of cardiomyocytes during the
differentiation. However these processes are prohibitively time consuming, imprecise, or expensive. Discovering
earlier time points and scalable, accurate markers that can determine differentiation outcomes is critical for
eliminating a severe bottleneck in cardiomyocyte differentiation and creating better iPSC-CM materials. Given
the rising importance of iPSC-CMs in cardiovascular research, advances in iPSC-CM differentiation would widely
accelerate the search for cures.
Here, I propose leveraging Artificial Intelligence (AI), deep learning and computer vision to develop scalable,
accurate methods for characterizing and predicting iPSC-CM differentiation outcomes. To achieve this, I will first
use deep learning models to identify markers and time points that can be used to predict and determine
differentiation outcomes. I have differentiated iPSC-CMs and obtained a dataset of images at each day of
differentiation that are labeled with their final differentiation outcome. I will analyze the dataset using a deep
learning model—an image classifier—to determine the earliest time point that can be used to predict
differentiation outcomes. I will then correlate results with transcriptomic data to gain mechanistic insight. To
evaluate whether deep learning methods are scalable and accurate models for predicting differentiation
outcomes, I will differentiate genetically diverse iPSCs lines to create an additional dataset to fine tune the model
and then evaluate the model’s potential for scalability. To validate the accuracy of the model, I will first verify that
the model’s predictions align with conventional functional and genetic markers of differentiated cardiomyocytes.
Then, I will functionally, morphologically, and genetically compare predictions made by the model against existing
methods for evaluating differentiating outcomes.
Completion of this proposal will eliminate a main bottleneck in iPSC-CM differentiation; create an extensive iPSC-
CM differentiation ‘morphology atlas’; and accelerate the application of AI and deep learning to iPSC-CMs.
Additionally, the outlined training will provide me with the computational and regenerative medicine expertise
required to later succeed as an independent investigator and physician scientist.

## Key facts

- **NIH application ID:** 10540303
- **Project number:** 5F30HL156478-02
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Angela Zhang
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $39,523
- **Award type:** 5
- **Project period:** 2021-06-14 → 2024-06-13

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10540303

## Citation

> US National Institutes of Health, RePORTER application 10540303, Using Deep Learning to Predict Induced Pluripotent Stem Cell-Derived Cardiomyocyte (iPSC-CM) Differentiation Outcomes (5F30HL156478-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10540303. Licensed CC0.

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