BrAIn: A comprehensive artificial intelligence-based morphology analysis system for brain organoids and neuroscience.
Overview
- İzmir Biomedicine and Genome Center, İzmir, Türkiye
- İzmir International Biomedicine and Genome Institute, Dokuz Eylül University, İzmir, Türkiye
- Department of Molecular Biology and Genetics, Faculty of Life and Natural Science, Abdullah Gül University, Kayseri, Türkiye
- Department of Computer Engineering, Faculty of Engineering, İzmir Institute of Technology, İzmir, Türkiye
- Department of Medical Biology and Genetics, Faculty of Medicine, Dokuz Eylül University, İzmir, Türkiye
Abstract
Human‐induced pluripotent stem cells (iPSCs) offer transformative potential for biomedical research, with iPSC‐derived organoids providing more physiologically relevant models than traditional 2D cell cultures. Among these, brain organoids (BO) are particularly valuable for drug screening, disease modeling, and investigations into molecular pathways. Accurate representation of brain morphology is critical, as more complex organoid structures better mimic the human brain. Deep learning (DL) and machine learning (ML) approaches have become integral to analyzing organoid morphology, yet tools for comprehensive, time‐resolved assessments are scarce. Here, we introduce BrAIn, a DL‐based application for analyzing the developmental progression of BOs. BrAIn tracks their evolution from embryoid bodies (EBs) and quantifies parameters including area, Feret diameter, perimeter, roundness, and circularity. It also classifies budding and abnormal morphologies of 3D organoids and detects monolayer neural rosette structures, key features of neuronal differentiation. Designed with accessibility in mind, BrAIn provides a no‐code interface, enabling researchers of all technical backgrounds to conduct advanced morphological analyses with ease. Our study demonstrates the application of BrAIn to evaluate the effects of different growth conditions—static, orbital shaker, and microfluidic chip‐based—on BO development. Orbital shaker cultures resulted in the largest organoids, while chip‐based systems achieved more homogeneous growth. Both conditions produced organoids with greater morphological complexity compared to static culture. BrAIn emerges as a robust, user‐friendly tool to quantify BO development and explore how versatile growth conditions influence their morphology and maturation.
Reproduced under the paper's license (CC BY), from the paper cited above.
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Data
Datasets cited
- zenodo:15513127, at Zenodo; found in “DATA AVAILABILITY STATEMENT”
Data availability statement
The data that support the findings of this study are openly available in Brain Organoid Dataset at https://
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
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Version 1, 30 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 5 keywords, 2 funders, 80 references.
Cite
This paper
Kahveci, B., Polatli, E., Evranos, A. E., Güner, H., Karakülah, G., Bastanlar, Y., & Güven, S. (2026). BrAIn: A comprehensive artificial intelligence-based morphology analysis system for brain organoids and neuroscience. Bioengineering & translational medicine, 11(3), e70123. https://
BibTeX
@article{kahveci2026brai
author = {Kahveci, Burak and Polatli, Elifsu and Evranos, Ali Eren and Güner, Hüseyin and Karakülah, Gökhan and Bastanlar, Yalin and Güven, Sinan},
title = {{BrAIn: A comprehensive artificial intelligence-based morphology analysis system for brain organoids and neuroscience}},
journal = {Bioengineering \& translational medicine},
year = {2026},
month = mar,
volume = {11},
number = {3},
pages = {e70123},
publisher = {Wiley},
issn = {2380-6761},
doi = {10.1002/
url = {https://
pmid = {42272957},
pmcid = {PMC13247413}
}
RIS
TY - JOUR
AU - Kahveci, Burak
AU - Polatli, Elifsu
AU - Evranos, Ali Eren
AU - Güner, Hüseyin
AU - Karakülah, Gökhan
AU - Bastanlar, Yalin
AU - Güven, Sinan
TI - BrAIn: A comprehensive artificial intelligence-based morphology analysis system for brain organoids and neuroscience
T2 - Bioengineering & translational medicine
J2 - Bioeng Transl Med
PY - 2026
DA - 2026/
VL - 11
IS - 3
SP - e70123
SN - 2380-6761
PB - Wiley
DO - 10.1002/
UR - https://
LA - en
ER -
CSL-JSON
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