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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Review

AI-Enhanced Morphological Phenotyping in Humanized Mouse Models: A Transformative Approach to Infectious Disease Research

by
Asim Muhammad
†,
Xin-Yu Zheng
†,
Hui-Lin Gan
,
Yu-Xin Guo
,
Jia-Hong Xie
,
Yan-Jun Chen
and
Jin-Jun Chen
*
Department of Veterinary Medicine, School of Coastal Agriculture, Guangdong Ocean University, Zhanjiang 524088, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Biophysica 2025, 5(4), 43; https://doi.org/10.3390/biophysica5040043
Submission received: 9 August 2025 / Revised: 14 September 2025 / Accepted: 17 September 2025 / Published: 24 September 2025
(This article belongs to the Special Issue Advances in Computational Biophysics)
Versions Notes

Abstract

Humanized mouse models offer human-specific platforms for investigating complex host–pathogen interactions, addressing shortcomings of conventional preclinical models that often fail to replicate human immune responses accurately. This integrative review examines the intersection of advanced morphological phenotyping and artificial intelligence (AI) to enhance predictive capacity and translational relevance in infectious disease research. A structured literature search was conducted across PubMed, Scopus, and Web of Science (2010–2025), applying defined inclusion and exclusion criteria. Evidence synthesis highlights imaging modalities, AI-driven phenotyping, and standardization strategies, supported by comparative analyses and quality considerations. Persistent challenges include variability in engraftment, lack of harmonized scoring systems, and ethical governance. We propose recommendations for standardized protocols, risk-of-bias mitigation, and collaborative training frameworks to accelerate adoption of these technologies in translational medicine.
Keywords: humanized mouse models; morphological phenotyping; artificial intelligence; infectious diseases; translational research; biomarkers humanized mouse models; morphological phenotyping; artificial intelligence; infectious diseases; translational research; biomarkers

Share and Cite

MDPI and ACS Style

Muhammad, A.; Zheng, X.-Y.; Gan, H.-L.; Guo, Y.-X.; Xie, J.-H.; Chen, Y.-J.; Chen, J.-J. AI-Enhanced Morphological Phenotyping in Humanized Mouse Models: A Transformative Approach to Infectious Disease Research. Biophysica 2025, 5, 43. https://doi.org/10.3390/biophysica5040043

AMA Style

Muhammad A, Zheng X-Y, Gan H-L, Guo Y-X, Xie J-H, Chen Y-J, Chen J-J. AI-Enhanced Morphological Phenotyping in Humanized Mouse Models: A Transformative Approach to Infectious Disease Research. Biophysica. 2025; 5(4):43. https://doi.org/10.3390/biophysica5040043

Chicago/Turabian Style

Muhammad, Asim, Xin-Yu Zheng, Hui-Lin Gan, Yu-Xin Guo, Jia-Hong Xie, Yan-Jun Chen, and Jin-Jun Chen. 2025. "AI-Enhanced Morphological Phenotyping in Humanized Mouse Models: A Transformative Approach to Infectious Disease Research" Biophysica 5, no. 4: 43. https://doi.org/10.3390/biophysica5040043

APA Style

Muhammad, A., Zheng, X.-Y., Gan, H.-L., Guo, Y.-X., Xie, J.-H., Chen, Y.-J., & Chen, J.-J. (2025). AI-Enhanced Morphological Phenotyping in Humanized Mouse Models: A Transformative Approach to Infectious Disease Research. Biophysica, 5(4), 43. https://doi.org/10.3390/biophysica5040043

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