In contemporary pathology, multiplexed immunofluorescence (mIF) and multiplex immunohistochemistry (mIHC) present both significant opportunities and challenges. These methodologies shed light on intricate tumor microenvironment interactions, emphasizing the need for intuitive visualization tools to analyze vast biological datasets effectively. As electronic health records (EHR) proliferate and physicians face increasing information overload, the integration of advanced technologies becomes imperative. SpatialVisVR emerges as a versatile VR platform tailored for comparing medical images, with adaptability for data privacy on embedded hardware. Clinicians can capture pathology slides in real-time via mobile devices, leveraging SpatialVisVR's deep learning algorithm to match and display similar mIF images. This interface supports the manipulation of up to 100 multiplexed protein channels, thereby assisting in immuno-oncology decision-making. Ultimately, SpatialVisVR aims to streamline diagnostic processes, advocating for a comprehensive and efficient approach to immuno-oncology research and treatment.
@article{arxiv.2401.02882,
title = {SpatialVisVR: An Immersive, Multiplexed Medical Image Viewer With Contextual Similar-Patient Search},
author = {Jai Prakash Veerla and Partha Sai Guttikonda and Amir Hajighasemi and Jillur Rahman Saurav and Aarti Darji and Cody T. Reynolds and Mohamed Mohamed and Mohammad S. Nasr and Helen H. Shang and Jacob M. Luber},
journal= {arXiv preprint arXiv:2401.02882},
year = {2024}
}