Document Type
Article
Publication Date
8-24-2023
Abstract
BACKGROUND AND PURPOSE: Traumatic brain injury (TBI) can cause progressive neuropathology that leads to chronic impairments, creating a need for biomarkers to detect and monitor this condition to improve outcomes. This study aimed to analyze the ability of data-driven analysis of diffusion tensor imaging (DTI) and neurite orientation dispersion imaging (NODDI) to develop biomarkers to infer symptom severity and determine whether they outperform conventional T1-weighted imaging.
MATERIALS AND METHODS: A machine learning-based model was developed using a dataset of hybrid diffusion imaging of patients with chronic traumatic brain injury. We first extracted the useful features from the hybrid diffusion imaging (HYDI) data and then used supervised learning algorithms to classify the outcome of TBI. We developed three models based on DTI, NODDI, and T1-weighted imaging, and we compared the accuracy results across different models.
RESULTS: Compared with the conventional T1-weighted imaging-based classification with an accuracy of 51.7-56.8%, our machine learning-based models achieved significantly better results with DTI-based models at 58.7-73.0% accuracy and NODDI with an accuracy of 64.0-72.3%.
CONCLUSION: The machine learning-based feature selection and classification algorithm based on hybrid diffusion features significantly outperform conventional T1-weighted imaging. The results suggest that advanced algorithms can be developed for inferring symptoms of chronic brain injury using feature selection and diffusion-weighted imaging.
Recommended Citation
Muller, Jennifer; Wang, Ruixuan; Middleton, Devon; Alizadeh, Mahdi; Kang, KiChang; Hryczyk, Ryan; Zabrecky, George; Hriso, Chloe; Navarreto, Emily; Wintering, Nancy; Bazzan, Anthony J.; Wu, Chengyuan; Monti, Daniel A.; Jiao, Xun; Wu, Qianhong; Newberg, Andrew B.; and Mohamed, Feroze, "Machine Learning-Based Classification of Chronic Traumatic Brain Injury Using Hybrid Diffusion Imaging" (2023). Marcus Institute of Integrative Health Faculty Papers. Paper 28.
https://jdc.jefferson.edu/jmbcimfp/28
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
PubMed ID
37694125
Language
English
Included in
Artificial Intelligence and Robotics Commons, Integrative Medicine Commons, Other Medicine and Health Sciences Commons, Trauma Commons
Comments
This article, first published by Frontiers Media, is the author's final published version in Frontiers in Neuroscience, Volume 17, 2023, Article number 1182509.
The published version is available at https://doi.org/10.3389/fnins.2023.1182509.
Copyright © 2021 by the authors.Licensee MDPI, Basel, Switzerland.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).