Aja-Fernandez, Santiago and Martin-Martin, Carmen and Planchuelo-Gomez, Alvaro and Faiyaz, Abrar and Uddin, Md Nasir and Schifitto, Giovanni and Tiwari, Abhishek and Shigwan, Saurabh J. and Singh, Rajeev Kumar and Zheng, Tianshu and Cao, Zuozhen and Wu, Dan and Blumberg, Stefano B. and Sen, Snigdha and Goodwin-Allcock, Tobias and Slator, Paddy J. and Avci, Mehmet Yigit and Li, Zihan and Bilgic, Berkin and Tian, Qiyuan and Wang, Xinyi and Tang, Zihao and Cabezas, Mariano and Rauland, Amelie and Merhof, Dorit and Maria, Renata Manzano and Campos, Vinicius Paraniba and Santini, Tales and Vieira, Marcelo Andrade da Costa and Hashemizadehkolowri, Seyyedkazem and Dibella, Edward and Peng, Chenxu and Shen, Zhimin and Chen, Zan and Ullah, Irfan and Mani, Merry and Abdolmotalleby, Hesam and Eckstrom, Samuel and Baete, Steven H. and Filipiak, Patryk and Dong, Tanxin and Fan, Qiuyun and de Luis-Garcia, Rodrigo and Tristan-Vega, Antonio and Pieciak, Tomasz (2023) Validation of deep learning techniques for quality augmentation in diffusion MRI for clinical studies. NEUROIMAGE-CLINICAL, 39: 103483. ISSN 2213-1582,
Full text not available from this repository. (Request a copy)Abstract
The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | TENSOR; NETWORK; PROPAGATION; UNCERTAINTY; ANISOTROPY; SCHEMES; MODEL; Deep learning; Machine learning; Artificial intelligence; Diffusion MRI; Angular resolution; Diffusion tensor |
| Subjects: | 000 Computer science, information & general works > 004 Computer science |
| Divisions: | Informatics and Data Science |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 22 Apr 2024 13:25 |
| Last Modified: | 22 Apr 2024 13:25 |
| URI: | https://pred.uni-regensburg.de/id/eprint/62943 |
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