e.g. mhealth
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Head MRI is a widely used diagnostic method for assessing dementia and Alzheimer disease (AD), as it offers intricate representations of the brain’s anatomy and physiology. In clinical practice, MRI is often combined with other imaging techniques and cognitive assessments to support the diagnosis of these conditions. The utilization of MRI has seen an upward trend in recent times, as it has become an instrumental tool for the early detection and tracking of the evolution of dementia and AD.
JMIR Aging 2025;8:e63686
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Digitally assisted consent also offers potential advantages in other research domains for informing participants of study procedures and potential risks such as neuroimaging methods (eg, magnetic resonance imaging [MRI]). Although MRI is largely a low-risk, noninvasive procedure that substitutes magnetic fields for high-energy radiation, misconceptions about its use abound, including false beliefs that MRI exposes participants to radiation [18].
JMIR Form Res 2025;9:e59954
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During the MRI examination, children can sometimes watch a movie in the bore of the MRI scanner (eg, using movie goggles or a screen), play a digital game [18,19], or enjoy a child-friendly imaging room (for a review of different approaches, see [20]).
Next to initiatives that focus on one specific touchpoint in a child’s diagnostic pathway (such as interventions at home or in the hospital or the MRI room), there are multifaceted concepts that cover the entire pathway from home to examination.
JMIR Serious Games 2025;13:e55720
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In particular, the use of MRI (magnetic resonance imaging) is limited to basic protocols, typically consisting of T1-weighted sequences, primarily focused on the quantification of muscle volume and CSA [13,15,19,21-24] thus not fully exploiting the strength and versatility of noninvasive MRI techniques in capturing other crucial aspects such as fat infiltration, tissue inflammation, and microstructural changes.
JMIR Rehabil Assist Technol 2025;12:e64825
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Convolutional neural networks (CNNs) are used in the analysis of AD image data in the form of MRI [33], PET [34], and CT scans [35]. CNNs can automatically extract relevant features from complex imaging data and learn hierarchical representations of subtle AD patterns.
Advanced techniques like Gradient-Weighted Class Activation Mapping after CNN model training highlight important regions of the input MRI brain image [36,37].
JMIR Aging 2024;7:e59370
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Response: The 5 participants that were only scanned with magnetic resonance imaging (MRI) once could not be used for this analysis to compare the change across time in the SCG. Thank you for suggesting to add a table—we added the flowchart showing how many scans were done at each session.
4. Head motion: could a quantitative comparison be made between the amount of head motion in the people with Parkinson disease group compared to controls?
JMIRx Med 2024;5:e67815
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Please move all the descriptions of methods into the Methods section and please organize this more logically into behavioral methods (acquisition and analysis) and MRI measures (acquisition, preprocessing, and analysis). No statistical methods are described in the Statistical Analysis section. Please describe here what statistical tests you used (t tests or analysis of covariance?). You state that “no significant interaction was found between experience and GDS”—what test was used?
JMIRx Med 2024;5:e67811
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Virtual Reality in Clinical Teaching and Diagnostics for Liver Surgery: Prospective Cohort Study
MRI: magnetic resonance imaging; VR: virtual reality. *indicates statistical significance between the groups.
The multifactorial 2-way repeated measures ANOVA showed a significant difference in error rates between the MRI and the VR condition (F1,59=314.376; P
The analysis of the processing time showed an average processing time using the MRI diagnostics of 16.25 (SD 1.25) minutes. With the additional use of VR, the average value was 11.45 (SD 2.26) minutes (P=.001; Figure 3).
JMIR XR Spatial Comput 2024;1:e60383
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