Objective: For patients with nonlesional refractory focal epilepsy (NLRFE), localization of the epileptogenic zone may be more arduous than for other types of epilepsy and frequently requires information from multiple noninvasive presurgical modalities and intracranial EEG (icEEG). In this prospective, blinded study, the authors assessed the clinical added value of magnetic source imaging (MSI) in the presurgical evaluation of patients with NLRFE.
Methods: This study prospectively included 57 consecutive patients with NLRFE who were considered for epilepsy surgery. All patients underwent noninvasive presurgical evaluation and then MSI. To determine the surgical plan, discussion of the results of the presurgical evaluation was first undertaken while discussion participants were blinded to the MSI results. MSI results were then presented. MSI influence on the initial management plan was assessed.
Results: MSI results influenced patient management in 32 patients. MSI results led to the following changes in surgical strategy in 14 patients (25%): allowing direct surgery in 6 patients through facilitating the detection of subtle cortical dysplasia in 4 patients and providing additional concordant diagnostic information to other presurgical workup in another 2 patients; rejection of surgery in 3 patients originally deemed surgical candidates; change of plan from direct surgery to icEEG in 2 patients; and allowing icEEG in 3 patients deemed not surgical candidates. MSI results led to changed electrode locations and contact numbers in another 18 patients. Epilepsy surgery was performed in 26 patients influenced by MSI results and good surgical outcome was achieved in 21 patients.
Conclusions: This prospective, blinded study showed that information provided by MSI allows more informed icEEG planning and surgical outcome in a significant percentage of patients with NLRFE and should be included in the presurgical workup in those patients.
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http://dx.doi.org/10.3171/2020.1.FOCUS19877 | DOI Listing |
JMIR Form Res
January 2025
Larner College of Medicine, University of Vermont, Burlington, VT, United States.
Background: Social media has become a widely used way for people to share opinions about health care and medical topics. Social media data can be leveraged to understand patient concerns and provide insight into why patients may turn to the internet instead of the health care system for health advice.
Objective: This study aimed to develop a method to investigate Reddit posts discussing health-related conditions.
JMIR Form Res
January 2025
Northwestern Medicine, Chicago, IL, United States.
Background: Patient recruitment and data management are laborious, resource-intensive aspects of clinical research that often dictate whether the successful completion of studies is possible. Technological advances present opportunities for streamlining these processes, thus improving completion rates for clinical research studies.
Objective: This paper aims to demonstrate how technological adjuncts can enhance clinical research processes via automation and digital integration.
JMIR Serious Games
January 2025
School of Computing, Engineering and Mathematical Sciences, Optus Chair Digital Health, La Trobe University, Melbourne, Australia.
Background: This review explores virtual reality (VR) and exercise simulator-based interventions for individuals with attention-deficit/hyperactivity disorder (ADHD). Past research indicates that both VR and simulator-based interventions enhance cognitive functions, such as executive function and memory, though their impacts on attention vary.
Objective: This study aimed to contribute to the ongoing scientific discourse on integrating technology-driven interventions into the management and evaluation of ADHD.
JMIR AI
January 2025
Department of Information Systems and Business Analytics, Iowa State University, Ames, IA, United States.
Background: In the contemporary realm of health care, laboratory tests stand as cornerstone components, driving the advancement of precision medicine. These tests offer intricate insights into a variety of medical conditions, thereby facilitating diagnosis, prognosis, and treatments. However, the accessibility of certain tests is hindered by factors such as high costs, a shortage of specialized personnel, or geographic disparities, posing obstacles to achieving equitable health care.
View Article and Find Full Text PDFJMIR Med Inform
January 2025
Department of Systems Design Engineering, Faculty of Engineering, University of Waterloo, Waterloo, ON, Canada.
Background: While expert optometrists tend to rely on a deep understanding of the disease and intuitive pattern recognition, those with less experience may depend more on extensive data, comparisons, and external guidance. Understanding these variations is important for developing artificial intelligence (AI) systems that can effectively support optometrists with varying degrees of experience and minimize decision inconsistencies.
Objective: The main objective of this study is to identify and analyze the variations in diagnostic decision-making approaches between novice and expert optometrists.
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