Interictal FDG-PET (iPET) is a core tool for localizing the epileptogenic focus, potentially before structural MRI, that does not require rare and transient epileptiform discharges or seizures on EEG. The visual interpretation of iPET is challenging and requires years of epilepsy-specific expertise. We have developed an automated computer-aided diagnostic (CAD) tool that has the potential to work both independent of and synergistically with expert analysis. Our tool operates on distributed metabolic changes across the whole brain measured by iPET to both diagnose and lateralize temporal lobe epilepsy (TLE). When diagnosing left TLE (LTLE) or right TLE (RTLE) vs. non-epileptic seizures (NES), our accuracy in reproducing the results of the gold standard long term video-EEG monitoring was 82% [95% confidence interval (CI) 69-90%] or 88% (95% CI 76-94%), respectively. The classifier that both diagnosed and lateralized the disease had overall accuracy of 76% (95% CI 66-84%), where 89% (95% CI 77-96%) of patients correctly identified with epilepsy were correctly lateralized. When identifying LTLE, our CAD tool utilized metabolic changes across the entire brain. By contrast, only temporal regions and the right frontal lobe cortex, were needed to identify RTLE accurately, a finding consistent with clinical observations and indicative of a potential pathophysiological difference between RTLE and LTLE. The goal of CADs is to complement - not replace - expert analysis. In our dataset, the accuracy of manual analysis (MA) of iPET (∼80%) was similar to CAD. The square correlation between our CAD tool and MA, however, was only 30%, indicating that our CAD tool does not recreate MA. The addition of clinical information to our CAD, however, did not substantively change performance. These results suggest that automated analysis might provide clinically valuable information to focus treatment more effectively.
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http://dx.doi.org/10.3389/fneur.2013.00031 | DOI Listing |
Sci Prog
January 2025
Yazd Cardiovascular Research Center, Non-communicable Diseases Research Institute, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
Objective: Coronary artery disease (CAD) remains a significant global health burden, characterized by the narrowing or blockage of coronary arteries. Treatment decisions are often guided by angiography-based scoring systems, such as the Synergy Between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery (SYNTAX) and Gensini scores, although these require invasive procedures. This study explores the potential of electrocardiography (ECG) as a noninvasive diagnostic tool for predicting CAD severity, alongside traditional risk factors.
View Article and Find Full Text PDFBMC Med Imaging
January 2025
Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
Problem: Breast cancer is a leading cause of death among women, and early detection is crucial for improving survival rates. The manual breast cancer diagnosis utilizes more time and is subjective. Also, the previous CAD models mostly depend on manmade visual details that are complex to generalize across ultrasound images utilizing distinct techniques.
View Article and Find Full Text PDFCad Saude Publica
January 2025
Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brasil.
Syndromic surveillance using primary health care (PHC) data is a valuable tool for early outbreak detection, as demonstrated by the potential to identify COVID-19 outbreaks. However, the potential of such an early warning system in the post-COVID-19 era remains largely unexplored. We analyzed PHC encounter counter of respiratory complaints registered in the database of the Brazilian Unified National Health System from October 2022 to July 2023.
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January 2025
State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Department of Cardiology, Clinical Medical Research Institute, First Affiliated Hospital of Xinjiang Medical University, Xinjiang Medical University, 137 Liyushan Road, Urumqi, 830011, China.
The present study was aimed to investigate whether Gensini score or SYNTAX score was a valuable tool to predict in-stent restenosis (ISR) in coronary artery disease (CAD) patients with drug-eluting stents (DES) implantation. A retrospective case-control study and a validating retrospective cohort study were designed. All subjects' information was collected from the First Affiliated Hospital of Xinjiang Medical University.
View Article and Find Full Text PDFCoron Artery Dis
November 2024
Department of Cardiology, Bülent Ecevit University Faculty of Medicine, Zonguldak, Turkey.
Background: Non-ST-segment elevation acute coronary syndrome (NSTE-ACS) has a significant impact on cardiovascular mortality in elderly patients. Identification of high-risk patients is essential to optimize clinical management. This study investigates the relationship between the TyG index and CAD complexity, as measured by the SYNTAX score, in elderly patients with NSTE-ACS.
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