scholarly journals Deep Learning-Based Positron Emission Tomography Molecular Imaging in the Assessment of Cognitive Dysfunction in Patients with Epilepsy

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Mayila Tuerxun ◽  
Lixin Yin ◽  
Huiqun Chen ◽  
Jingqian Lin
Keyword(s):  
Quality Of Life ◽  
Positron Emission Tomography ◽  
Deep Learning ◽  
Molecular Imaging ◽  
Cognitive Dysfunction ◽  
Emission Tomography ◽  
Control Group ◽  
Positron Emission ◽  
Patients With Epilepsy

This work aimed to investigate the application of positron emission tomography (PET) molecular imaging based on the deep learning algorithm in the assessment of cognitive dysfunction in patients with epilepsy. In this study, 52 hospitalized patients with epilepsy were selected as the epilepsy group and treated with different kinds of antiepileptic drugs, and 52 volunteers were selected as the control group. A U-net optimized network structure algorithm based on deep learning was proposed in this study and compared with a fully convolutional neural network (FCNN). Besides, it was applied in the PET molecular imaging of patients with epilepsy, and the segmentation effect of the U-net optimized network structure was good. According to event-related potential examinations, the proportion of patients with cognitive dysfunction in the epilepsy group (74.19%) was higher than the proportion of the control group (7.46%) ( P < 0.05 ). The patients with cognitive dysfunction (57.89%) who took one antiepileptic drug were lower than those with two antiepileptic drugs (84.61%) ( P < 0.05 ). The difference was statistically obvious in the overall quality of life of patients with epilepsy ( P < 0.05 ). The occurrence of cognitive dysfunction in patients with epilepsy was related to the type of seizures. In addition, the quality of life of patients who suffered from cognitive dysfunction was low.

scholarly journals Quantitative Molecular Positron Emission Tomography Imaging Using Advanced Deep Learning Techniques

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Habib Zaidi ◽  
Issam El Naqa
Keyword(s):  
Positron Emission Tomography ◽  
Deep Learning ◽  
Molecular Imaging ◽  
Multimodality Imaging ◽  
Positron Emission Tomography Imaging ◽  
Tracer Uptake ◽  
Emission Tomography ◽  
Annual Review ◽  
Publication Date ◽  
Positron Emission

The widespread availability of high-performance computing and the popularity of artificial intelligence (AI) with machine learning and deep learning (ML/DL) algorithms at the helm have stimulated the development of many applications involving the use of AI-based techniques in molecular imaging research. Applications reported in the literature encompass various areas, including innovative design concepts in positron emission tomography (PET) instrumentation, quantitative image reconstruction and analysis techniques, computer-aided detection and diagnosis, as well as modeling and prediction of outcomes. This review reflects the tremendous interest in quantitative molecular imaging using ML/DL techniques during the past decade, ranging from the basic principles of ML/DL techniques to the various steps required for obtaining quantitatively accurate PET data, including algorithms used to denoise or correct for physical degrading factors as well as to quantify tracer uptake and metabolic tumor volume for treatment monitoring or radiation therapy treatment planning and response prediction. This review also addresses future opportunities and current challenges facing the adoption of ML/DL approaches and their role in multimodality imaging. Expected final online publication date for the Annual Review of Biomedical Engineering, Volume 23 is June 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Cognitive impairments in patients with treatment resistant epilepsy and complex rehabilitation

2021 ◽  
Vol 8 (2) ◽  
pp. 123-129
Author(s):  
Volodymyr Korostiy ◽  
Iryna Blazhina
Keyword(s):  
Quality Of Life ◽  
Cognitive Decline ◽  
Cognitive Training ◽  
Affective Disorders ◽  
Depressive Disorders ◽  
Cognitive Impairments ◽  
Control Group ◽  
Quality Of Life Scale ◽  
Patients With Epilepsy

Background. The study of features of comorbid pathology in patients with epilepsy is of particular interest due to the high prevalence of this pathology and a significant impact on the quality of life of patients and their social adaptation. Aim. The aim of the research was to detect versatile cognitive impairments and affective disorders in epilepsy, and to study the results of cognitive training and psychoeducation. Materials and methods. The theoretical analysis of modern scientific researches in the field of cognitive and affective impairments during epilepsy was carried out. We studied the features of clinical and psychopathological manifestations in patients, suffering from epilepsy. The study covered 146patients (85 men and 61 women) who were in inpatient care. The following psychodiagnostic techniques were used: the MOCA test, the Toronto Cognitive Assessment (TorCA), the MiniMult test, the Münsterberg test, the quality of life scale, the Hamilton scale of depression and anxiety. Results. This publication offers the results of a study of cognitive and affective disorders the quality of life in patients who suffer from epilepsy and the results of online cognitive training and psychoeducation. We found cognitive decline in 88% of patients with epilepsy and improvement of cognitive functions by methods of non-pharmacological correction. Conclusions. Affective and cognitive disorders significantly affects the quality of life of patients, their ability to work and socialization. The conducted research showed that compared to the control group of healthy persons, patients with epilepsy showed improvement in their cognitive decline, anxiety and depressive disorders. Cognitive online training appeared to be effective for the patients with epilepsy.

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