Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression

Shoeibi, Afshin; Ghassemi, Navid; Khodatars, Marjane; Moridian, Parisa; Khosravi, Abbas; Zare, Assef; Górriz, Juan Manuel; Hossein, Chale-Chale, Amir; Khadem, Ali; Acharya, U. Rajendra

doi:10.1007/s11571-022-09897-w

Cited by 37 publications

(7 citation statements)

References 127 publications

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“…Methods of explanation aid in eliminating the process of difficulty in understanding the deep learning model and providing reasons behind the machine prediction. Deep learning model interpretation and explainability work at various levels during the model development phase or after development; it can also be used at the algorithmic or model level, and it can be used to represent classification problems, predictive systems, or clinical decision support systems [68]. XAI assists humans in comprehending deep learning models, which is critical in healthcare.…”

Section: Concepts Of Explainable Artificial Intelligencementioning

confidence: 99%

A Comprehensive Survey of ArtificialIntelligence in Precision Healthcare:Shedding Light on Interpretability

MK,

KS,

Buhari

2024

Preprint

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Trust levels of the popularity of deep learning models is enhanced throughXAI, which provides explanations and interpretations on the decisionsmade and also in making more interpretable decisions and explainableoutcomes. Recently, there has been a surge of interest in developingXAI models in medical fields such as oncology, ophthalmology pedi-atrics and orthopedics. In particular XAI is also advancing rapidly inpredicting psychological health conditions such as depression, stress, anxiety, ADHD, schizophrenia, neuropsychiatric problems and many others.The primary objective of this article is to review the recent findingsof the XAI models that are used for explanations for different diseaseswhich uses deep learning models with various forms of data. Finally,the focus is made on few issues and possibilities in medicine XAI.

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Section: Concepts Of Explainable Artificial Intelligencementioning

confidence: 99%

A Comprehensive Survey of ArtificialIntelligence in Precision Healthcare:Shedding Light on Interpretability

MK,

KS,

Buhari

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…SZ is a persistent mental illness marked by abnormal perceptions, actions, and thoughts. Deep learning algorithms have been used to identify useful characteristics and patterns suggestive of SZ from multimodal neuroimaging data, including fMRI, diffusion tensor imaging (DTI), and electroencephalography (EEG) [ 35 ]. These models have proven to be effective at differentiating between healthy people and people with SZ, facilitating early detection and individualized treatment plans.…”

Section: Related Workmentioning

confidence: 99%

“…For instance, utilizing resting-state functional MRI (rs-fMRI) data, Shoeibi et al [ 35 ] present a new DL strategy for the intelligent detection of schizophrenia (SZ) and attention deficit hyperactivity disorder (ADHD). In their method, the data are preprocessed, features are extracted using a convolutional autoencoder model, and interval type-2 fuzzy regression (IT2FR) using optimization approaches is used.…”

Section: Related Workmentioning

confidence: 99%

An Approach to Binary Classification of Alzheimer’s Disease Using LSTM

et al. 2023

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In this study, we use LSTM (Long-Short-Term-Memory) networks to evaluate Magnetic Resonance Imaging (MRI) data to overcome the shortcomings of conventional Alzheimer’s disease (AD) detection techniques. Our method offers greater reliability and accuracy in predicting the possibility of AD, in contrast to cognitive testing and brain structure analyses. We used an MRI dataset that we downloaded from the Kaggle source to train our LSTM network. Utilizing the temporal memory characteristics of LSTMs, the network was created to efficiently capture and evaluate the sequential patterns inherent in MRI scans. Our model scored a remarkable AUC of 0.97 and an accuracy of 98.62%. During the training process, we used Stratified Shuffle-Split Cross Validation to make sure that our findings were reliable and generalizable. Our study adds significantly to the body of knowledge by demonstrating the potential of LSTM networks in the specific field of AD prediction and extending the variety of methods investigated for image classification in AD research. We have also designed a user-friendly Web-based application to help with the accessibility of our developed model, bridging the gap between research and actual deployment.

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“…This complexity makes accurate diagnosis and effective treatment of ADHD a challenge (Amado-Caballero et al, 2020 ). Traditional diagnosis of ADHD relies on behavioral observations and psychological assessments, but these methods carry the potential for subjective judgments that can lead to diagnostic inconsistencies and accuracy issues (Shoeibi et al, 2023 ). In addition, due to the diversity of ADHD symptoms and their similarity to other disorders, it is often difficult for a single diagnostic approach to fully capture the full picture of the disease (Berrezueta-Guzman et al, 2021a ).…”

Section: Introductionmentioning

confidence: 99%

Using artificial intelligence methods to study the effectiveness of exercise in patients with ADHD

Yu,

Fang

2024

Front. Neurosci.

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Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder that significantly affects children and adults worldwide, characterized by persistent inattention, hyperactivity, and impulsivity. Current research in this field faces challenges, particularly in accurate diagnosis and effective treatment strategies. The analysis of motor information, enriched by artificial intelligence methodologies, plays a vital role in deepening our understanding and improving the management of ADHD. The integration of AI techniques, such as machine learning and data analysis, into the study of ADHD-related motor behaviors, allows for a more nuanced understanding of the disorder. This approach facilitates the identification of patterns and anomalies in motor activity that are often characteristic of ADHD, thereby contributing to more precise diagnostics and tailored treatment strategies. Our approach focuses on utilizing AI techniques to deeply analyze patients' motor information and cognitive processes, aiming to improve ADHD diagnosis and treatment strategies. On the ADHD dataset, the model significantly improved accuracy to 98.21% and recall to 93.86%, especially excelling in EEG data processing with accuracy and recall rates of 96.62 and 95.21%, respectively, demonstrating precise capturing of ADHD characteristic behaviors and physiological responses. These results not only reveal the great potential of our model in improving ADHD diagnostic accuracy and developing personalized treatment plans, but also open up new research perspectives for understanding the complex neurological logic of ADHD. In addition, our study not only suggests innovative perspectives and approaches for ADHD treatment, but also provides a solid foundation for future research exploring similar complex neurological disorders, providing valuable data and insights. This is scientifically important for improving treatment outcomes and patients' quality of life, and points the way for future-oriented medical research and clinical practice.

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Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression

Cited by 37 publications

References 127 publications

A Comprehensive Survey of ArtificialIntelligence in Precision Healthcare:Shedding Light on Interpretability

A Comprehensive Survey of ArtificialIntelligence in Precision Healthcare:Shedding Light on Interpretability

An Approach to Binary Classification of Alzheimer’s Disease Using LSTM

Using artificial intelligence methods to study the effectiveness of exercise in patients with ADHD

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