Ahmad Shalbaf

• Professor (Assistant) at Shahid Beheshti University of Medical Sciences

Post-COVID-19, depression rates have risen sharply, increasing the need for early diagnosis using electroencephalogram (EEG) and deep learning. To tackle this, we developed a cloud-based computer-aided depression diagnostic (CCADD) system that utilizes EEG signals from local databases. This system was optimized through a series of experiments to id...

The symptoms of ovarian cancer are nonspecific, and current screening methods lack sufficient accuracy for early diagnosis. This often leads to detection at a later, more advanced stage of the disease. Medical imaging provides morphological and functional data to help characterize ovarian tumors, but more research is needed to develop reliable earl...

Obsessive-compulsive disorder (OCD) is a neuropsychiatric disorder that causes unwanted th oughts and repetitive behaviors in a person's life. Clinical data and neuroimaging technologies are common methods used to diagnose OCD disorders and predict treatment responses. This study reviews the application of artificial intelligence (AI), particularly...

Background In this study, we want to evaluate the response to Lutetium-177 ( ¹⁷⁷ Lu)-DOTATATE treatment in patients with neuroendocrine tumors (NETs) using single-photon emission computed tomography (SPECT) and computed tomography (CT), based on image-based radiomics and clinical features. Methods The total volume of tumor areas was segmented into...

Due to the absence of definitive treatment for Alzheimer’s disease (AD), slowing its development is essential. Accurately predicting the conversion of mild cognitive impairment (MCI) -a potential early stage of AD- to AD is challenging due to the subtle distinctions between individuals who will develop AD and those who will not. As an increasing bo...

Estimation of mental workload from electroencephalogram (EEG) signals aims to accurately measure the cognitive demands placed on an individual during multitasking mental activities. By analyzing the brain activity of the subject, we can determine the level of mental effort required to perform a task and optimize the workload to prevent cognitive ov...

Schizophrenia (SZ) is a serious mental disorder that can mainly be distinguished by symptoms including delusions and hallucinations. This mental disorder makes difficult conditions for the person and her/his relatives. Electroencephalogram (EEG) signal is a sophisticated neuroimaging technique that helps neurologists to diagnose this mental disorde...

Mental workload refers to the cognitive effort required to perform tasks, and it is an important factor in various fields, including system design, clinical medicine, and industrial applications. In this paper, we propose innovative methods to assess mental workload from EEG data that use effective brain connectivity for the purpose of extracting f...

Purpose: This study aims to diagnose the severity of important pathological indices, i.e., fibrosis, steatosis, lobular inflammation, and ballooning from the pathological images of the liver tissue based on extracted features by radiomics methods. Materials and Methods: This research uses the pathological images obtained from liver tissue samples f...

This study aims to develop a machine learning approach leveraging clinical data and blood parameters to predict non-alcoholic steatohepatitis (NASH) based on the NAFLD Activity Score (NAS). Using a dataset of 181 patients, we performed preprocessing including normalization and categorical encoding. To identify predictive features, we applied sequen...

In this study, we have developed a novel method based on deep learning and brain effective connectivity to classify responders and non-responders to selective serotonin reuptake inhibitors (SSRIs) antidepressants in major depressive disorder (MDD) patients prior to the treatment using EEG signal. The effective connectivity of 30 MDD patients was de...

Background The Gleason grading system has been the most effective prediction for prostate cancer patients. This grading system provides this possibility to assess prostate cancer’s aggressiveness and then constitutes an important factor for stratification and therapeutic decisions. However, determining Gleason grade requires highly-trained patholog...

This study aims to develop a machine learning approach leveraging clinical data and blood parameters to predict non-alcoholic steatohepatitis (NASH) based on the NAFLD Activity Score (NAS). Using a dataset of 181 patients, we performed preprocessing including normalization and categorical encoding. To identify predictive features, we applied sequen...

The objective of this paper is to develop a novel emotion recognition system from electroencephalogram (EEG) signals using effective connectivity and deep learning methods. Emotion recognition is an important task for various applications such as human-computer interaction and, mental health diagnosis. The paper aims to improve the accuracy and rob...

Introduction Functional neuroimaging has developed a fundamental ground for understanding the physical basis of the brain. Recent studies have extracted invaluable information from the underlying substrate of the brain. However, cognitive deficiency has insufficiently been assessed by researchers in multiple sclerosis (MS). Therefore, extracting th...

This study aimed to investigate the diagnostic performance of machine learning-based radiomics analysis to diagnose coronary artery disease status and risk from rest/stress Myocardial Perfusion Imaging (MPI) single-photon emission computed tomography (SPECT). A total of 395 patients suspicious of coronary artery disease who underwent 2-day stress-r...

Background: Although renal transplantation is a crucial option for End-Stage Renal Disease (ESRD) patients, some recipients may encounter acute or chronic rejection. As a result, precise prediction of the outcome of kidney transplantation is important. Objective: This study aimed to predict the renal transplantation outcome with desirable accuracy...

This study aims to automatically detect the degree of pathological indices as a reference method for detecting the severity and extent of various liver diseases from pathological images of liver tissue with the help of deep learning algorithms. Grading is done using a collection of pre‐trained convolutional neural networks, including DenseNet121, R...

Prediction of response to Repetitive Transcranial Magnetic Stimulation (rTMS) can build a very effective treatment platform that helps Major Depressive Disorder (MDD) patients to receive timely treatment. We proposed a deep learning model powered up by state-of-the-art methods to classify responders (R) and non-responders (NR) to rTMS treatment. Pr...

The early diagnosis of NASH disease can decrease the risk of proceeding elements and treatment costs for patients. This study aims to present an optimal combination of intelligent algorithms using advanced machine learning methods, including different feature selections and classifications based on clinical data and blood factors. In this work, col...

Background: Diagnosis of the stage of COVID-19 patients using the chest computed tomography (CT) can help the physician in making decisions on the length of time required for hospitalization and adequate selection of patient care. This diagnosis requires very expert radiologists who are not available everywhere and is also tedious and subjective....

Introduction Repetitive transcranial magnetic stimulation (rTMS) is a non-pharmacological treatment for drug-resistant major depressive disorder (MDD) patients. Since the success rate of rTMS treatment is about 50%–55%, it is essential to predict the treatment outcome before starting based on electroencephalogram (EEG) signals, leading to identifyi...

In this study, we have developed a novel method based on deep learning and brain effective connectivity to classify responders and non-responders to selective serotonin reuptake inhibitors (SSRIs) antidepressants in major depressive disorder (MDD) patients prior to the treatment using EEG signal. The effective connectivity of 30 MDD patients was de...

Repetitive Transcranial Magnetic Stimulation (rTMS) can be used as an effective treatment for Major Depressive Disorder (MDD) especially when a patient does not respond to multiple antidepressants. However, the prediction of the treatment outcome of rTMS is a vital task to prevent starting an inefficient treatment which may waste several important...

Purpose: Emotions are integral brain states that can influence our behavior, decision-making, and functions. Electroencephalogram (EEG) is an appropriate modality for emotion recognition since it has high temporal resolution and is a noninvasive and cheap technique. Materials and Methods:.A novel approach based on Ensemble pre-trained Convolutiona...

One of the most effective treatments for drug-resistant Major depressive disorder (MDD) patients is repetitive transcranial magnetic stimulation (rTMS). To improve treatment efficacy and reduce health care costs, it is necessary to predict the treatment response. In this study, we intend to predict the rTMS treatment response in MDD patients from e...

Repetitive Transcranial Magnetic Stimulation (rTMS) is proposed as an effective treatment for major depressive disorder (MDD). However, because of the suboptimal treatment outcome of rTMS, the prediction of response to this technique is a crucial task. We developed a deep learning (DL) model to classify responders (R) and non-responders (NR). With...

Major Depressive Disorder (MDD) is a high prevalence disease that needs an effective and timely treatment to prevent its progress and additional costs. Repetitive Transcranial Magnetic Stimulation (rTMS) is an effective treatment option for MDD patients which uses strong magnetic pulses to stimulate specific regions of the brain. However, some pati...

Introduction: Quantification of lung involvement in COVID-19 using chest Computed tomography (CT) scan can help physicians to evaluate the progression of the disease or treatment response. This paper presents an automatic deep transfer learning ensemble based on pre-trained convolutional neural networks (CNNs) to determine the severity of COVID -19...

Detection of mental disorders such as schizophrenia (SZ) through investigating brain activities recorded via Electroencephalogram (EEG) signals is a promising field in neuroscience. This study presents a hybrid brain effective connectivity and deep learning framework for SZ detection on multichannel EEG signals. First, the effective connectivity ma...

Introduction: Coronary stenosis is one of the leading causes of death due to cardiovascular disease in most countries worldwide. Therefore, early identification of evidence for this disease is essential to take the necessary measures to prevent further developments. This study aimed to diagnose coronary stenosis by radiomics analysis of myocardial...

The aim of this study is to classify patients suspected from COVID-19 to five stages as normal, early, progressive, peak, and absorption stages using radiomics approach based on lung computed tomography images. Lung CT scans of 683 people were evaluated. A set of statistical texture features was extracted from each CT image. The people were classif...

Convolutional Neural Networks (CNN) have been widely utilized in Emotion Recognition (ER) research due to their vast benefits. Designing specific CNN configuration and learning accurate parameters are provided through the Transfer Learning (TL) approach. A novel combined ER schema based on sophisticated Frequency Effective Connectivity (FEC) maps a...

Purpose: Alzheimer's is the most common irreversible neurodegenerative disease. Its symptoms range from memory impairments to degradation of multiple cognitive abilities and ultimately death. Mild cognitive impairment (MCI) is the earliest detectable stage that happens between normal aging and early dementia, and even though MCI subjects have a ch...

Background: Motor Imagery (MI) Brain Computer Interface (BCI) directly links central nervous system to a computer or a device. Most MI-BCI structures rely on features of a single channel of Electroencephalogram (EEG). However, to provide more valuable features, the relationships among EEG channels in the form of effective brain connectivity analys...

Early prediction of COVID-19 mortality outcome can decrease expiration risk by alerting healthcare personnel to assure efficient resource allocation and treatment planning. This study introduces a machine learning framework for the prediction of COVID-19 mortality using demographics, vital signs, and laboratory blood tests (complete blood count (CB...

Functional neuroimaging has developed a fundamental ground for understanding the physical basis of the brain. Recent studies extracted invaluable information from the underlying substrate of the brain. However, Cognitive deficiency has insufficiently been assessed by researchers in Multiple Sclerosis (MS). Therefore, extracting the brain network di...

Convolutional Neural Networks (CNN) have recently made considerable advances in the field of biomedical signal processing. These methodologies can assist in emotion recognition for affective brain computer interface. In this paper, a novel emotion recognition system based on the effective connectivity and the fine-tuned CNNs from multichannel Elect...

Major Depressive Disorder (MDD) is a widespread global mental disease. The Effectiveness of Selective Serotonin Reuptake Inhibitors (SSRIs) antidepressants prescribed for MDD patients is limited and pre-treatment assessment of treatment outcome is a vital task. In this study, a hybrid model based on Transfer Learning (TL) of powerful pre-trained de...

Severity assessment of the novel Coronavirus (COVID‐19) using chest computed tomography (CT) scan is crucial for the effective administration of the right therapeutic drugs and also for monitoring the progression of the disease. However, determining the severity of COVID‐19 needs a highly expert radiologist by visual assessment, which is time‐consu...

Introduction: Mental arithmetic analysis based on Electroencephalogram (EEG) signals can help understand disorders, such as attention-deficit hyperactivity, dyscalculia, or autism spectrum disorder where the difficulty in learning or understanding the arithmetic exists. Most mental arithmetic recognition systems rely on features of a single channe...

Introduction The right and left-hand motor imagery (MI) analysis based on the electroencephalogram (EEG) signal can directly link the central nervous system to a computer or a device. This study aims to identify a set of robust and nonlinear effective brain connectivity features quantified by transfer entropy (TE) to characterize the relationship b...

EMG signals have played a pivotal role as a fundamental component of myriad modern prostheses to control prostheses’ movements as well as identifying individual and combined hand or finger gestures. Despite a great deal of interest in these signals, the non-stationary nature of biological EMG signals has led to complications in EMG applications. St...

Computer-aided diagnosis (CAD) of heart diseases using machine learning techniques has recently received much attention. In this study, we present a novel parametric-based feature selection method using the three-dimensional spherical harmonic (SHs) shape descriptors of the left ventricle (LV) for intelligent myocardial infarction (MI) classificati...

Accurate and early detection of non-alcoholic fatty liver, which is a major cause of chronic diseases is very important and is vital to prevent the complications associated with this disease. Ultrasound of the liver is the most common and widely performed method of diagnosing fatty liver. However, due to the low quality of ultrasound images, the ne...

Background: Deep learning techniques have recently made considerable advances in the field of artificial intelligence. These methodologies can assist psychologists in early diagnosis of mental disorders and preventing severe trauma. Major Depression Disorder (MDD) is a common and serious medical condition whose exact manifestations are not fully un...

Acute lymphoblastic leukemia (ALL) is the most frequently leukemia and categorized into three morphological subtypes named L1, L2 and L3. Early diagnosis of ALL plays a key role in treatment procedure especially in the case of children. Several similarities between morphology of three subtypes ALL (L1, L2, L3) and lymphocyte subtypes (normal, react...

Introduction: Ensuring an adequate Depth of Anesthesia (DOA) during surgery is essential for anesthesiologists. Since the effect of anesthetic drugs is on the central nervous system, brain signals such as Electroencephalogram (EEG) can be used for DOA estimation. Anesthesia can interfere among brain regions, so the relationship among different area...

Background: Surgery and accurate removal of the brain tumor in the operating room and after opening the scalp is one of the major challenges for neurosurgeons due to the removal of skull pressure and displacement and deformation of the brain tissue. This displacement of the brain changes the location of the tumor relative to the MR image taken...

PurposeCOVID-19 has infected millions of people worldwide. One of the most important hurdles in controlling the spread of this disease is the inefficiency and lack of medical tests. Computed tomography (CT) scans are promising in providing accurate and fast detection of COVID-19. However, determining COVID-19 requires highly trained radiologists an...

Introduction: In this study, a hybrid brain-computer interface for the classifiation of right and left-hand motor imagery using the deep learning method is presented to increase accuracy and performance. A hybrid brain-computer interface is designed to achieve a way of communicating between the brain and an external device for patients such as amyo...

Schizophrenia (SZ) is a severe disorder of the human brain which disturbs behavioral characteristics such as interruption in thinking, memory, perception, speech and other living activities. If the patient suffering from SZ is not diagnosed and treated in the early stages, damage to human behavioral abilities in its later stages could become more s...

Background: Ensuring adequate depth of anesthesia during surgery is essential for anesthesiologists to prevent the occurrence of unwanted alertness during surgery or failure to return to consciousness. Since the purpose of using anesthetics is to affect the central nervous system, brain signal processing such as electroencephalography (EEG) can be...

Introduction: Mental arithmetic analysis based on Electroencephalogram (EEG) signal can be helpful for understanding some disorders like attention deficit hyperactivity, dyscalculia, or autism spectrum disorder where the difficulty in learning or understanding the arithmetic exists. Most mental arithmetic recognition systems rely on features of a s...

Monitoring level of hypnosis is a major ongoing challenge for anesthetists to reduce anesthetic drug consumption, avoiding intraoperative awareness and prolonged recovery. This paper proposes a novel automated method for accurate assessing of the level of hypnosis with sevoflurane in 17 patients using the electroencephalogram signal. In this method...

Quantifying brain dynamics during anesthesia is an important challenge for understanding the neurophysiological mechanisms of anesthetic drug effect. Several single channel Electroencephalogram (EEG) indices have been proposed for monitoring anesthetic drug effect. The most commonly used single channel commercial index is the Bispectral index (BIS)...

Several studies have already assessed brain network variations in multiple sclerosis (MS) patients and healthy controls (HCs). The underlying neural system's functioning is apparently too complicated, however. Therefore, the neural time series' analysis through new methods is the aim of any recent research. Functional magnetic resonance imaging (fM...

Highlights  Achieving high accuracy in differentiating responder from non-responder to rTMS in MDD  Specifying a measure of Granger causality for predicting the response to rTMS significantly  Describing an effective connectivity pattern as a biomarker for major depression Abstract Objective: In this research, we explored among measures of ef...

Curve of left ventricular (LV) volume changes throughout the cardiac cycle is a fundamental parameter for clinical evaluation of various cardiovascular diseases. Currently, this evaluation is often performed manually which is tedious and time consuming and suffers from significant interobserver and intraobserver variability. This paper introduces a...

Purpose: The aim of this study is to evaluate the efficiency of a new automatic image processing technique, based on nonlinear dimensionality reduction (NLDR) to separate a cardiac cycle and also detect end-diastole (ED) (cardiac cycle start) and end-systole (ES) frames on an echocardiography system without using ECG. Methods: Isometric feature...

Purpose: Identification and assessment of left ventricular (LV) global and regional wall motion (RWM) abnormalities are essential for clinical evaluation of various cardiovascular diseases. Currently, this evaluation is performed visually which is highly dependent on the training and experience of echocardiographers and thus is prone to considerab...

In this paper, an automatic method for segmentation of the left ventricle in two-dimensional (2D) echocardiography images of one cardiac cycle is proposed. In the first step of this method, using a mean image of a sequence of echocardiography images and its statistical properties the approximate region of left ventricle (LV) is extracted. Then the...

Identification and classification of left ventricular (LV) regional wall motion (RWM) abnormalities on echocardiograms has fundamental clinical importance for various cardiovascular disease assessments especially in ischemia. In clinical practice, this evaluation is still performed visually which is highly dependent on training and experience of th...

The aim of this study is to evaluate the efficiency of applying a new non-rigid image registration method on two-dimensional echocardiographic images for computing the left ventricle (LV) myocardial motion field over a cardiac cycle. The key feature of our method is to register all images in the sequence to a reference image (end-diastole image) us...

Medical applications of ultrasound imaging have expanded enormously over the last two decades. De-noising is challenging issues for better medical interpretation and diagnosis on high volume of data sets in echocardiography. In this paper, manifold learning algorithm is applied on 2-D echocardiography images to discover the relationship between the...

Echocardiographic images have considerable noises (Especially speckle noise) because of their inherent nature and do not have desirable quality which makes difficult to analyze them. Therefore, it is essential to run pre-processing to reduce noises before their interpretation and analysis. In this paper, we have used Contourlet method to reduce the...

The first step for automatic calculation of the ejection fraction, stroke volume and some other features related to heart motion abnormalities in echocardiographic images is automatic detection of the end-systole and end-diastole frames. In this paper, modified Isomap algorithm is applied on two dimensional (2-D) echocardiographic images to reveal...

The automatic detection of end-diastole and end-systole frames of echocardiography images is the first step for calculation of the ejection fraction, stroke volume and some other features related to heart motion abnormalities. In this paper, the manifold learning algorithm is applied on 2D echocardiography images to find out the relationship betwee...

Visual information from lip shapes and movement help to improve the accuracy of a speech recognition system. This paper describes a novel approach for visual speech recognition that includes two stages: feature extraction from sequence of lip images and classifying them. This algorithm describes a region-based lip contour extraction algorithm based...

Beat detection algorithms have many clinical applications including pulse oximetry, cardiac arrhythmia detection, and cardiac output monitoring. Most of these algorithms have been developed by medical device companies and are proprietary. Thus, researchers who wish to investigate pulse contour analysis must rely on manual annotations or develop the...