Early prognostication of overall survival for pediatric diffuse midline gliomas using MRI radiomics and machine learning: a two-center study.

Background: Diffuse midline gliomas (DMG) are aggressive pediatric brain tumors that are diagnosed and monitored through MRI. We developed an automatic pipeline to segment subregions of DMG and select radiomic features that predict patient overall survival (OS). Methods: We acquired diagnostic and post-radiation therapy (RT) multisequence MRI (T1, T1ce, T2, T2 FLAIR) and manual segmentations from two centers of 53 (internal cohort) and 16 (external cohort) DMG patients. We pretrained a deep learning model on a public adult brain tumor dataset, and finetuned it to automatically segment tumor core (TC) and whole tumor (WT) volumes. PyRadiomics and sequential feature selection were used for feature extraction and selection based on the segmented volumes. Two machine learning models were trained on our internal cohort to predict patient 1-year survival from diagnosis. One model used only diagnostic tumor features and the other used both diagnostic and post-RT features. Results: For segmentation, Dice score (mean [median]±SD) was 0.91 (0.94)±0.12 and 0.74 (0.83)±0.32 for TC, and 0.88 (0.91)±0.07 and 0.86 (0.89)±0.06 for WT for internal and external cohorts, respectively. For OS prediction, accuracy was 77% and 81% at time of diagnosis, and 85% and 78% post-RT for internal and external cohorts, respectively. Homogeneous WT intensity in baseline T2 FLAIR and larger post-RT TC/WT volume ratio indicate shorter OS. Conclusions: Machine learning analysis of MRI radiomics has potential to accurately and non-invasively predict which pediatric patients with DMG will survive less than one year from the time of diagnosis to provide patient stratification and guide therapy.

Automatic Visual Acuity Loss Prediction in Children with Optic Pathway Gliomas using Magnetic Resonance Imaging.

Children with optic pathway gliomas (OPGs), a low-grade brain tumor associated with neurofibromatosis type 1 (NF1-OPG), are at risk for permanent vision loss. While OPG size has been associated with vision loss, it is unclear how changes in size, shape, and imaging features of OPGs are associated with the likelihood of vision loss. This paper presents a fully automatic framework for accurate prediction of visual acuity loss using multi-sequence magnetic resonance images (MRIs). Our proposed framework includes a transformer-based segmentation network using transfer learning, statistical analysis of radiomic features, and a machine learning method for predicting vision loss. Our segmentation network was evaluated on multi-sequence MRIs acquired from 75 pediatric subjects with NF1-OPG and obtained an average Dice similarity coefficient of 0.791. The ability to predict vision loss was evaluated on a subset of 25 subjects with ground truth using cross-validation and achieved an average accuracy of 0.8. Analyzing multiple MRI features appear to be good indicators of vision loss, potentially permitting early treatment decisions.Clinical relevance- Accurately determining which children with NF1-OPGs are at risk and hence require preventive treatment before vision loss remains challenging, towards this we present a fully automatic deep learning-based framework for vision outcome prediction, potentially permitting early treatment decisions.

The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn).

Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions.

The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma.

Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on multiparametric MRI (mpMRI) for diagnosis, treatment planning, and longitudinal treatment monitoring; yet automated, objective, and quantitative tools for non-invasive assessment of meningiomas on mpMRI are lacking. The BraTS meningioma 2023 challenge will provide a community standard and benchmark for state-of-the-art automated intracranial meningioma segmentation models based on the largest expert annotated multilabel meningioma mpMRI dataset to date. Challenge competitors will develop automated segmentation models to predict three distinct meningioma sub-regions on MRI including enhancing tumor, non-enhancing tumor core, and surrounding nonenhancing T2/FLAIR hyperintensity. Models will be evaluated on separate validation and held-out test datasets using standardized metrics utilized across the BraTS 2023 series of challenges including the Dice similarity coefficient and Hausdorff distance. The models developed during the course of this challenge will aid in incorporation of automated meningioma MRI segmentation into clinical practice, which will ultimately improve care of patients with meningioma.

Delayed Cerebral Ischemia after Subarachnoid Hemorrhage.

Delayed cerebral ischemia (DCI) is the most feared complication of aneurysmal subarachnoid hemorrhage (aSAH). It increases the mortality and morbidity associated with aSAH. Previously, large cerebral artery vasospasm was thought to be the sole major contributing factor associated with increased risk of DCI. Recent literature has challenged this concept. We conducted a literature search using PUBMED as the prime source of articles discussing various other factors which may contribute to the development of DCI both in the presence or absence of large cerebral artery vasospasm. These factors include microvascular spasm, micro-thrombosis, cerebrovascular dysregulation, and cortical spreading depolarization. These factors collectively result in inflammation of brain parenchyma, which is thought to precipitate early brain injury and DCI. We conclude that diagnostic modalities need to be refined in order to diagnose DCI more efficiently in its early phase, and newer interventions need to be developed to prevent and treat this condition. These newer interventions are currently being studied in experimental models. However, their effectiveness on patients with aSAH is yet to be determined.

EEG in game user analysis: A framework for expertise classification during gameplay.

Video games have become a ubiquitous part of demographically diverse cultures. Numerous studies have focused on analyzing the cognitive aspects involved in game playing that could help in providing an optimal gaming experience by improving video game design. To this end, we present a framework for classifying the game player's expertise level using wearable electroencephalography (EEG) headset. We hypothesize that expert and novice players' brain activity is different, which can be classified using frequency domain features extracted from EEG signals of the game player. A systematic channel reduction approach is presented using a correlation-based attribute evaluation method. This approach lead us in identifying two significant EEG channels, i.e., AF3 and P7, among fourteen channels available in Emotiv EPOC headset. In particular, features extracted from these two EEG channels contributed the most to the video game player's expertise level classification. This finding is validated by performing statistical analysis (t-test) over the extracted features. Moreover, among multiple classifiers used, K-nearest neighbor is the best classifier in classifying game player's expertise level with a classification accuracy of up to 98.04% (without data balancing) and 98.33% (with data balancing).

Morphometric and Functional Brain Connectivity Differentiates Chess Masters From Amateur Players.

A common task in brain image analysis includes diagnosis of a certain medical condition wherein groups of healthy controls and diseased subjects are analyzed and compared. On the other hand, for two groups of healthy participants with different proficiency in a certain skill, a distinctive analysis of the brain function remains a challenging problem. In this study, we develop new computational tools to explore the functional and anatomical differences that could exist between the brain of healthy individuals identified on the basis of different levels of task experience/proficiency. Toward this end, we look at a dataset of amateur and professional chess players, where we utilize resting-state functional magnetic resonance images to generate functional connectivity (FC) information. In addition, we utilize T1-weighted magnetic resonance imaging to estimate morphometric connectivity (MC) information. We combine functional and anatomical features into a new connectivity matrix, which we term as the functional morphometric similarity connectome (FMSC). Since, both the FC and MC information is susceptible to redundancy, the size of this information is reduced using statistical feature selection. We employ off-the-shelf machine learning classifier, support vector machine, for both single- and multi-modality classifications. From our experiments, we establish that the saliency and ventral attention network of the brain is functionally and anatomically different between two groups of healthy subjects (chess players). We argue that, since chess involves many aspects of higher order cognition such as systematic thinking and spatial reasoning and the identified network is task-positive to cognition tasks requiring a response, our results are valid and supporting the feasibility of the proposed computational pipeline. Moreover, we quantitatively validate an existing neuroscience hypothesis that learning a certain skill could cause a change in the brain (functional connectivity and anatomy) and this can be tested via our novel FMSC algorithm.

Physiological Sensors Based Emotion Recognition While Experiencing Tactile Enhanced Multimedia.

Emotion recognition has increased the potential of affective computing by getting an instant feedback from users and thereby, have a better understanding of their behavior. Physiological sensors have been used to recognize human emotions in response to audio and video content that engages single (auditory) and multiple (two: auditory and vision) human senses, respectively. In this study, human emotions were recognized using physiological signals observed in response to tactile enhanced multimedia content that engages three (tactile, vision, and auditory) human senses. The aim was to give users an enhanced real-world sensation while engaging with multimedia content. To this end, four videos were selected and synchronized with an electric fan and a heater, based on timestamps within the scenes, to generate tactile enhanced content with cold and hot air effect respectively. Physiological signals, i.e., electroencephalography (EEG), photoplethysmography (PPG), and galvanic skin response (GSR) were recorded using commercially available sensors, while experiencing these tactile enhanced videos. The precision of the acquired physiological signals (including EEG, PPG, and GSR) is enhanced using pre-processing with a Savitzky-Golay smoothing filter. Frequency domain features (rational asymmetry, differential asymmetry, and correlation) from EEG, time domain features (variance, entropy, kurtosis, and skewness) from GSR, heart rate and heart rate variability from PPG data are extracted. The K nearest neighbor classifier is applied to the extracted features to classify four (happy, relaxed, angry, and sad) emotions. Our experimental results show that among individual modalities, PPG-based features gives the highest accuracy of 78.57 % as compared to EEG- and GSR-based features. The fusion of EEG, GSR, and PPG features further improved the classification accuracy to 79.76 % (for four emotions) when interacting with tactile enhanced multimedia.

EEG based Classification of Long-term Stress Using Psychological Labeling.

Stress research is a rapidly emerging area in the field of electroencephalography (EEG) signal processing. The use of EEG as an objective measure for cost effective and personalized stress management becomes important in situations like the nonavailability of mental health facilities. In this study, long-term stress was classified with machine learning algorithms using resting state EEG signal recordings. The labeling for the stress and control groups was performed using two currently accepted clinical practices: (i) the perceived stress scale score and (ii) expert evaluation. The frequency domain features were extracted from five-channel EEG recordings in addition to the frontal and temporal alpha and beta asymmetries. The alpha asymmetry was computed from four channels and used as a feature. Feature selection was also performed to identify statistically significant features for both stress and control groups (via t-test). We found that support vector machine was best suited to classify long-term human stress when used with alpha asymmetry as a feature. It was observed that the expert evaluation-based labeling method had improved the classification accuracy by up to 85.20%. Based on these results, it is concluded that alpha asymmetry may be used as a potential bio-marker for stress classification, when labels are assigned using expert evaluation.

A modular cluster based collaborative recommender system for cardiac patients.

In the last few years, hospitals have been collecting a large amount of health related digital data for patients. This includes clinical test reports, treatment updates and disease diagnosis. The information extracted from this data is used for clinical decisions and treatment recommendations. Among health recommender systems, collaborative filtering technique has gained a significant success. However, traditional collaborative filtering algorithms are facing challenges such as data sparsity and scalability, which leads to a reduction in system accuracy and efficiency. In a clinical setting, the recommendations should be accurate and timely. In this paper, an improvised collaborative filtering technique is proposed, which is based on clustering and sub-clustering. The proposed methodology is applied on a supervised set of data for four different types of cardiovascular diseases including angina, non-cardiac chest pain, silent ischemia, and myocardial infarction. The patient data is partitioned with respect to their corresponding disease class, which is followed by k-mean clustering, applied separately on each disease partition. A query patient once directed to the correct disease partition requires to get similarity scores from a reduced sub-cluster, thereby improving the efficiency of the system. Each disease partition has a separate process for recommendation, which gives rise to modularization and helps in improving scalability of the system. The experimental results demonstrate that the proposed modular clustering based recommender system reduces the spatial search domain for a query patient and the time required for providing accurate recommendations. The proposed system improves upon the accuracy of recommendations as demonstrated by the precision and recall values. This is significant for health recommendation systems particularly for those related to cardiovascular diseases.

A study on the effects of traditional and olfaction enhanced multimedia on pleasantness classification based on brain activity analysis.

Human emotions are recognized in response to content engaging one (audio music) or two human senses (videos). An enhanced sensation with a more realistic feel could be achievable by engaging more than two human senses. In this study, olfaction enhanced multimedia content is generated by synchronizing traditional multimedia content with an olfaction dispenser for engaging olfactory sense in addition to vision and auditory senses. Brain activity of 20 participants (10 males and 10 females) is recorded with a commercially available EEG headband, while engaging with traditional and olfaction enhanced multimedia content. The human brain activity is used to analyze and differentiate the content engaging two (traditional multimedia content) or more than two (olfaction enhanced multimedia content) human senses. For brain activity analysis, we apply a t-test on the power spectra of five frequency sub-bands (delta, theta, alpha, beta, and gamma) of the acquired EEG data in response to traditional and olfaction enhanced multimedia. We observe that alpha, theta, and delta bands are significant in discriminating the response to traditional and olfaction enhanced multimedia content. High brain activity is observed in alpha, theta, and delta bands of frontal channels, while experiencing the olfaction enhanced multimedia content. A user-independent pleasantness classification based on human brain activity is also presented, where classification performance is measured using 10-fold cross validation. We extract features in frequency domain i.e., rational asymmetry (RASM) and differential asymmetry (DASM) from five EEG bands to classify two pleasantness states based on their valence scores using support vector machine (SVM) classifier. Features are further selected based on EEG electrode pair positions and sub-bands. We observed that RASM and DASM features selected from delta band (olfaction enhanced content), and alpha or gamma bands (traditional multimedia content) gives best classification accuracy. We achieved an accuracy of 75%, sensitivity of 77.7%, and specificity of 72.7% in response to olfaction enhanced multimedia content and an accuracy of 68.7%, sensitivity of 71.4%, and specificity of 69.2% in response to traditional multimedia content in classifying pleasant and unpleasant states using SVM. We observed that classification of pleasant state was comparatively better with olfaction enhanced multimedia content than traditional multimedia content.

Classification of Perceived Mental Stress Using A Commercially Available EEG Headband.

Human stress is a serious health concern, which must be addressed with appropriate actions for a healthy society. This paper presents an experimental study to ascertain the appropriate phase, when electroencephalography (EEG) based data should be recorded for classification of perceived mental stress. The process involves data acquisition, pre-processing, feature extraction and selection, and classification. The stress level of each subject is recorded by using a standard perceived stress scale questionnaire, which is then used to label the EEG data. The data are divided into two (stressed and non-stressed) and three (non-stressed, mildly stressed, and stressed) classes. The EEG data of 28 participants are recorded using a commercially available four channel Muse EEG headband in two phases i.e., pre-activity and post-activity. Five feature groups, which include power spectral density, correlation, differential asymmetry, rational asymmetry, and power spectrum are extracted from five bands of each EEG channel. We propose a new feature selection algorithm, which selects features from appropriate EEG frequency band based on classification accuracy. Three classifiers i.e., support vector machine, the Naive Bayes, and multi-layer perceptron are used to classify stress level of the participants. It is evident from our results that EEG recording during the pre-activity phase is better for classifying the perceived stress. An accuracy of [Formula: see text] and [Formula: see text] is achieved for two- and three-class stress classification, respectively, while utilizing five groups of features from theta band. Our proposed feature selection algorithm is compared with existing algorithms and gives better classification results.

Human stress classification using EEG signals in response to music tracks.

Stress is inevitably experienced by almost every person at some stage of their life. A reliable and accurate measurement of stress can give an estimate of an individual's stress burden. It is necessary to take essential steps to relieve the burden and regain control for better health. Listening to music is a way that can help in breaking the hold of stress. This study examines the effect of music tracks in English and Urdu language on human stress level using brain signals. Twenty-seven subjects including 14 males and 13 females having Urdu as their first language, with ages ranging from 20 to 35 years, voluntarily participated in the study. The electroencephalograph (EEG) signals of the participants are recorded, while listening to different music tracks by using a four-channel MUSE headband. Participants are asked to subjectively report their stress level using the state and trait anxiety questionnaire. The English music tracks used in this study are categorized into four genres i.e., rock, metal, electronic, and rap. The Urdu music tracks consist of five genres i.e., famous, patriotic, melodious, qawali, and ghazal. Five groups of features including absolute power, relative power, coherence, phase lag, and amplitude asymmetry are extracted from the preprocessed EEG signals of four channels and five bands, which are used by the classifier for stress classification. Four classifier algorithms namely sequential minimal optimization, stochastic decent gradient, logistic regression (LR), and multilayer perceptron are used to classify the subject's stress level into two and three classes. It is observed that LR performs well in identifying stress with the highest reported accuracy of 98.76% and 95.06% for two- and three-level classification respectively. For understanding gender, language, and genre related discriminations in stress, a t-test and one-way analysis of variance is used. It is evident from results that English music tracks have more influence on stress level reduction as compared to Urdu music tracks. Among the genres of both languages, a noticeable difference is not found. Moreover, significant difference is found in the scores reported by females as compared to males. This indicates that the stress behavior of females is more sensitive to music as compared to males.

Emotion Classification in Response to Tactile Enhanced Multimedia using Frequency Domain Features of Brain Signals.

Tactile enhanced multimedia is generated by synchronizing traditional multimedia clips, to generate hot and cold air effect, with an electric heater and a fan. This objective is to give viewers a more realistic and immersing feel of the multimedia content. The response to this enhanced multimedia content (mulsemedia) is evaluated in terms of the appreciation/emotion by using human brain signals. We observe and record electroencephalography (EEG) data using a commercially available four channel MUSE headband. A total of 21 participants voluntarily participated in this study for EEG recordings. We extract frequency domain features from five different bands of each EEG channel. Four emotions namely: happy, relaxed, sad, and angry are classified using a support vector machine in response to the tactile enhanced multimedia. An increased accuracy of 76.19% is achieved when compared to 63.41% by using the time domain features. Our results show that the selected frequency domain features could be better suited for emotion classification in mulsemedia studies.

Classification of Perceived Human Stress using Physiological Signals.

In this paper, we present an experimental study for the classification of perceived human stress using non-invasive physiological signals. These include electroencephalography (EEG), galvanic skin response (GSR), and photoplethysmography (PPG). We conducted experiments consisting of steps including data acquisition, feature extraction, and perceived human stress classification. The physiological data of 28 participants are acquired in an open eye condition for a duration of three minutes. Four different features are extracted in time domain from EEG, GSR and PPG signals and classification is performed using multiple classifiers including support vector machine, the Naive Bayes, and multi-layer perceptron (MLP). The best classification accuracy of 75% is achieved by using MLP classifier. Our experimental results have shown that our proposed scheme outperforms existing perceived stress classification methods, where no stress inducers are used.

Arrhythmia Classification of ECG Signals Using Hybrid Features.

Automatic detection and classification of life-threatening arrhythmia plays an important part in dealing with various cardiac conditions. In this paper, a novel method for classification of various types of arrhythmia using morphological and dynamic features is presented. Discrete wavelet transform (DWT) is applied on each heart beat to obtain the morphological features. It provides better time and frequency resolution of the electrocardiogram (ECG) signal, which helps in decoding important information of a quasiperiodic ECG using variable window sizes. RR interval information is used as a dynamic feature. The nonlinear dynamics of RR interval are captured using Teager energy operator, which improves the arrhythmia classification. Moreover, to remove redundancy, DWT subbands are subjected to dimensionality reduction using independent component analysis, and a total of twelve coefficients are selected as morphological features. These hybrid features are combined and fed to a neural network to classify arrhythmia. The proposed algorithm has been tested over MIT-BIH arrhythmia database using 13724 beats and MIT-BIH supraventricular arrhythmia database using 22151 beats. The proposed methodology resulted in an improved average accuracy of 99.75% and 99.84% for class- and subject-oriented scheme, respectively, using three-fold cross validation.

Brain tumor segmentation on Multimodal MRI scans using EMAP Algorithm.

The utilization of digital images is becoming popular in multiple areas such as clinical applications. There are multiple diagnostic and machine vision-based applications, where image processing plays a vital role in analyzing, interpreting, and solving the problem. Digital image processing techniques are used to increase the quality of images for human interpretation and machine perception. Tumor segmentation in brain magnetic resonance (MRI) volumes is considered as a complex task because of tumor shape, location, and texture. Manual segmentation is a time-consuming task that can be inaccurate due to an increasing volume of MR scanning performed. The goal of this research is to propose an automated method that can identify the whole tumor in each slice in volumetric MRI brain images, and find out the sub-tumor (core tumor, enhancing and non-enhancing) regions. The proposed algorithm is fully automated to segment out both high-grade glioma (HGG) and low-grade glioma (LGG), using the information provided by a sequence of MRI volumes. The designed algorithm does not require any training database and estimates the tumor regions independently using image processing techniques based on expectation maximization and K-mean clustering. The method is evaluated on BRATS 2015 dataset of LGG and HGG MR volumes. The average DICE score achieved by using the proposed technique is 0.92 and is comparable to state-of-the-art techniques which rely on computationally expensive algorithms.

Medical Image Analysis using Convolutional Neural Networks: A Review.

The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an affective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering have made medical image analysis one of the top research and development area. One of the reasons for this advancement is the application of machine learning techniques for the analysis of medical images. Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features. This is in contrast to those methods where traditionally hand crafted features are used. The selection and calculation of these features is a challenging task. Among deep learning techniques, deep convolutional networks are actively used for the purpose of medical image analysis. This includes application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval. In this study, a comprehensive review of the current state-of-the-art in medical image analysis using deep convolutional networks is presented. The challenges and potential of these techniques are also highlighted.

Multiclass Classification of Cardiac Arrhythmia Using Improved Feature Selection and SVM Invariants.

Arrhythmia is considered a life-threatening disease causing serious health issues in patients, when left untreated. An early diagnosis of arrhythmias would be helpful in saving lives. This study is conducted to classify patients into one of the sixteen subclasses, among which one class represents absence of disease and the other fifteen classes represent electrocardiogram records of various subtypes of arrhythmias. The research is carried out on the dataset taken from the University of California at Irvine Machine Learning Data Repository. The dataset contains a large volume of feature dimensions which are reduced using wrapper based feature selection technique. For multiclass classification, support vector machine (SVM) based approaches including one-against-one (OAO), one-against-all (OAA), and error-correction code (ECC) are employed to detect the presence and absence of arrhythmias. The SVM method results are compared with other standard machine learning classifiers using varying parameters and the performance of the classifiers is evaluated using accuracy, kappa statistics, and root mean square error. The results show that OAO method of SVM outperforms all other classifiers by achieving an accuracy rate of 81.11% when used with 80/20 data split and 92.07% using 90/10 data split option.

Electronic cigarettes use and perception amongst medical students: a cross sectional survey from Sindh, Pakistan.

OBJECTIVE: The manufacturers of electronic cigarettes (e-cigarettes) are actively marketing their product through electronic and social media. Undergraduate medical students are expected to have better knowledge and awareness as they directly interact with patients in their training, The purpose of this study is therefore, to determine knowledge, use and perception regarding e-cigarettes among medical students from Sindh, Pakistan. RESULTS: A cross-sectional study was conducted between 1st July and 30th September 2016 at five different medical colleges situated in the second largest province of Sindh, Pakistan. The data was collected through a structured, self-administered questionnaire. Of the 500 students, the mean age was 21.5 ± 1.7 years and 58% were females. Over (65.6%) students were aware of e-cigarettes, 31 (6.2%) reported having used e-cigarettes, of whom 6 (1.2%) self-reported daily use. Users of conventional tobacco products were significantly more likely to have heard of e-cigarettes (87.6% vs 51.6%, p < 0.001) and having used them (13.9% vs 1.3%, p < 0.001). On multivariable logistic regression analysis we found a strong association of e-cigarette use with consumption of conventional cigarettes [OR: 10.6, 95% CI 3.6-30.8, p < 0.001], use of smokeless tobacco products [OR: 7.9, 95% CI 2.7-23.4, p < 0.001] however a weak association was observed for Shisha use [OR: 3.05, 95% CI 0.9-9.6, p = 0.05].