Filters








457 Hits in 0.91 sec

Association of parental stress and early childhood caries

Seyed Ebrahim Jabbarifar, Neda Ahmady, Seyed Ahmad Reza Sahafian, Fatemeh Samei, Shima Soheillipour
2009 Dental Research Journal  
Little research has been carried out on whether the parental stress affects children's oral health in general and dental caries in particular. This study aimed to investigate the association between parental stress and early childhood caries (ECC). A cross-sectional study was designed that included 250 children of 4-6 year-old; 127 ones attended the pediatric department of Isfahan School of Dentistry who had early childhood caries and a comparison group of 123 caries free children attended five
more » ... kindergartens and pre-schools in Isfahan city. Clinical examinations were conducted to evaluate the caries status. The parents of the two study groups completed the self-administrated long form of the Parenting Stress Index questionnaire. Details of their socio-demographic status were gathered too. The collected data were analyzed by SPSS version 11.5. The nonparametric Mantel-Haenszel test for correlation statistics was used to determine bivariate associations between total parenting stress and their domains scores in the two groups; i.e., those with early childhood caries and the caries free group. Mean score of PSI in the early childhood caries and caries free group were 286.66 ± 66.26 and 273.87 ± 31.03, respectively. There was not any significant relationship between total parental stress and ECC. The scores of the following domains of PSI demonstrated significant differences between ECC and CF groups: child reinforcement, child distractibility, child deficit attention, life stress and relationship with spouse (P = 0.01, 0.01, 0.001, 0.005 respectively). Findings of this study did not show any significant association between total parenting stress score and prevalence of early childhood caries.
pmid:21528033 pmcid:PMC3075457 fatcat:pdxme55k4zgu5l6ama3bsgot3i

V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation [article]

Fausto Milletari, Nassir Navab, Seyed-Ahmad Ahmadi
2016 arXiv   pre-print
Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields. Despite their popularity, most approaches are only able to process 2D images while most medical data used in clinical practice consists of 3D volumes. In this work we propose an approach to 3D image segmentation based on a volumetric, fully convolutional, neural network. Our CNN is trained end-to-end on MRI volumes depicting prostate, and learns to
more » ... redict segmentation for the whole volume at once. We introduce a novel objective function, that we optimise during training, based on Dice coefficient. In this way we can deal with situations where there is a strong imbalance between the number of foreground and background voxels. To cope with the limited number of annotated volumes available for training, we augment the data applying random non-linear transformations and histogram matching. We show in our experimental evaluation that our approach achieves good performances on challenging test data while requiring only a fraction of the processing time needed by other previous methods.
arXiv:1606.04797v1 fatcat:ykepws3ou5b7xdu5gf7h6psx2q

Multi-modal Disease Classification in Incomplete Datasets Using Geometric Matrix Completion [article]

Gerome Vivar, Andreas Zwergal, Nassir Navab, Seyed-Ahmad Ahmadi
2018 arXiv   pre-print
In large population-based studies and in clinical routine, tasks like disease diagnosis and progression prediction are inherently based on a rich set of multi-modal data, including imaging and other sensor data, clinical scores, phenotypes, labels and demographics. However, missing features, rater bias and inaccurate measurements are typical ailments of real-life medical datasets. Recently, it has been shown that deep learning with graph convolution neural networks (GCN) can outperform
more » ... al machine learning in disease classification, but missing features remain an open problem. In this work, we follow up on the idea of modeling multi-modal disease classification as a matrix completion problem, with simultaneous classification and non-linear imputation of features. Compared to methods before, we arrange subjects in a graph-structure and solve classification through geometric matrix completion, which simulates a heat diffusion process that is learned and solved with a recurrent neural network. We demonstrate the potential of this method on the ADNI-based TADPOLE dataset and on the task of predicting the transition from MCI to Alzheimer's disease. With an AUC of 0.950 and classification accuracy of 87%, our approach outperforms standard linear and non-linear classifiers, as well as several state-of-the-art results in related literature, including a recently proposed GCN-based approach.
arXiv:1803.11550v1 fatcat:7k7xl7w6jbcblnutd4grlvp6ey

Latent Patient Network Learning for Automatic Diagnosis [article]

Luca Cosmo, Anees Kazi, Seyed-Ahmad Ahmadi, Nassir Navab, Michael Bronstein
2020 arXiv   pre-print
Recently, Graph Convolutional Networks (GCNs) has proven to be a powerful machine learning tool for Computer Aided Diagnosis (CADx) and disease prediction. A key component in these models is to build a population graph, where the graph adjacency matrix represents pair-wise patient similarities. Until now, the similarity metrics have been defined manually, usually based on meta-features like demographics or clinical scores. The definition of the metric, however, needs careful tuning, as GCNs are
more » ... very sensitive to the graph structure. In this paper, we demonstrate for the first time in the CADx domain that it is possible to learn a single, optimal graph towards the GCN's downstream task of disease classification. To this end, we propose a novel, end-to-end trainable graph learning architecture for dynamic and localized graph pruning. Unlike commonly employed spectral GCN approaches, our GCN is spatial and inductive, and can thus infer previously unseen patients as well. We demonstrate significant classification improvements with our learned graph on two CADx problems in medicine. We further explain and visualize this result using an artificial dataset, underlining the importance of graph learning for more accurate and robust inference with GCNs in medical applications.
arXiv:2003.13620v1 fatcat:cdfxbi5sezaobil4xuc4xrnqum

Decision Support for Intoxication Prediction Using Graph Convolutional Networks [article]

Hendrik Burwinkel, Matthias Keicher, David Bani-Harouni, Tobias Zellner, Florian Eyer, Nassir Navab, Seyed-Ahmad Ahmadi
2020 arXiv   pre-print
Every day, poison control centers (PCC) are called for immediate classification and treatment recommendations if an acute intoxication is suspected. Due to the time-sensitive nature of these cases, doctors are required to propose a correct diagnosis and intervention within a minimal time frame. Usually the toxin is known and recommendations can be made accordingly. However, in challenging cases only symptoms are mentioned and doctors have to rely on their clinical experience. Medical experts
more » ... our analyses of a regional dataset of intoxication records provide evidence that this is challenging, since occurring symptoms may not always match the textbook description due to regional distinctions, inter-rater variance, and institutional workflow. Computer-aided diagnosis (CADx) can provide decision support, but approaches so far do not consider additional information of the reported cases like age or gender, despite their potential value towards a correct diagnosis. In this work, we propose a new machine learning based CADx method which fuses symptoms and meta information of the patients using graph convolutional networks. We further propose a novel symptom matching method that allows the effective incorporation of prior knowledge into the learning process and evidently stabilizes the poison prediction. We validate our method against 10 medical doctors with different experience diagnosing intoxication cases for 10 different toxins from the PCC in Munich and show our method's superiority in performance for poison prediction.
arXiv:2005.00840v1 fatcat:pqg7gojlobbrhkvk2oib363kay

Multi-modal Graph Fusion for Inductive Disease Classification in Incomplete Datasets [article]

Gerome Vivar, Hendrik Burwinkel, Anees Kazi, Andreas Zwergal, Nassir Navab, Seyed-Ahmad Ahmadi
2019 arXiv   pre-print
Clinical diagnostic decision making and population-based studies often rely on multi-modal data which is noisy and incomplete. Recently, several works proposed geometric deep learning approaches to solve disease classification, by modeling patients as nodes in a graph, along with graph signal processing of multi-modal features. Many of these approaches are limited by assuming modality- and feature-completeness, and by transductive inference, which requires re-training of the entire model for
more » ... h new test sample. In this work, we propose a novel inductive graph-based approach that can generalize to out-of-sample patients, despite missing features from entire modalities per patient. We propose multi-modal graph fusion which is trained end-to-end towards node-level classification. We demonstrate the fundamental working principle of this method on a simplified MNIST toy dataset. In experiments on medical data, our method outperforms single static graph approach in multi-modal disease classification.
arXiv:1905.03053v1 fatcat:i6m7vayfqjasxj6jltcif6ba6e

TOMAAT: volumetric medical image analysis as a cloud service [article]

Fausto Milletari, Johann Frei, Seyed-Ahmad Ahmadi
2018 arXiv   pre-print
Deep learning has been recently applied to a multitude of computer vision and medical image analysis problems. Although recent research efforts have improved the state of the art, most of the methods cannot be easily accessed, compared or used by either researchers or the general public. Researchers often publish their code and trained models on the internet, but this does not always enable these approaches to be easily used or integrated in stand-alone applications and existing workflows. In
more » ... is paper we propose a framework which allows easy deployment and access of deep learning methods for segmentation through a cloud-based architecture. Our approach comprises three parts: a server, which wraps trained deep learning models and their pre- and post-processing data pipelines and makes them available on the cloud; a client which interfaces with the server to obtain predictions on user data; a service registry that informs clients about available prediction endpoints that are available in the cloud. These three parts constitute the open-source TOMAAT framework.
arXiv:1803.06784v2 fatcat:4d4qdxwf6fbhlmfpytqqpudzfu

Qualitative Evaluation of Men Vulnerability to Extramarital Relations

Sadegh Baranoladi, Ozra Etemadi, Seyed Ahmad Ahmadi, Maryam Fatehizade
2016 Asian Social Science  
<p class="a"><span lang="EN-US">Because of the negative effects of marital infidelity followed to determine the reasons for clinicians and researchers is important. The purpose of this study was to investigate the causes of men marital infidelity. The approach used in the current study was a qualitative research method.To collect data, semi-structured interviews were used. Interview content analysis and categorization codes revealed that the reasons for marital infidelity placed in several
more » ... ories. Sexual (seeking happiness and freshness due to marriage burnout, having new sexual experiences, sensation seeking, and wife sloppiness), emotional (marital conflicts, crises of life, loss of self, and emotion and though sharing), and external factors (power, having the opportunity to relationship, confidence and support received from friends, attitude or entitlement, de inhibition due to drug use). These categorizations have implications for clinicians and researchers. Therapists working with infidelity should consider these factors in prevention programs and family enrichment.</span></p>
doi:10.5539/ass.v12n7p202 fatcat:qrlr4spi6nfcteofo5hqvab554

Recovery of Surgical Workflow Without Explicit Models [chapter]

Seyed-Ahmad Ahmadi, Tobias Sielhorst, Ralf Stauder, Martin Horn, Hubertus Feussner, Nassir Navab
2006 Lecture Notes in Computer Science  
Workflow recovery is crucial for designing context-sensitive service systems in future operating rooms. Abstract knowledge about actions which are being performed is particularly valuable in the OR. This knowledge can be used for many applications such as optimizing the workflow, recovering average workflows for guiding and evaluating training surgeons, automatic report generation and ultimately for monitoring in a context aware operating room. This paper describes a novel way for automatic
more » ... very of the surgical workflow. Our algorithms perform this task without an implicit or explicit model of the surgery. This is achieved by the synchronization of multidimensional state vectors of signals recorded in different operations of the same type. We use an enhanced version of the dynamic time warp algorithm to calculate the temporal registration. The algorithms have been tested on 17 signals of six different surgeries of the same type. The results on this dataset are very promising because the algorithms register the steps in the surgery correctly up to seconds, which is our sampling rate. Our software visualizes the temporal registration by displaying the videos of different surgeries of the same type with varying duration precisely synchronized to each other. The synchronized videos of one surgery are either slowed down or speeded up in order to show the same steps as the ones presented in the videos of the other surgery.
doi:10.1007/11866565_52 fatcat:3tksmo3fqrbzlkn7znxoatzdgu

Stabilizing Inputs to Approximated Nonlinear Functions for Inference with Homomorphic Encryption in Deep Neural Networks [article]

Moustafa AboulAtta, Matthias Ossadnik, Seyed-Ahmad Ahmadi
2019 arXiv   pre-print
Leveled Homomorphic Encryption (LHE) offers a potential solution that could allow sectors with sensitive data to utilize the cloud and securely deploy their models for remote inference with Deep Neural Networks (DNN). However, this application faces several obstacles due to the limitations of LHE. One of the main problems is the incompatibility of commonly used nonlinear functions in DNN with the operations supported by LHE, i.e. addition and multiplication. As common in LHE approaches, we
more » ... a model with a nonlinear function, and replace it with a low-degree polynomial approximation at inference time on private data. While this typically leads to approximation errors and loss in prediction accuracy, we propose a method that reduces this loss to small values or eliminates it entirely, depending on simple hyper-parameters. This is achieved by the introduction of a novel and elegantly simple Min-Max normalization scheme, which scales inputs to nonlinear functions into ranges with low approximation error. While being intuitive in its concept and trivial to implement, we empirically show that it offers a stable and effective approximation solution to nonlinear functions in DNN. In return, this can enable deeper networks with LHE, and facilitate the development of security- and privacy-aware analytics applications.
arXiv:1902.01870v1 fatcat:kan7xmyb4jf2vn2ptcg6biyahy

Explaining Experiences, Challenges and Adaptation Strategies in COVID-19 Patients: A Qualitative Study in Iran

Sina Ahmadi, Seyed Fahim Irandoost, Ahmad Ahmadi, Javad Yoosefi Lebni, Mohammad Ali Mohammadi Gharehghani, Nafe Baba Safari
2022 Frontiers in Public Health  
In the study of Ahmad and Murad, social media has a significant impact on the spread of fear and panic caused by the outbreak of COVID-19 in Iraqi Kurdistan and has a potentially negative impact on the  ... 
doi:10.3389/fpubh.2021.778026 pmid:35186867 pmcid:PMC8850373 fatcat:o6hhd3xzmzcs7ltsndlct2qofu

InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction [article]

Anees Kazi, Shayan shekarforoush, S.Arvind krishna, Hendrik Burwinkel, Gerome Vivar, Karsten Kortuem, Seyed-Ahmad Ahmadi, Shadi Albarqouni, Nassir Navab
2019 arXiv   pre-print
Geometric deep learning provides a principled and versatile manner for the integration of imaging and non-imaging modalities in the medical domain. Graph Convolutional Networks (GCNs) in particular have been explored on a wide variety of problems such as disease prediction, segmentation, and matrix completion by leveraging large, multimodal datasets. In this paper, we introduce a new spectral domain architecture for deep learning on graphs for disease prediction. The novelty lies in defining
more » ... metric 'inception modules' which are capable of capturing intra- and inter-graph structural heterogeneity during convolutions. We design filters with different kernel sizes to build our architecture. We show our disease prediction results on two publicly available datasets. Further, we provide insights on the behaviour of regular GCNs and our proposed model under varying input scenarios on simulated data.
arXiv:1903.04233v1 fatcat:o6mv437ikfbajlujjw7hd25pxu

Frangi Goes US : Multiscale Tubular Structure Detection Adapted to 3D Ultrasound [chapter]

Paulo Waelkens, Seyed-Ahmad Ahmadi, Nassir Navab
2012 Lecture Notes in Computer Science  
We propose a Hessian matrix based multiscale tubular structure detection (TSD) algorithm adapted to 3D B-mode vascular US images. The algorithm is designed to highlight blood vessel centerline points and yield an estimate of the cross-section radius at each centerline point. It can be combined with a simple centerline extraction scheme, yielding precise, fast and fully automatic lumen segmentation initializations. TSD algorithms designed with CTA and MRA datasets in mind, e.g. the Frangi Filter
more » ... [3], are not capable of reliably distinguishing centerline points from other points in vascular US datasets, since some assumptions underlying these algorithms are not reasonable for US datasets. The algorithm we propose, does not have these shortcomings and performs significantly better on vascular US datasets. We propose a statistic to evaluate how well a TSD algorithm is able to distinguish centerline points from other points. Based on this statistic, we compare the Frangi Filter to various versions of our new algorithm, on 11 3D US carotid datasets.
doi:10.1007/978-3-642-33415-3_77 fatcat:db76wnagivckzpeuq73q3htwbu

Sonography in the diagnosis of acute appendicitis

Ahmad Ryazi, Razyeh Tavakoli Rishehry, Ahmadi Ali Karimi, Mohammad Reza Farzaneh, Seyed Sajad Eghbali
2003 Iranian South Medical Journal  
Graded compressive sonography may be useful as an adjuvant in the diagnosis of acute appendicitis. To determine the role of sonography in the differential diagnosis of acute appendicitis, preappendectomy sonographic data of 164 consecutive cases in Fatemeh-Zahra Teaching Hospital were evaluated. Of 113 (68.9%) patients who had acute appendicitis in histopathology, 64 (56.6%) cases had preoperative sonographic diagnosis of acute appendicitis. Of 51 patients who had normal appendices, 40 (78.4%)
more » ... ases had normal appendices in sonographic evaluations. Sensitivity, specificity and accuracy of sonography for acute appendicitis were 56.7%, 78.4% and 0.63, respectively. The positive and negative predictive values were 85.3% and 44.49% respectively. As a result, sonographic evaluation is an additional diagnostic tool in acute appendicitis.
doaj:03d4061fa3754357b202b863f7710ce2 fatcat:ppufqrhekrdxnpbyoyed5jg75a

Statistical modeling and recognition of surgical workflow

Nicolas Padoy, Tobias Blum, Seyed-Ahmad Ahmadi, Hubertus Feussner, Marie-Odile Berger, Nassir Navab
2012 Medical Image Analysis  
Also demonstrated on cholecystectomy, we have proposed in previous work approaches based on Dynamic Time Warping for segmenting the surgical phases of the surgery using laparoscopic tool usage (Ahmadi  ... 
doi:10.1016/j.media.2010.10.001 pmid:21195015 fatcat:zairjix6mreshl5ynhcr4p3f5e
« Previous Showing results 1 — 15 out of 457 results