Posterior reversible encephalopathy syndrome: A case report

Posterior Reversible Encephalopathy Syndrome (PRES) is a radio-clinical entity associating reversible damage of the central nervous system and typical brain imaging. The clinical context is often suggestive with, in half of cases, the use of vasoactive substances (cannabis, antidepressants, nasal decongestants) and/or postpartum. The etiologies are dominated by hypertensive encephalopathy, preeclampsia, eclampsia, immunosuppressive therapies, and systemic diseases. We report a case of posterior encephalopathy syndrome occurring in a young female without hypertension. It was about a 40-year-old female without hypertension underlying condition, received at the emergency department for headaches and generalized tonic-clonic seizures. The physical examination was unremarkable, and her blood pressure was 130/70 mm Hg. CT scan revealed bilateral white matter hypodensity in the posterior occipital regions and a right frontal subarachnoid hemorrhage. There was no aneurysmal malformation of the polygon of Willis and no cerebral thrombophlebitis. Brain MRI showed T2 and FLAIR hypersignal areas in the occipital and frontal cortico-subcortical regions, with no diffusion signal abnormalities or contrast enhancement, and a right frontal subarachnoid hemorrhagic lesion with no other impairment. The diagnosis of reversible posterior encephalopathy syndrome was made up, and the outcome was favorable under treatment. Posterior reversible encephalopathy syndrome is an uncommon but probably underdiagnosed condition. Hypertensive encephalopathy is the most common etiology. However, there would be cases of PRES without hypertension as shown in this observation.

Torsion of the spermatic cord revealing cystic dysplasia of the testicle

Cystic dysplasia of the testis is characterized by the presence of multiple cysts within the testicular parenchyma. It is a rare benign tumor. It is often accompanied by kidney malformations. There is no consensus on treatment. We report here the case of testicular dysplasia revealed by a torsion of the spermatic cord in an adult. The diagnosis of cystic dysplasia of the testis was made intraoperatively and confirmed by pathology. An orchiectomy was performed. Serum testicular cancer markers were normal postoperatively.

Deep Learning-Based Assessment Model for Real-Time Identification of Visual Learners Using Raw EEG

Automatic identification of visual learning style in real time using raw electroencephalogram (EEG) is challenging. In this work, inspired by the powerful abilities of deep learning techniques, deep learning-based models are proposed to learn high-level feature representation for EEG visual learning identification. Existing computer-aided systems that use electroencephalograms and machine learning can reasonably assess learning styles. Despite their potential, offline processing is often necessary to eliminate artifacts and extract features, making these methods unsuitable for real-time applications. The dataset was chosen with 34 healthy subjects to measure their EEG signals during resting states (eyes open and eyes closed) and while performing learning tasks. The subjects displayed no prior knowledge of the animated educational content presented in video format. The paper presents an analysis of EEG signals measured during a resting state with closed eyes using three deep learning techniques: Long-term, short-term memory (LSTM), Long-term, short-term memory-convolutional neural network (LSTM-CNN), and Long-term, short-term memory-Fully convolutional neural network (LSTM-FCNN). The chosen techniques were based on their suitability for real-time applications with varying data lengths and the need for less computational time. The optimization of hypertuning parameters has enabled the identification of visual learners through the implementation of three techniques. LSTM-CNN technique has the highest average accuracy of 94%, a sensitivity of 80%, a specificity of 92%, and an F1 score of 94% when identifying the visual learning style of the student out of all three techniques. This research has shown that the most effective method is the deep learning-based LSTM-CNN technique, which accurately identifies a student's visual learning style.

Small bowel obstruction due to congenital band in an adult: Radio-surgical correlation

Congenital band is a rare cause of bowel obstruction, most commonly occurring in childhood. We report a case of a young adult with no medical and surgical previous history who had symptoms of bowel obstruction evolving for 2 days. Computed tomography (CT) found an adhesive band causing a small bowel obstruction. An open laparotomy was performed, and the intraoperative findings were consistent with a congenital band compressing the ileum. Through this clinical case, we illustrate an uncommon cause of bowel obstruction and the interest of the CT for the management.

Anterior dislocation of the shoulder associated with a diaphyseal fracture of the ipsilateral humerus: a case report

Background

Anterior dislocation of the shoulder associated with a diaphyseal fracture of the ipsilateral humerus is a rare and controversial occurrence, with very few cases reported in the literature.

Case presentation

We present a case of a 39-year-old right-handed driver who presented with an anterior dislocation of the shoulder associated with a diaphyseal fracture of the ipsilateral humerus following a road traffic accident. The lateral approach to the fracture allowed us to use two forceps to gain a good grip on the proximal fragment and perform the maneuver to reduce the dislocation. The fracture was reduced and fixed with a molded Lecestre-type plate.

Conclusion

In this case, we employed the approach of initially reducing the shoulder dislocation with forceps, followed by osteosynthesis of the humeral fracture. The functional results were excellent after 6 months.

Acute mesenteric ischemia: A case report

Acute mesenteric ischemia is a rare life-threatening diagnostic and therapeutic emergency. Lack of clinical and biological specificity makes the diagnosis difficult. Imaging, particularly computed tomography can help confirm the diagnosis. An underlying cause is identified in about 30%-70% of cases and should always be sought. We report a case of a 51-year-old man with chronic alcoholic liver disease admitted to the emergency department for abdominal pain. Computed tomography showed mesenteric venous thrombosis with signs of small bowel ischemia and cirrhosis with portal hypertension. Through this observation, we describe the imaging aspects of mesenteric ischemia and emphasize the necessity of seeking underlying pathological condition.

Early stage of cerebral amyloid angiopathy revealed by follow-up of a minimal head injury

Cerebral amyloid angiopathy (CAA) is an age-related cerebral microangiopathy characterized by the accumulation of amyloid-beta peptide in the wall of leptomeningeal arteries and cortical vessels. Diagnosis of sporadic amyloid angiopathy is most often made in elderly patient with lobar hematoma. We report a case of a 68-year-old female who had minimal head injury. Cerebral CT showed a right cerebellar hematoma. Follow-up MRI after 4 months showed signs of cerebral amyloid angiopathy. Through this observation, we describe the MRI semiology that helps make the diagnosis of cerebral amyloid angiopathy.

Atypical fibromuscular dysplasia or carotid web revealed by cerebral infarction: A review of 2 cases

Atypical fibromuscular dysplasia of the bulb or carotid web is a nonatheromatous pathology more common in African and African-American populations. It is implicated in the occurrence of cerebral infarcts of unknown causes. Its diagnosis is made by angio-CT of the supra-aortic trunks and is characterized by a defect in the posterior wall of the bulb. Treatment with antiplatelet agents prevents the occurrence of stroke, but radical treatment remains surgical and endovascular. We report 2 observations of carotid web diagnosed and medically managed at the regional hospital of Saint Louis.

Numerical Solution of Inverse Problem in Functional Near Infrared Spectroscopy using L1-Norm Method

It has been more than three decades since researchers began investigating functional near-infrared spectroscopy (fNIRs) and its applications with near-infrared light for use in both clinical and pre-clinical settings. In order to increase the accuracy of fNIRs of complex tissue structures, it is necessary to create more advanced image reconstruction methods. Real fNIRs data have been used to develop an implementation of the L1-Norm approach for tackling the inverse problem in this work. The Monte Carlo (MC) simulation is used to construct the sensitivity matrix for this research. Finally, a numerical algorithm for the L1-Norm approach of image reconstruction is developed and implemented in MATLAB to aid in the process. The results showed good agreement with the actual fNIRs data.

Pylephlebitis complicating acute calculous cholecystitis: A case report

Pylephlebitis is a complication of intra-abdominal infections. Its occurrence during cholecystitis is a rare situation. We report the case of a 43-year-old female patient who presented with septic thrombosis of the right portal branch following acute calculous cholecystitis diagnosed on abdominal CT. The clinical evolution was favorable under antibiotic therapy and a cholecystectomy was scheduled.

Weber's syndrome revealing a Percheron artery infarction: A case report

Key Clinical Message

Weber's syndrome revealing a Percheron artery infarction is a rare clinical occurrence. Its diagnosis requires careful clinical examination and brain MRI, which is the gold standard for diagnosis. If this is not available, combined cerebral CT scan with a CT angiography of supra-aortic arteries may be useful for the diagnosis.

Abstract

Percheron's artery (PA) occlusion is an uncommon type of stroke involving paramedian thalamus and/or midbrain infarction. It accounts for 4%-18% of all thalamic infarcts and 0.1%-2% of all strokes. Its clinical manifestations are variable and its mode of presentation as Weber's syndrome is exceptional due to the unusual clinical presentation.

Numerical investigation of treated brain glioma model using a two-stage successive over-relaxation method

A brain tumor is a dynamic system in which cells develop rapidly and abnormally, as is the case with most cancers. Cancer develops in the brain or inside the skull when aberrant and odd cells proliferate in the brain. By depriving the healthy cells of leisure, nutrition, and oxygen, these aberrant cells eventually cause the healthy cells to perish. This article investigated the development of glioma cells in treating brain tumors. Mathematically, reaction-diffusion models have been developed for brain glioma growth to quantify the diffusion and proliferation of the tumor cells within brain tissues. This study presents the formulation the two-stage successive over-relaxation (TSSOR) algorithm based on the finite difference approximation for solving the treated brain glioma model to predict glioma cells in treating the brain tumor. Also, the performance of TSSOR method is compared to the Gauss-Seidel (GS) and two-stage Gauss-Seidel (TSGS) methods in terms of the number of iterations, the amount of time it takes to process the data, and the rate at which glioma cells grow the fastest. The implementation of the TSSOR, TSGS, and GS methods predicts the growth of tumor cells under the treatment protocol. The results show that the number of glioma cells decreased initially and then increased gradually by the next day. The computational complexity analysis is also used and concludes that the TSSOR method is faster compared to the TSGS and GS methods. According to the results of the treated glioma development model, the TSSOR approach reduced the number of iterations by between 8.0 and 71.95%. In terms of computational time, the TSSOR approach is around 1.18-76.34% faster than the TSGS and GS methods.

A novel method for efficient estimation of brain effective connectivity in EEG

BACKGROUND AND OBJECTIVE

Brain connectivity plays a pivotal role in understanding the brain's information processing functions by providing various details including magnitude, direction, and temporal dynamics of inter-neuron connections. While the connectivity may be classified as structural, functional and causal, a complete in-vivo directional analysis is guaranteed by the latter and is referred to as Effective Connectivity (EC). Two most widely used EC techniques are Directed Transfer Function (DTF) and Partial Directed Coherence (PDC) which are based on multivariate autoregressive models. The drawbacks of these techniques include poor frequency resolution and the requirement for experimental approach to determine signal normalization and thresholding techniques in identifying significant connectivities between multivariate sources.

METHODS

In this study, the drawbacks of DTF and PDC are addressed by proposing a novel technique, termed as Efficient Effective Connectivity (EEC), for the estimation of EC between multivariate sources using AR spectral estimation and Granger causality principle. In EEC, a linear predictive filter with AR coefficients obtained via multivariate EEG is used for signal prediction. This leads to the estimation of full-length signals which are then transformed into frequency domain by using Burg spectral estimation method. Furthermore, the newly proposed normalization method addressed the effect on each source in EEC using the sum of maximum connectivity values over the entire frequency range. Lastly, the proposed dynamic thresholding works by subtracting the first moment of causal effects of all the sources on one source from individual connections present for that source.

RESULTS

The proposed method is evaluated using synthetic and real resting-state EEG of 46 healthy controls. A 3D-Convolutional Neural Network is trained and tested using the PDC and EEC samples. The result indicates that compared to PDC, EEC improves the EEG eye-state classification accuracy, sensitivity and specificity by 5.57%, 3.15% and 8.74%, respectively.

CONCLUSION

Correct identification of all connections in synthetic data and improved resting-state classification performance using EEC proved that EEC gives better estimation of directed causality and indicates that it can be used for reliable understanding of brain mechanisms. Conclusively, the proposed technique may open up new research dimensions for clinical diagnosis of mental disorders.

Multiple aneurysms coexisting with carotid occlusion revealed by cerebral infarction: A case report

Intracranial aneurysms are focal dilations of an intracranial artery. They can be discovered incidentally, during a hemorrhagic stroke or subarachnoid hemorrhage, but it is rare for it to be detected after an ischemic stroke. The prevalence of the association between symptomatic carotid occlusion or stenosis and intracranial aneurysms is estimated to be 6.3%. We report the case of a patient hospitalized for the management of a stroke in whom investigations had revealed the coexistence of right carotid occlusion and multiple aneurysms in the right middle cerebral artery. The diagnosis was made by CT angiography of supra-aortic trunks.

Facial spasms revealing a Parry-Romberg syndrome: A case report

Introduction and importance

Parry-Romberg syndrome (PRS) is a rare clinical entity characterized by progressive atrophy of the hemifacial region, occasionally associated with systemic manifestations. The presence of facial muscles spasms is exceptional.

Case presentation

We report the case of a young woman who presented with progressive atrophy of the right hemiface associated with vitiligo and facial muscles spasms. The diagnosis of Parry Romberg syndrome was retained. Electromyogram showed an intermittent motor unit potential. Cerebral MRI showed atrophy of the muscle and subcutaneous fat of the right hemiface. She received corticosteroid in combination with botulinum toxin injection, which stopped the spasms.

Clinical discussion

It is a rare condition with a poorly understood etiology, which is responsible for the delay in diagnosis often noted. The association of this syndrome with neurological signs is exceptional and rarely described in the literature. Injection of botulinum toxin associated with corticosteroids can stop the spasms but only surgery can reduce the facial deformities.

Conclusion

Parry-Romberg syndrome is a rare disease, more frequent in women. It poses a real diagnostic problem and its treatment is poorly codified.

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Parry-Romberg syndrome is a rare disease whose prevalence isn't well known.

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This is an unknown disease and is the reason for the delay in diagnosis.

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Facial muscles spams is rarely associated with Parry-Romberg syndrome.

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Botulinum toxin can stop facial muscle spasms.

Translating medical image to radiological report: Adaptive multilevel multi-attention approach

BACKGROUND AND OBJECTIVE

Medical imaging techniques are widely employed in disease diagnosis and treatment. A readily available medical report can be a useful tool in assisting an expert for investigating the patient's health. A radiologist can benefit from an automatic medical image to radiological report translation system while preparing a final report. Previous attempts on automatic medical report generation task includes image captioning algorithms without taking domain-specific visual and textual contents into account, thus arises the question about credibility of generated report.

METHODS

In this work, a novel Adaptive Multilevel Multi-Attention (AMLMA) approach is proposed by offering domain-specific visual-textual knowledge to generate a thorough and believable radiological report for any view of a human chest X-ray image. The proposed approach leverages the encoder-decoder framework incorporated with multiple adaptive attention mechanisms. The potential of a convolutional neural network (CNN) with residual attention module (RAM) is demonstrated as a strong visual encoder for multi-label abnormality detection. The multilevel visual features (local and global) are extracted from proposed visual encoder to retrieve regional-level and abstract-level radiology-based semantic information. The Word2Vec and FastText word embeddings are trained on medical reports to acquire radiological knowledge and further used as textual encoders, feeding as input to Bi-directional Long Short Term Memory (Bi-LSTM) network to learn the co-relationship between medical terminologies in radiological reports. The AMLMA employs a weighted multilevel association of adaptive visual-semantic attention and visual-based linguistic attention mechanisms. This association of adaptive attention is exploited as a decoder and produces significant improvements in the report generation task.

RESULTS

The proposed approach is evaluated on a publicly available Indiana University chest X-ray (IU-CXR) dataset. The CNN with RAM shows the significant improvement in recall (0.4423), precision (0.1803) and F1-score (0.2551) for prediction of multiple abnormalities in X-ray image. The results of language generation metrics for proposed variants were acquired using the COCO-caption evaluation Application Program Interface (API). The trained embeddings with AMLMA model generates the convincing radiology report and outperform state-of-the-art (SOTA) approaches with high evaluation metrics scores for Bleu-4 (0.172), Meteor (0.247), Rouge_L (0.376) and CIDEr (0.381). In addition, a new "Unique Index" (UI) statistic is introduced to highlight the model's ability for generating unique reports.

CONCLUSION

The overall architecture aids to the understanding of various X-ray image views and generating the relevant normal and abnormal radiography statements. The proposed model is emphasized on multi-level visual-textual knowledge with adaptive attention mechanism to balance visual and linguistic information for the generation of admissible radiology report.

Spontaneous rupture of a giant pyonephrosis: A case report

Pyonephrosis is a suppurative infection of the kidney caused by ureteral obstruction. It can lead to kidney failure, septic shock, and death. Thus, it requires prompt assessment and appropriate management. We report a case of a 63-year-old male with giant pyonephrosis contained 10 liters of pus and spontaneously ruptured in the adjacent muscles. This clinical case illustrates the value of computed tomography scan in the diagnosis and management of an uncommon upper urinary tract infection and its complications.

[Contribution of ultrasound in the management of acute intestinal intussusception of the infant]

OBJECTIVE

The objective of this work was to study the place of ultrasound in the positive diagnosis, etiology and choice of the therapeutic modality of acute intestinal intussusception.

MATERIAL AND METHODS

This was a retrospective, descriptive, cross-sectional, multicenterstudy, carried out over a period of 18 months (January 1, 2016 to June 30, 2017) on 45 patient files collected in the radiologydepartments (Aristide Le Dantec and Albert Royer). Were included all patients aged less than 14 years, admitted with acute abdominal pain, whose diagnosis of IIA was retained on ultrasound. Pneumatic disinvagination was performed in patients without signs of severity. We studied the time of management, the ultrasound aspects of the invagination puddles, the therapeutic choice but also the radiosurgical concordance and the factors of failure of the pneumatic enema. Statistical analysis was done by SPSS version 21.0 software.

RESULTS

Ultrasound was used to make the diagnosis of IIA in 43 cases (95.5%). The sonographic characteristics were as follows: 27.9% of the IIA were located in the right hypochondrium, 19 cases were ileo-caecal, 10 (22.2%) ileo-caeco-colic, 9 (20%) ileo-colic, 4 (8.9%) colo-colic and one (2.2%) gregelic; 44 cases were idiopathic and one case was a Meckel's diverticulum The management time was less than 48 hours in 34% of cases and 66% more than 48 hours. Pneumatic reduction was performed in 18 cases (40%), with success in 14 cases (77.8%) and one case of pneumoperitoneum complication. Surgery was performed in 31 cases (68.8%). The sensitivity and specificity of ultrasound in the diagnosis of signs of severity were 77.7% and 78.9%. Good agreement was observed between the results of the Doppler ultrasound and the intraoperative findings. Ultrasound parameters associated with failed pneumatic deinvagination were: outer cylinder thickness ≥10 mm, adenopathy at the level of the small-axis bladder ≥10 mm, effusion in the bladder, and hypovascularization of the bladder head. Hypovascularization of the boudin head was the only factor independently associated with failure of pneumatic disinvagination.

CONCLUSION

Ultrasound is a powerful imaging modality in the positive diagnosis, etiology, severity and therapeutic choice of IIA.

Is Response to Genotoxic Stress Similar in Populations of African and European Ancestry? A Study of Dose-Response After in vitro Irradiation

Background: Exposure to genotoxic stress such as radiation is an important public health issue affecting a large population. The necessity of analyzing cytogenetic effects of such exposure is related to the need to estimate the associated risk. Cytogenetic biological dosimetry is based on the relationship between the absorbed dose and the frequency of scored chromosomal aberrations. The influence of confounding factors on radiation response is a topical issue. The role of ethnicity is unclear. Here, we compared the dose-response curves obtained after irradiation of circulating lymphocytes from healthy donors of African and European ancestry.

Materials and Methods: Blood samples from six Africans living in Africa, five Africans living in Europe, and five Caucasians living in Europe were exposed to various doses (0–4 Gy) of X-rays at a dose-rate of 0.1 Gy/min using an X-RAD320 irradiator. A validated cohort composed of 14 healthy Africans living in three African countries was included and blood samples were irradiated using the same protocols. Blood lymphocytes were cultured for 48 h and chromosomal aberrations scored during the first mitosis by telomere and centromere staining. The distribution of dicentric chromosomes was determined and the Kruskal-Wallis test was used to compare the dose-response curves of the two populations.

Results: No spontaneous dicentric chromosomes were detected in African donors, thus establishing a very low background of unstable chromosomal aberrations relative to the European population. There was a significant difference in the dose response curves between native African and European donors. At 4 Gy, African donors showed a significantly lower frequency of dicentric chromosomes (p = 8.65 10^–17), centric rings (p = 4.0310^–14), and resulting double-strand-breaks (DSB) (p = 1.32 10^–18) than European donors. In addition, a significant difference was found between African donors living in Europe and Africans living in Africa.

Conclusion: This is the first study to demonstrate the important role of ethnic and environmental factors that may epigenetically influence the response to irradiation. It will be necessary to establish country-of-origen-specific dose response curves to practice precise and adequate biological dosimetry. This work opens new perspective for the comparison of treatments based on genotoxic agents, such as irradiation.

Automated tibiofemoral joint segmentation based on deeply supervised 2D-3D ensemble U-Net: Data from the Osteoarthritis Initiative

Improving longevity is one of the greatest achievements in humanity. Because of this, the population is growing older, and the ubiquity of knee osteoarthritis (OA) is on the rise. Nonetheless, the understanding and ability to investigate potential precursors of knee OA have been impeded by time-consuming and laborious manual delineation processes which are prone to poor reproducibility. A method for automatic segmentation of the tibiofemoral joint using magnetic resonance imaging (MRI) is presented in this work. The proposed method utilizes a deeply supervised 2D-3D ensemble U-Net, which consists of foreground class oversampling, deep supervision loss branches, and Gaussian weighted softmax score aggregation. It was designed, optimized, and tested on 507 3D double echo steady-state (DESS) MR volumes using a two-fold cross-validation approach. A state-of-the-art segmentation accuracy measured as Dice similarity coefficient (DSC) for the femur bone (98.6 ± 0.27%), tibia bone (98.8 ± 0.31%), femoral cartilage (90.3 ± 2.89%), and tibial cartilage (86.7 ± 4.07%) is achieved. Notably, the proposed method yields sub-voxel accuracy for an average symmetric surface distance (ASD) less than 0.36 mm. The model performance is not affected by the severity of radiographic osteoarthritis (rOA) grades or the presence of pathophysiological changes. The proposed method offers an accurate segmentation with high time efficiency (~62 s) per 3D volume, which is well suited for efficient processing and analysis of the large prospective cohorts of the Osteoarthritis Initiative (OAI).

Conversational Task Increases Heart Rate Variability of Individuals Susceptible to Perceived Social Isolation

Studies showed that introversion is the strongest personality trait related to perceived social isolation (loneliness), which can predict various complications beyond objective isolation such as living alone. Lonely individuals are more likely to resort to social media for instantaneous comfort, but it is not a perpetual solution. Largely negative implications including poorer interpersonal relationship and depression were reported due to excessive social media usage. Conversational task is an established intervention to improve verbal communication, cognitive and behavioral adaptation among lonely individuals. Despite that behavioral benefits have been reported, it is unclear if they are accompanied by objective benefits underlying physiological changes. Here, we investigate the physiological signals from 28 healthy individuals during a conversational task. Participants were ranked by trait extraversion, where greater introversion is associated with increased susceptibility to perceived social isolation as compared to participants with greater extraversion as controls. We found that introverts had a greater tendency to be neurotic, and these participants also exhibited significant differences in task-related electrodermal activity (EDA), heart rate (HR) and HR variability (HRV) as compared to controls. Notably, resting state HRV among individuals susceptible to perceived loneliness was below the healthy thresholds established in literature. Conversational task with a stranger significantly increased HRV among individuals susceptible to isolation up to levels as seen in controls. Since HRV is also elevated by physical exercise and administration of oxytocin hormone (one form of therapy for behavioral isolation), conversational therapy among introverts could potentially confer physiological benefits to ameliorate social isolation and loneliness. Our findings also suggest that although the recent pandemic has changed how people are interacting typically, we should maintain a healthy dose of social interaction innovatively.

Identification of Autism Subtypes Based on Wavelet Coherence of BOLD FMRI Signals Using Convolutional Neural Network

The functional connectivity (FC) patterns of resting-state functional magnetic resonance imaging (rs-fMRI) play an essential role in the development of autism spectrum disorders (ASD) classification models. There are available methods in literature that have used FC patterns as inputs for binary classification models, but the results barely reach an accuracy of 80%. Additionally, the generalizability across multiple sites of the models has not been investigated. Due to the lack of ASD subtypes identification model, the multi-class classification is proposed in the present study. This study aims to develop automated identification of autism spectrum disorder (ASD) subtypes using convolutional neural networks (CNN) using dynamic FC as its inputs. The rs-fMRI dataset used in this study consists of 144 individuals from 8 independent sites, labeled based on three ASD subtypes, namely autistic disorder (ASD), Asperger’s disorder (APD), and pervasive developmental disorder not otherwise specified (PDD-NOS). The blood-oxygen-level-dependent (BOLD) signals from 116 brain nodes of automated anatomical labeling (AAL) atlas are used, where the top-ranked node is determined based on one-way analysis of variance (ANOVA) of the power spectral density (PSD) values. Based on the statistical analysis of the PSD values of 3-level ASD and normal control (NC), putamen_R is obtained as the top-ranked node and used for the wavelet coherence computation. With good resolution in time and frequency domain, scalograms of wavelet coherence between the top-ranked node and the rest of the nodes are used as dynamic FC feature input to the convolutional neural networks (CNN). The dynamic FC patterns of wavelet coherence scalogram represent phase synchronization between the pairs of BOLD signals. Classification algorithms are developed using CNN and the wavelet coherence scalograms for binary and multi-class identification were trained and tested using cross-validation and leave-one-out techniques. Results of binary classification (ASD vs. NC) and multi-class classification (ASD vs. APD vs. PDD-NOS vs. NC) yielded, respectively, 89.8% accuracy and 82.1% macro-average accuracy, respectively. Findings from this study have illustrated the good potential of wavelet coherence technique in representing dynamic FC between brain nodes and open possibilities for its application in computer aided diagnosis of other neuropsychiatric disorders, such as depression or schizophrenia.

Analysis of Default Mode Network in Social Anxiety Disorder: EEG Resting-State Effective Connectivity Study

Recent brain imaging findings by using different methods (e.g., fMRI and PET) have suggested that social anxiety disorder (SAD) is correlated with alterations in regional or network-level brain function. However, due to many limitations associated with these methods, such as poor temporal resolution and limited number of samples per second, neuroscientists could not quantify the fast dynamic connectivity of causal information networks in SAD. In this study, SAD-related changes in brain connections within the default mode network (DMN) were investigated using eight electroencephalographic (EEG) regions of interest. Partial directed coherence (PDC) was used to assess the causal influences of DMN regions on each other and indicate the changes in the DMN effective network related to SAD severity. The DMN is a large-scale brain network basically composed of the mesial prefrontal cortex (mPFC), posterior cingulate cortex (PCC)/precuneus, and lateral parietal cortex (LPC). The EEG data were collected from 88 subjects (22 control, 22 mild, 22 moderate, 22 severe) and used to estimate the effective connectivity between DMN regions at different frequency bands: delta (1-3 Hz), theta (4-8 Hz), alpha (8-12 Hz), low beta (13-21 Hz), and high beta (22-30 Hz). Among the healthy control (HC) and the three considered levels of severity of SAD, the results indicated a higher level of causal interactions for the mild and moderate SAD groups than for the severe and HC groups. Between the control and the severe SAD groups, the results indicated a higher level of causal connections for the control throughout all the DMN regions. We found significant increases in the mean PDC in the delta (p = 0.009) and alpha (p = 0.001) bands between the SAD groups. Among the DMN regions, the precuneus exhibited a higher level of causal influence than other regions. Therefore, it was suggested to be a major source hub that contributes to the mental exploration and emotional content of SAD. In contrast to the severe group, HC exhibited higher resting-state connectivity at the mPFC, providing evidence for mPFC dysfunction in the severe SAD group. Furthermore, the total Social Interaction Anxiety Scale (SIAS) was positively correlated with the mean values of the PDC of the severe SAD group, r (22) = 0.576, p = 0.006 and negatively correlated with those of the HC group, r (22) = -0.689, p = 0.001. The reported results may facilitate greater comprehension of the underlying potential SAD neural biomarkers and can be used to characterize possible targets for further medication.

Effective Connectivity in Default Mode Network for Alcoholism Diagnosis

Alcohol Use Disorder (AUD) is a chronic relapsing brain disease characterized by excessive alcohol use, loss of control over alcohol intake, and negative emotional states under no alcohol consumption. The key factor in successful treatment of AUD is the accurate diagnosis for better medical and therapy management. Conventionally, for individuals to be diagnosed with AUD, certain criteria as outlined in the Diagnostic and Statistical Manual of Mental Disorders (DSM) should be met. However, this process is subjective in nature and could be misleading due to memory problems and dishonesty of some AUD patients. In this paper, an assessment scheme for objective diagnosis of AUD is proposed. For this purpose, EEG recording of 31 healthy controls and 31 AUD patients are used for the calculation of effective connectivity (EC) between the various regions of the brain Default Mode Network (DMN). The EC is estimated using partial directed coherence (PDC) which are then used as input to a 3D Convolutional Neural Network (CNN) for binary classification of AUD cases. Using 5-fold cross validation, the classification of AUD vs. HC effective connectivity matrices using the proposed 3D-CNN gives an accuracy of 87.85 ± 4.64 %. For further validation, 32 and 30 subjects are randomly selected for training and testing, respectively, giving 100% correct classification of all the testing subjects.

Co-creation or Co-destruction: A Perspective of Online Customer Engagement Valence

The increasing interest in online shopping in recent years has increased the importance of understanding customer engagement valence (CEV) in a virtual service network. There is yet a comprehensive explanation of the CEV concept, particularly its impact on multi-actor networks such as web stores. Therefore, this study aims to fill this research gap. In this study, past literature in the marketing and consumer psychology field was critically reviewed to understand the concept of CEV in online shopping, and the propositional-based style was employed to conceptualize the CEV within the online shopping (web stores) context. The outcomes demonstrate that the valence of customer engagement is dependent on the cognitive interpretation of signals that are prompted by multiple actors on a web store service network. If the signals are positively interpreted, positive outcomes such as service co-creation are expected, but if they are negatively interpreted, negative outcomes such as service co-destruction are predicted. These notions create avenues for future empirical research and practical implications.

Sturge Weber syndrome, when brain CT is enough for diagnosis: about a case

One of the main manifestations of Sturge Weber syndrome is seizures. We report the case of a child received in the context of generalized seizures and in whom a cerebral contrast CT was sufficient to make the diagnosis of Sturge Weber syndrome.

[Sorting and detection of COVID-19 by low-dose thoracic CT scan in patients consulting the radiology department of Fann hospital (Dakar-Senegal)]

Introduction

COVID-19 has spread rapidly since its emergence in China and is currently a global health issue. Its definitive diagnosis is made by PCR on nasopharyngeal swabs. However, this diagnostic test has low sensitivity with delayed results. Hence, thoracic computed tomography represents an interesting alternative. The aims of this study were to assess the frequency of computed tomography (CT) lesions suggestive of COVID-19 and to compare the results of CT and PCR test.

Methods

a prospective study carried out over15 working days and involved 47 patients. These patients were recruited based on the presence of at least 2 clinical signs of COVID-19. Chest CT without contrast according to the "LOW-DOSE" protocol was performed. A PCR test on nasopharyngeal swabs was done in patients with signs suggestive of COVID on CT. A serological test was performed in case of a discrepancy between the CT and PCR results.

Results

thoracic CT was abnormal in 38 patients and normal in 9 patients. Lesions suggestive of COVID-19 have been identified in 32 patients. Two patients had lesions of non-specific pneumonia. Tuberculosis lesions were visualized in 3 patients. One patient had lesions of interstitial pneumonia. The mean DLP was 59 mGy.cm with extremes of 25 and 95 mGy.cm. Ground-glass opacity was present in 100% of COVID-19 suspects on CT. The results of the PCR test were the same than CT in 12 patients. The positive predictive value for CT was 37.5%. In 20 patients with COVID lesions on CT, the PCR test was negative with a false positive rate of 62.5%. In the patients with negative PCR test, 4 had a serological test for COVID-19 and this test was positive in 3.

Conclusion

low-dose chest CT can reduce radiation exposure in COVID-19 patients who are at risk of cumulative dose due to repetitive exam. CT can identify lesions suggestive of COVID-19. It also enables the triage of patients by identifying other diagnoses.

Healthcare Professional in the Loop (HPIL): Classification of Standard and Oral Cancer-Causing Anomalous Regions of Oral Cavity Using Textural Analysis Technique in Autofluorescence Imaging

Oral mucosal lesions (OML) and oral potentially malignant disorders (OPMDs) have been identified as having the potential to transform into oral squamous cell carcinoma (OSCC). This research focuses on the human-in-the-loop-system named Healthcare Professionals in the Loop (HPIL) to support diagnosis through an advanced machine learning procedure. HPIL is a novel system approach based on the textural pattern of OML and OPMDs (anomalous regions) to differentiate them from standard regions of the oral cavity by using autofluorescence imaging. An innovative method based on pre-processing, e.g., the Deriche-Canny edge detector and circular Hough transform (CHT); a post-processing textural analysis approach using the gray-level co-occurrence matrix (GLCM); and a feature selection algorithm (linear discriminant analysis (LDA)), followed by k-nearest neighbor (KNN) to classify OPMDs and the standard region, is proposed in this paper. The accuracy, sensitivity, and specificity in differentiating between standard and anomalous regions of the oral cavity are 83%, 85%, and 84%, respectively. The performance evaluation was plotted through the receiver operating characteristics of periodontist diagnosis with the HPIL system and without the system. This method of classifying OML and OPMD areas may help the dental specialist to identify anomalous regions for performing their biopsies more efficiently to predict the histological diagnosis of epithelial dysplasia.

Deep learning architectures analysis for age-related macular degeneration segmentation on optical coherence tomography scans

BACKGROUND AND OBJECTIVES

Aged people usually are more to be diagnosed with retinal diseases in developed countries. Retinal capillaries leakage into the retina swells and causes an acute vision loss, which is called age-related macular degeneration (AMD). The disease can not be adequately diagnosed solely using fundus images as depth information is not available. The variations in retina volume assist in monitoring ophthalmological abnormalities. Therefore, high-fidelity AMD segmentation in optical coherence tomography (OCT) imaging modality has raised the attention of researchers as well as those of the medical doctors. Many methods across the years encompassing machine learning approaches and convolutional neural networks (CNN) strategies have been proposed for object detection and image segmentation.

METHODS

In this paper, we analyze four wide-spread deep learning models designed for the segmentation of three retinal fluids outputting dense predictions in the RETOUCH challenge data. We aim to demonstrate how a patch-based approach could push the performance for each method. Besides, we also evaluate the methods using the OPTIMA challenge dataset for generalizing network performance. The analysis is driven into two sections: the comparison between the four approaches and the significance of patching the images.

RESULTS

The performance of networks trained on the RETOUCH dataset is higher than human performance. The analysis further generalized the performance of the best network obtained by fine-tuning it and achieved a mean Dice similarity coefficient (DSC) of 0.85. Out of the three types of fluids, intraretinal fluid (IRF) is more recognized, and the highest DSC value of 0.922 is achieved using Spectralis dataset. Additionally, the highest average DSC score is 0.84, which is achieved by PaDeeplabv3+ model using Cirrus dataset.

CONCLUSIONS

The proposed method segments the three fluids in the retina with high DSC value. Fine-tuning the networks trained on the RETOUCH dataset makes the network perform better and faster than training from scratch. Enriching the networks with inputting a variety of shapes by extracting patches helped to segment the fluids better than using a full image.

False ground-glass opacity and suspicion of COVID-19, beware of the technique for performing the CT

Ground-glass opacity is a CT sign that is currently experiencing renewed interest since it is very common in patients with COVID-19. However, this sign is not specific to any disease. Besides, the possibility of false positive ground-glass opacity related to insufficient inspiration during the acquisition of the chest CT should be known. We report the case of a 36-year-old patient suspected of COVID-19 and in whom a second acquisition of chest CT was necessary to remove false ground-glass opacities that erroneously supported the diagnosis of COVID-19.

Performance Evaluation Of Convolutions And Atrous Convolutions In Deep Networks For Retinal Disease Segmentation On Optical Coherence Tomography Volumes

The deterioration of the retina center could be the main reason for vision loss. Older people usually ranging from 50 years and above are exposed to age-related macular degeneration (AMD) disease that strikes the retina. The lack of human expertise to interpret the complexity in diagnosing diseases leads to the importance of developing an accurate method to detect and localize the targeted infection. Approaching the performance of ophthalmologists is the consistent main challenge in retinal disease segmentation. Artificial intelligence techniques have shown enormous achievement in various tasks in computer vision. This paper depicts an automated end-to-end deep neural network for retinal disease segmentation on optical coherence tomography (OCT) scans. The work proposed in this study shows the performance difference between convolution operations and atrous convolution operations. Three deep semantic segmentation architectures, namely U-net, Segnet, and Deeplabv3+, have been considered to evaluate the performance of varying convolution operations. Empirical outcomes show a competitive performance to the human level, with an average dice score of 0.73 for retinal diseases.

Review of EEG, ERP, and Brain Connectivity Estimators as Predictive Biomarkers of Social Anxiety Disorder

Social anxiety disorder (SAD) is characterized by a fear of negative evaluation, negative self-belief and extreme avoidance of social situations. These recurrent symptoms are thought to maintain the severity and substantial impairment in social and cognitive thoughts. SAD is associated with a disruption in neuronal networks implicated in emotional regulation, perceptual stimulus functions, and emotion processing, suggesting a network system to delineate the electrocortical endophenotypes of SAD. This paper seeks to provide a comprehensive review of the most frequently studied electroencephalographic (EEG) spectral coupling, event-related potential (ERP), visual-event potential (VEP), and other connectivity estimators in social anxiety during rest, anticipation, stimulus processing, and recovery states. A search on Web of Science provided 97 studies that document electrocortical biomarkers and relevant constructs pertaining to individuals with SAD. This study aims to identify SAD neuronal biomarkers and provide insight into the differences in these biomarkers based on EEG, ERPs, VEP, and brain connectivity networks in SAD patients and healthy controls (HC). Furthermore, we proposed recommendations to improve methods of delineating the electrocortical endophenotypes of SAD, e.g., a fusion of EEG with other modalities such as functional magnetic resonance imaging (fMRI) and magnetoencephalograms (MEG), to realize better effectiveness than EEG alone, in order to ultimately evolve the treatment selection process, and to review the possibility of using electrocortical measures in the early diagnosis and endophenotype examination of SAD.

Tri et détection du COVID-19 par TDM thoracique low-dose chez des patients tout venants au service de radiologie de l’Hôpital de Fann (Dakar-Sénégal)

Introduction

la COVID-19 s´est rapidement propagée depuis son apparition en Chine devenant actuellement un problème de santé internationale. Son diagnostic définitif se fait par réaction de polymérisation en chaîne (PCR) sur des prélèvements nasopharyngés. Ce moyen diagnostic est de faible sensibilité avec des résultats différés. Ce qui laisse place à la tomodensitométrie thoracique comme moyen diagnostic alternatif. Les objectifs de cette étude étaient d´évaluer la fréquence des lésions TDM évocatrices de COVID-19 et de confronter les résultats de la tomodensitométrie (TDM) à ceux du test PCR.

Méthodes

étude prospective réalisée sur une période de 15 jours ouvrables et qui a porté sur 47 patients. Ces patients étaient recrutés sur la présence d´au moins 2 signes cliniques du COVID-19. Une TDM thoracique sans injection selon le protocole « FAIBLE-DOSE » a été réalisé. Un test PCR sur prélèvements nasopharyngés a été fait chez les patients avec des signes évocateurs de COVID à la TDM. Un test sérologique été réalisé en cas de discordance entre les résultats TDM et PCR.

Résultats

la TDM thoracique était anormale chez 38 patients et normale chez 9 patients. Des lésions évocatrices de COVID-19 ont été identifiées chez 32 patients. Deux patients ont eu des lésions de pneumopathie non spécifique. Des lésions de pneumopathie tuberculeuse ont étés visualisées chez 3 patients. Un patient a eu des lésions de pneumopathie interstitielle commune. La DLP (dose-length product) moyenne était de 59 mGy.cm avec des extrêmes de 25 et 95 mGy.cm. L´opacité en verre dépoli était présente chez 100% des suspects de COVID-19 à la TDM. Le résultat du test PCR a confirmé la TDM chez 12 patients soit une valeur prédictive positive de la TDM de 37,5%. Chez 20 patients avec lésions COVID à la TDM, le test PCR était négatif soit un taux de faux positif de 62,5%. Chez les patients avec test PCR négatif, 4 ont fait un test sérologique de COVID-19 et ce test était positif chez 3.

Conclusion

la TDM thoracique low-dose permet de réduire l´irradiation chez les patients COVID-19 qui sont à risque de dose cumulative par répétition des TDM. La TDM permet d´identifier les lésions évocatrices de COVID-19. Elle permet également le triage des patients en permettant d´identifier d´autres diagnostics.

[Diagnostic performance of imaging examinations in acute non-traumatic abdominal pain in the radiology department of the Kaolack Regional Hospital]

AIM

The aim of this work was to evaluate the diagnostic performance of imaging examinations in the presence of acute non-traumatic abdominal pain.

MATERIALS AND METHODS

This was a prospective, cross-sectional and descriptive study over 6 months in the radiology and medical imaging department of the Kaolack Regional Hospital, including any patient received for acute non-traumatic abdominal pain with informed consent in whom the etiological diagnosis is supported by an imaging examination. We investigated the etiologies of acute abdominal pain and compared the imaging findings with surgical exploration. Our data were analyzed using SPSS 24.0 and Excel 2013 with a coefficient of significance of less than 5%.

RESULTS

106 patients were enrolled. The mean age was 32 years and the gender-ratio was 1.52 in favour of women. Acute abdominal pain was diffuse in 25.5% of patients and localized in 74.5%, of which 18.9% were at right iliac fossa.Abdominal X-ray was performed alone in 4 patients (3.8%), ultrasound alone in 46 patients (43.3%) and abdominal CT scan in 34 patients (32%). CT was combined with ultrasound in 6 patients (5.7%) and with abdominal X-ray in 16 patients (15%). The initial clinical diagnosis was corrected in 49.1% of patients. The sensitivity of the imaging compared to the final diagnosis retained was 96.2%.

CONCLUSION

Imaging represents a turning point in the management of patients with acute non-traumatic abdominal pain by providing better diagnostic guidance and avoiding serious complications and unnecessary interventions.

Neural correlates of eye contact in face-to-face verbal interaction: An EEG-based study of the extraversion personality trait

The extraversion personality trait has a positive correlation with social interaction. In neuroimaging studies, investigations on extraversion in face-to-face verbal interactions are still scarce. This study presents an electroencephalography (EEG)-based investigation of the extraversion personality trait in relation to eye contact during face-to-face interactions, as this is a vital signal in social interactions. A sample of healthy male participants were selected (consisting of sixteen more extraverted and sixteen less extraverted individuals) and evaluated with the Eysenck's Personality Inventory (EPI) and Big Five Inventory (BFI) tools. EEG alpha oscillations in the occipital region were measured to investigate extraversion personality trait correlates of eye contact during a face-to-face interaction task and an eyes-open condition. The results revealed that the extraversion personality trait has a significant positive correlation with EEG alpha coherence in the occipital region, presumably due to its relationship with eye contact during the interaction task. Furthermore, the decrease in EEG alpha power during the interaction task compared to the eyes-open condition was found to be greater in the less extraverted participants; however, no significant difference was observed between the less and more extraverted participants. Overall, these findings encourage further research towards the understanding of neural mechanism correlates of the extraversion personality trait-particularly in social interaction.

Classification of Visual and Non-visual Learners Using Electroencephalographic Alpha and Gamma Activities

This study analyzes the learning styles of subjects based on their electroencephalo-graphy (EEG) signals. The goal is to identify how the EEG features of a visual learner differ from those of a non-visual learner. The idea is to measure the students' EEGs during the resting states (eyes open and eyes closed conditions) and when performing learning tasks. For this purpose, 34 healthy subjects are recruited. The subjects have no background knowledge of the animated learning content. The subjects are shown the animated learning content in a video format. The experiment consists of two sessions and each session comprises two parts: (1) Learning task: the subjects are shown the animated learning content for an 8-10 min duration. (2) Memory retrieval task The EEG signals are measured during the leaning task and memory retrieval task in two sessions. The retention time for the first session was 30 min, and 2 months for the second session. The analysis is performed for the EEG measured during the memory retrieval tasks. The study characterizes and differentiates the visual learners from the non-visual learners considering the extracted EEG features, such as the power spectral density (PSD), power spectral entropy (PSE), and discrete wavelet transform (DWT). The PSD and DWT features are analyzed. The EEG PSD and DWT features are computed for the recorded EEG in the alpha and gamma frequency bands over 128 scalp sites. The alpha and gamma frequency band for frontal, occipital, and parietal regions are analyzed as these regions are activated during learning. The extracted PSD and DWT features are then reduced to 8 and 15 optimum features using principal component analysis (PCA). The optimum features are then used as an input to the k-nearest neighbor (k-NN) classifier using the Mahalanobis distance metric, with 10-fold cross validation and support vector machine (SVM) classifier using linear kernel, with 10-fold cross validation. The classification results showed 97% and 94% accuracies rate for the first session and 96% and 93% accuracies for the second session in the alpha and gamma bands for the visual learners and non-visual learners, respectively, for k-NN classifier for PSD features and 68% and 100% accuracies rate for first session and 100% accuracies rate for second session for DWT features using k-NN classifier for the second session in the alpha and gamma band. For PSD features 97% and 96% accuracies rate for the first session, 100% and 95% accuracies rate for second session using SVM classifier and 79% and 82% accuracy for first session and 56% and 74% accuracy for second session for DWT features using SVM classifier. The results showed that the PSDs in the alpha and gamma bands represent distinct and stable EEG signatures for visual learners and non-visual learners during the retrieval of the learned contents.

Pulse waveform analysis on temporal changes in ocular blood flow due to caffeine intake: a comparative study between habitual and non-habitual groups

PURPOSE

To evaluate and compare the temporal changes in pulse waveform parameters of ocular blood flow (OBF) between non-habitual and habitual groups due to caffeine intake.

METHOD

This study was conducted on 19 healthy subjects (non-habitual 8; habitual 11), non-smoking and between 21 and 30 years of age. Using laser speckle flowgraphy (LSFG), three areas of optical nerve head were analyzed which are vessel, tissue, and overall, each with ten pulse waveform parameters, namely mean blur rate (MBR), fluctuation, skew, blowout score (BOS), blowout time (BOT), rising rate, falling rate, flow acceleration index (FAI), acceleration time index (ATI), and resistive index (RI). Two-way mixed ANOVA was used to determine the difference between every two groups where p < 0.05 is considered significant.

RESULT

There were significant differences between the two groups in several ocular pulse waveform parameters, namely MBR (overall, vessel, tissue), BOT (overall), rising rate (overall), and falling rate (vessel), all with p < 0.05. In addition, the ocular pulse waveform parameters, i.e., MBR (overall), skew (tissue), and BOT (tissue) showed significant temporal changes within the non-habitual group, but not within the habitual group. The temporal changes in parameters MBR (vessel, tissue), skew (overall, vessel), BOT (overall, vessel), rising rate (overall), falling rate (overall, vessel), and FAI (tissue) were significant for both groups (habitual and non-habitual) in response to caffeine intake.

CONCLUSION

The experiment results demonstrated caffeine does modulate OBF significantly and response differently in non-habitual and habitual groups. Among all ten parameters, MBR and BOT were identified as the suitable biomarkers to differentiate between the two groups.

Removal of BCG artefact from concurrent fMRI-EEG recordings based on EMD and PCA

BACKGROUND

Simultaneous electroencephalography (EEG) and functional magnetic resonance image (fMRI) acquisitions provide better insight into brain dynamics. Some artefacts due to simultaneous acquisition pose a threat to the quality of the data. One such problematic artefact is the ballistocardiogram (BCG) artefact.

METHODS

We developed a hybrid algorithm that combines features of empirical mode decomposition (EMD) with principal component analysis (PCA) to reduce the BCG artefact. The algorithm does not require extra electrocardiogram (ECG) or electrooculogram (EOG) recordings to extract the BCG artefact.

RESULTS

The method was tested with both simulated and real EEG data of 11 participants. From the simulated data, the similarity index between the extracted BCG and the simulated BCG showed the effectiveness of the proposed method in BCG removal. On the other hand, real data were recorded with two conditions, i.e. resting state (eyes closed dataset) and task influenced (event-related potentials (ERPs) dataset). Using qualitative (visual inspection) and quantitative (similarity index, improved normalized power spectrum (INPS) ratio, power spectrum, sample entropy (SE)) evaluation parameters, the assessment results showed that the proposed method can efficiently reduce the BCG artefact while preserving the neuronal signals.

COMPARISON WITH EXISTING METHODS

Compared with conventional methods, namely, average artefact subtraction (AAS), optimal basis set (OBS) and combined independent component analysis and principal component analysis (ICA-PCA), the statistical analyses of the results showed that the proposed method has better performance, and the differences were significant for all quantitative parameters except for the power and sample entropy.

CONCLUSIONS

The proposed method does not require any reference signal, prior information or assumption to extract the BCG artefact. It will be very useful in circumstances where the reference signal is not available.

Automated microaneurysm detection in diabetic retinopathy using curvelet transform

Microaneurysms (MAs) are known to be the early signs of diabetic retinopathy (DR). An automated MA detection system based on curvelet transform is proposed for color fundus image analysis. Candidates of MA were extracted in two parallel steps. In step one, blood vessels were removed from preprocessed green band image and preliminary MA candidates were selected by local thresholding technique. In step two, based on statistical features, the image background was estimated. The results from the two steps allowed us to identify preliminary MA candidates which were also present in the image foreground. A collection set of features was fed to a rule-based classifier to divide the candidates into MAs and non-MAs. The proposed system was tested with Retinopathy Online Challenge database. The automated system detected 162 MAs out of 336, thus achieved a sensitivity of 48.21% with 65 false positives per image. Counting MA is a means to measure the progression of DR. Hence, the proposed system may be deployed to monitor the progression of DR at early stage in population studies.

Machine Learning to Differentiate Between Positive and Negative Emotions Using Pupil Diameter

Pupil diameter (PD) has been suggested as a reliable parameter for identifying an individual's emotional state. In this paper, we introduce a learning machine technique to detect and differentiate between positive and negative emotions. We presented 30 participants with positive and negative sound stimuli and recorded pupillary responses. The results showed a significant increase in pupil dilation during the processing of negative and positive sound stimuli with greater increase for negative stimuli. We also found a more sustained dilation for negative compared to positive stimuli at the end of the trial, which was utilized to differentiate between positive and negative emotions using a machine learning approach which gave an accuracy of 96.5% with sensitivity of 97.93% and specificity of 98%. The obtained results were validated using another dataset designed for a different study and which was recorded while 30 participants processed word pairs with positive and negative emotions.

Image Completion: Survey and Comparative Study

Image completion is an active and interesting research area in image processing and computer graphics. Restoration and retouching of damaged areas in an undetectable form is the objective of image completion techniques. Most of the recently developed video completion methods are extensions of image completion techniques to restore the damaged frames. With respect to video completion challenges and image completion future work, we survey existing methods and introduce a new classification. The methods in each category are described in detail. In the second part of the paper, we provide a comparison and evaluation study between the most recent image completion methods qualitatively as well as quantitatively. For a fair comparison, we introduced a new dataset and evaluated four available image completion methods on the same hardware. Experimental results are conducted to highlight the strengths and drawbacks of each image completion method.