Convolutional Neural Network for Segmentation and Classification of Glaucoma

Glaucoma is an eye disease that is caused by elevated intraocular pressure and commonly leads to optic nerve damage. Thanks to its vital role in transmitting visual signals from the eye to the brain, the optic nerve is essential for maintaining good and clear vision. Glaucoma is considered one of the leading causes of blindness. Accordingly, the earlier doctors can diagnose and detect the disease, the more feasible its treatment becomes. Aiming to facilitate this task, this study proposes a method for detecting diseases by analyzing images of the interior of the eye using a convolutional neural network. This method consists of segmentation based on a modified U-Net architecture and classification using the DenseNet-201 technique. The proposed model utilized the DRISHTI-GS and RIM-ONE datasets to evaluate glaucoma images. These datasets served as valuable sources of diverse and representative glaucoma-related images, enabling a thorough evaluation of the model’s performance. Finally, the results were highly promising after subjecting the model to a thorough evaluation process. The segmentation accuracy reached 96.65%, while the classification accuracy reached 96.90%. This means that the model excelled in accurately delineating and isolating the relevant regions of interest within the eye images, such as the optical disc and optical cup, which are crucial for diagnosing glaucoma

Health Vigilance for Medical Imaging Diagnostic Optimization: Automated segmentation of COVID-19 lung infection from CT images

Covid-19 disease has confronted the world with an unprecedented health crisis, faced with its quick spread, the health system is called upon to increase its vigilance. So, it is essential to set up a quick and automated diagnosis that can alleviate pressure on health systems. Many techniques used to diagnose the covid-19 disease, including imaging techniques, like computed tomography (CT). In this paper, we present an automatic method for COVID-19 Lung Infection Segmentation from CT Images, that can be integrated into a decision support system for the diagnosis of covid-19 disease. To achieve this goal, we focused to new techniques based on artificial intelligent concept, in particular the uses of deep convolutional neural network, and we are interested in our study to the most popular architecture used in the medical imaging community based on encoder-decoder models. We use an open access data collection for Artificial Intelligence COVID-19 CT segmentation or classification as dataset, the proposed model implemented on keras framework in python. A short description of model, training, validation and predictions is given, at the end we compare the result with an existing labeled data. We tested our trained model on new images, we obtained for Area under the ROC Curve the value 0.884 from the prediction result compared with manual expert segmentation. Finally, an overview is given for future works, and use of the proposed model into homogeneous framework in a medical imaging context for clinical purpose

La coagulation intravasculaire disséminée: intérêt du score de la société internationale sur la thrombose et l´hémostase

La coagulation intravasculaire disséminée (CIVD) est une cause de mortalité redoutable en milieu de réanimation. L´utilisation du système de score de la société internationale sur la thrombose et l´hémostase (ISTH) permet de faciliter le diagnostic précoce de la CIVD. Nous présentons trois observations cliniques de CIVD d´étiologies différentes: un adénocarcinome prostatique, un choc septique et un hématome rétro-placentaire. Les tests d´hémostase nécessaires au calcul du score de la Société Internationale sur la Thrombose et l´Hémostase (ISTH) (numération plaquettaire, taux de prothrombine, fibrinogène et D-dimères) ont été régulièrement réalisés. D´autres tests complémentaires (recherche de complexes solubles, test de lyse des euglobulines, dosage des taux d´antithrombine, de protéine C activée et du facteur V) ont été également réalisés. L´utilisation du score ISTH permet de faciliter le diagnostic précoce de la CIVD

Vigilance towards the use of artificial intelligence applications for breast cancer screening and early diagnosis

Breast cancer is a real public health problem in Morocco. It is the cause of a significant number of deaths caused by late diagnosis. Mammography plays an essential role in the detection of breast cancer and in the early management of its treatment. Despite the existence of screening programs, there are still high rates of false positives and false negatives. Indeed, women were called back for additional diagnoses based on suspicious results that eventually led to cancer. Artificial intelligence (AI) algorithms represent a promising solution to improve the accuracy of digital mammography offering, on the one hand, the possibility of better cancer detection, and, on the other hand, improved efficiency for radiologists for good decision-making. In this work, through a review of the literature on the tools used to evaluate the performance of AI systems dedicated to early detection and diagnosis of breast cancer. We set out to answer the following questions: Is the ethics relating to patient data during the development phase of this software is respected? Do these tools take into consideration the specificities of the field? What about the specification, accuracy and limitations of these applications? At the end, we show through this work recommendations to adapt these evaluation tools of AI applications for breast cancer screening for an optimized and rational consideration of the principle of health vigilance and compliance with the regulatory standards in force governing this field

Association between an angiotensin-converting enzyme gene polymorphism and Alzheimer’s disease in a Tunisian population

Abstract Background The angiotensin-converting enzyme gene (ACE) insertion/deletion (I/D or indel) polymorphism has long been linked to Alzheimer’s disease (AD), but the interpretation of established data remains controversial. The aim of this study was to determine whether the angiotensin-converting enzyme is associated with the risk of Alzheimer’s disease in Tunisian patients. Methods We analyzed the genotype and allele frequency distribution of the ACE I/D gene polymorphism in 60 Tunisian AD patients and 120 healthy controls. Results There is a significantly increased risk of AD in carriers of the D/D genotype (51.67% in patients vs. 31.67% in controls; p = .008, OR = 2.32). The D allele was also more frequently found in patients compared with controls (71.67% vs. 56.25%; p = .003, OR = 2.0). Moreover, as assessed by the Mini-Mental State Examination, patient D/D carriers were more frequently found to score in the severe category of dementia (65%) as compared to the moderate category (32%) or mild category (3%). Conclusions The D/D genotype and D allele of the ACE I/D polymorphism were associated with an increased risk in the development of AD in a Tunisian population. Furthermore, at the time of patient evaluation (average age 75 years), patients suffering with severe dementia were found predominantly in D/D carriers and, conversely, the D/D genotype and D allele were more frequently found in AD patients with severe dementia. These preliminary exploratory results should be confirmed in larger studies and further work is required to explore and interpret possible alternative findings in diverse populations

-174G>C interleukin-6 gene polymorphism in Tunisian patients with coronary artery disease

<b>Background and Objectives :</b> A state of low-grade inflammation accompanies the pathogenesis of atherosclerotic events. Interleukin-6 (IL-6) is a pleotropic pro-inflammatory cytokine that modulates the development of acute coronary syndromes (ACSs), partly by destabilizing coronary atherosclerotic plaques. We have examined the contribution of the -174G&gt;C IL-6 promoter variant on the risk of coronary artery disease (CAD) among Tunisians. <b>Patients and Methods :</b> Study subjects included 418 CAD patients and 406 age- and sex-matched controls. IL-6 genotyping was done by PCR-restriction fragment length polymorphism. <b>Results</b> : The frequency of the -174C allele (mutant) was lower in Tunisians than in Europeans, and the distribution of -174 G&gt;C genotypes was similar between CAD patients and control subjects. Moreover, compared to GG genotype carriers, -174C allele carriage did not increase the CAD relative risk (odds ratio and 95&#x0025; confidence interval=1.09 and 0.80-1.49), which remained nonsignificant after adjusting for traditional risk factors for CAD (age, smoking, hypertension, diabetes and obesity). <b>Conclusion</b> : The -174G&gt;C IL-6 promoter variant is not associated with an increased risk of CAD among Tunisians

Identifying Retinal Diseases on OCT Image Based on Deep Learning

Computer-aided diagnosis has the potential to replace or at least support medical personnel in their everyday responsibilities such as diagnosis, therapy, and surgery. In the area of ophthalmology, artificial intelligence approaches have been incorporated in the diagnosis of the most frequent ocular disorders, such as choroidal neovascularization (CNV), diabetic macular oedema (DMO), and DRUSEN; these illnesses pose a significant risk of vision loss. Optical coherence tomography (OCT) is an imaging technology used to diagnose the aforementioned eye disorders. It enables ophthalmologists to see the back of the eye and take various slices of the retina. The goal of this research is to automate the diagnosis of retinopathy, which includes CNV, DME, and DRUSEN. The approach employed is a deep learning-based, and transfer learning technique, applying to a public dataset of OCT pictures and two pertained neural network models VGG16 and InceptionV3, which are trained on the big database "ImageNet." That allows them to be able to extract the main features of millions of images. Furthermore, fine-tuning approaches are applied to outperform the feature extraction method, by modifying the hyperparameters. The findings showed that the VGG16 model performed better in classification than the InceptionV3 architecture, with a 0.93 accuracy

–308G&#62;A and –1031T&#62;C tumor necrosis factor gene polymorphisms in Tunisian patients with coronary artery disease

Background: Recent research has shown that inflammation plays a key role in coronary artery disease (CAD) and other manifestations of atherosclerosis. Several lines of evidence support a key role for tumor necrosis factor-α (TNF-α), a potent immunomodulator and pro-inflammatory cytokine, in the development of atherosclerosis and in complications of CAD. Methods: We investigated the possible association between CAD and the TNF gene promoter polymorphisms –308G&#62;A and –1031T&#62;C in a Tunisian population. We compared the distribution of these polymorphisms between 418 patients with CAD and 406 healthy controls using polymerase chain reaction restriction fragment length-polymorphism analysis. Results: The frequency of the TNF-α –308A allele in the control group was similar to that observed in CAD patients [p=0.78; odds ratio (OR)=1.15; 95% confidence interval (CI)=0.86–1.55], but higher than those described in other Europeans, such as in the French, Finnish and Spanish. Concerning the TNF-α –1031T/C polymorphism, the same distribution was observed between patients with CAD and controls (p=0.12; OR=1.27; 95% CI=0.94–1.72). In addition, the genotype and allele frequencies of control individuals were comparable to those previously reported in healthy Tunisian controls and other ethnic groups. Haplotype analysis (TNF-α –308G&#62;A and –1031T&#62;C) demonstrated no significant association between TNF haplotypes and CAD. Conclusions: We conclude that TNF promoter gene polymorphisms at position –308G&#62;A and –1031T&#62;C do not play a major role in the pathogenesis of CAD in the Tunisian population. Clin Chem Lab Med 2009;47:1247–51.Peer Reviewe

Neuro-meningeal cryptococcal infection revealing a multiple myeloma

Rare cases of Cryptococcus have been documented in patients living with multiple myeloma. To date there has been no documented evidence of cryptococcosis revealing multiple myeloma. We reported a 63-year-old man who had a 2-months history continuous holocranial headaches, morning vomiting, complaining of blurred vision and fever. The biologic and the imaging showed a Cryptococcus meningoencephalitis. The search for a cause of immunodeficiency revealed a multiple myeloma. The diagnosis for Cryptococcus was confirmed according to an India ink stain, blood and cerebrospinal fluid culture. The patient's treatment for multiple myeloma was initiated with a chemotherapy regimen. The evolution was good without complication. Cryptococcosis, especially in the neuro-meningeal form, is a serious, deadly opportunistic infection. The search of an underlining immunodeficiency must be systematic. In this case, it was associated with early stage multiple myeloma