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

Quantitation of venom Antigens from Moroccan vipers in serum by using an Enzyme-Linked Immunosorbent Assay (ELISA) toward improving health vigilance systems

In the present study an ELISA assay was developed and validated for detection and determination of the concentration of snakes venom in biological samples. Individual component of each venom (Cerastes cerastes and Macrovipera mauretanica) used as immunogen to raise specific rabbit IgGs in order to set up a sandwich-type ELISA. Lower detection limit, linearity, accuracy, precision, reproducibility, and reference intervals were determined. The method proved to be simple, specific, reproducible, sensitive (detection limit = 0.5 ng/ml) and the calibration plot was based on linear regression analysis (r = 0.980) between 0.9 and 1000 ng/mL of venom concentration, with a lower limit of quantification of 1.58 ng/mL. The intra- and interassay coefficient of variation ranged from 2,02 to 4.62% and 5.29 to 7.40%, respectively. The specificity of the assay was tested using vipers, cobra and scorpion venom. This method detected venom from all viper species tested without significant cross reactivity with other venoms in the concentration range of 0.9–1000 ng/mL. This ELISA described is sufficiently validated for clinical evaluation. The method is adaptable to other venoms. This is potentially useful for clinical diagnosis of snakebite, to monitor antivenom dose, and consequently to improve the national health monitoring systems

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

Development and implementation of a system for medical devices monitoring in Morocco

Given the importance of medical devices in improving health, a system of monitoring their use is necessary to ensure an acceptable benefit/risk ratio. The present study focuses on the post-marketing monitoring system, of which the aimis to develop, a national strategy for the establishment of a multidimensional vigilance system to monitor medical devices in Morocco. Methods : The study is based on a systemic review selected by the PRISMA method for the period between [2011-2021] and on the Scopus, Pubmed, Science direct and Web of science databases. Results: A preliminary analysis of the data identified some challenges such as under-reporting and lack of standardization of adverse reaction coding, standard nomenclature problem for international trade, lack of clarity of requirements for manufacturers, and insufficient regulation and significant incentives for the use of unique device identifiers. Recommendations for a more effective national system are put for ward which address the regulation and computerization of the system for the development of medical devices monitoring mechanisms

Development of stability indicating method for quality assessment of African Albendazole tablets

In order to assess the quality of Albendazole tablets (Alb) sampled in three countries from West Africa, several physical and chemical tests were performed on tablets at normal conditions. A simple and economic HPLC method has been developed, validated and used for the simultaneous determination of Albendazole (Alb) content, as well as its impurities and the uniformity of its content. The stability-indicating HPLC method was performed on a Symmetry C18-5µm 250 mm × 4.6 mm column with a gradient elution using a mobile phase composed of acetonitrile and sodium acetate buffer. The flow rate was set at 1 mL.min−1 and the eluent was monitored at 295nm. The method was validated for specificity, linearity, accuracy, precision, robustness and detection and quantification limits, in accordance with International Conference on Harmonisation Quality 2 (ICH Q2) guidelines. This method was performed on different Alb samples (originals and generics) products collected from Senegal, Niger and Mali. The obtained results showed that, the contents of the generic tablets from Niger and Mali comply with the United States Pharmacopeia (USP) monograph acceptance criteria. However, more than 20% of the generic tablets don’t meet the USP monograph impurity limits. In conclusion, the described analytical method is simple, sensitive and accurate. Thus, it could be useful for manufacturing and quality control assays

Using in vitro tensile strength test to monitoring quality and effectiveness of suture in the oral environment

Sutures are medical devices used in surgery. They serve as tissues stabilizers in contact with or near to the surgical site without compromising the healing process. They must keep their physical properties for the necessary time, in particular tensile strength. In view of the wide variety of references offered by all specialtys combined, which supply sutures with all materials described, whose use is indicated for all surgical procedures. The objective of this work is to evaluate the tensile strength of absorbable and non-absorbable sutures for a period of 10 to 28 days under conditions simulated by the oral route. 5 sutures materials were tested with a metric diameter of 1.5 and 4.The tensile strength test was used according to the protocol of the European Pharmacopoeia (Eur.Ph.9.5). 5 fragments of each material were measured before and after their immersion in Artificial Saliva (AS). In AS, the Polypropylene suture significantly maintained (p = 5%) its tensile strength compared to that of Polyamide. For absorbable sutures, a loss of more than 70% of their initial strength was marked on the 7th day of immersion. In view of the results obtained, during oral surgical operations, the material of choice is in favor of Propylene

Radiodiagnostic Equipment: Regulations and Materiovigilance

The use of medical devices (MDs) in the field of medical imaging has always been governed by rigorous regulations, in particular the authorizations and compliance of radiological installations and premises in view of the risks generated by the ionizing radiation produced by these MDs. The regulatory bases that deal with equipment emitting ionizing radiation are diversified between those specific to the protection of the public and users of ionizing radiation and those relating to medical devices. In addition, radio-diagnostic equipment must provide all the guarantees in terms of the balance between benefits and risks. Although radiation protection is essential, materiovigilance is one of the key elements of technological monitoring and surveillance of the risks that may result from the use of these medical devices after they have been placed on the market. The Moroccan legislation has a legal arsenal in accordance with the model of the World Health Organization’s global regulatory framework for medical devices. It outlines regulations and adheres to international guidelines in the field of vigilance against ionizing radiation. However, it is necessary to move on to the specification of procedures in order to remove any ambiguity

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

Toxicovigilance: the misuse of psychotropic drugs in Morocco. Results of a survey conducted in Casablanca

In Morocco, there are very few studies on the use of drugs and even less on psychotropic medicines (MPSYC). In this study we identified the misuse of MPSYC and their sources of supply in order to determine their modes of consumption and also assess the consumers health status. Methods. An anonymous survey of 500 MPSYC consumers was conducted in Casablanca. Data entry and statistical analysis were carried out using SPSS 25.0 software.Results: 500 participants in the survey were poly-drug users of MPSYC with an average of 4.13 ± 0.007 (± SEM) drugs per user. The most misused MPSYC are Clonazepam, Diazepam, Nordazepam and Tramadol with respective prevalences of 96.80%, 82%, 55.60% and 47.80%. A statistical analysis showed that clonazepam (p=0.047) and tramadol (p=0.005) are prevalent in the street market. 54.20% of survey participants use MPSYC once a week while 39.80% of them use it twice a week. The same statistical analysis revealed that taking several MPSYC lead to an increase of ingested doses (p<0.001) and alcohol consumption (p=0.003). 96.40% (n=482) of the participants declared that they had experienced discomfort misusing the medicine