PurposeAppendicitis is the most common abdominal inflammatory process in children which were sometimes followed by complications including intra-abdominal abscess. This later needs classically a surgical drainage. We evaluated the efficacy of antibiotic treatment and surgical drainage.MethodsHospital records of children treated in our unit for intra-abdominal post appendectomy abscesses over a 6 years period were reviewed retrospectively.ResultsThis study investigates a series of 14 children from 2 to 13 years of age with one or many abscesses after appendectomy, treated between 2002 and 2007. Seven underwent surgery and the others were treated with triple antibiotherapy. The two groups were comparable.For the 7 patients who receive medical treatment alone, it was considered efficient in 6 cases (85%) with clinical, biological and radiological recovery of the abscess. There was one failure (14%). The duration of hospitalization from the day of diagnosis of intra-abdominal abscess was approximately 10.28 days (range 7 to 14 days). In the other group, the efficacy of treatment was considered satisfactory in all cases. The duration of hospitalization was about 13 days (range: 9 to 20).ConclusionCompared to surgical drainage, antibiotic management of intra-abdominal abscesses was a no invasive treatment with shorter hospitalization.

Coronavirus 2019 (COVID-19) is a highly transmissible and pathogenic virus caused by severe respiratory syndrome coronavirus 2 (SARS-CoV-2), which first appeared in Wuhan, China, and has since spread in the whole world. This pathology has caused a major health crisis in the world. However, the early detection of this anomaly is a key task to minimize their spread. Artificial intelligence is one of the approaches commonly used by researchers to discover the problems it causes and provide solutions. These estimates would help enable health systems to take the necessary steps to diagnose and track cases of COVID. In this review, we intend to offer a novel method of automatic detection of COVID-19 using tomographic images (CT) and radiographic images (Chest X-ray). In order to improve the performance of the detection system for this outbreak, we used two deep learning models: the VGG and ResNet. The results of the experiments show that our proposed models achieved the best accuracy of 99.35 and 96.77% respectively for VGG19 and ResNet50 with all the chest X-ray images.

Infectious diseases pose a threat to human life and could affect the whole world in a very short time. Corona-2019 virus disease (COVID-19) is an example of such harmful diseases. COVID-19 is a pandemic of an emerging infectious disease, called coronavirus disease 2019 or COVID-19, caused by the coronavirus SARS-CoV-2, which first appeared in December 2019 in Wuhan, China, before spreading around the world on a very large scale. The continued rise in the number of positive COVID-19 cases has disrupted the health care system in many countries, creating a lot of stress for governing bodies around the world, hence the need for a rapid way to identify cases of this disease. Medical imaging is a widely accepted technique for early detection and diagnosis of the disease which includes different techniques such as Chest X-ray (CXR), Computed Tomography (CT) scan, etc. In this paper, we propose a methodology to investigate the potential of deep transfer learning in building a classifier to detect COVID-19 positive patients using CT scan and CXR images. Data augmentation technique is used to increase the size of the training dataset in order to solve overfitting and enhance generalization ability of the model. Our contribution consists of a comprehensive evaluation of a series of pre-trained deep neural networks: ResNet50, InceptionV3, VGGNet-19, and Xception, using data augmentation technique. The findings proved that deep learning is effective at detecting COVID-19 cases. From the results of the experiments it was found that by considering each modality separately, the VGGNet-19 model outperforms the other three models proposed by using the CT image dataset where it achieved  88.5% precision, 86% recall, 86.5% F1-score, and 87% accuracy while the refined Xception version gave the highest precision, recall, F1-score, and accuracy values which equal 98% using CXR images dataset. On the other hand, and by applying the average of the two modalities X-ray and CT, VGG-19 presents the best score which is 90.5% for the accuracy and the F1-score, 90.3% for the recall while the precision is 91.5%. These results enables to automatize the process of analyzing chest CT scans and X-ray images with high accuracy and can be used in cases where RT-PCR testing and materials are limited.