ABSTRACT
OBJECTIVE: In epilepsy, early diagnosis, accurate determination of epilepsy type, proper selection of antiseizure medication, and monitoring are all essential. However, despite recent therapeutic advances and conceptual reconsiderations in the classification and management of epilepsy, serious gaps are still encountered in day-to-day practice in Egypt as well as several other resource-limited countries. Premature mortality, poor quality of life, socio-economic burden, cognitive problems, poor treatment outcomes, and comorbidities are major challenges that require urgent actions to be implemented at all levels. In recognition of this, a group of Egyptian epilepsy experts met through a series of consecutive meetings to specify the main concepts concerning the diagnosis and management of epilepsy, with the ultimate goal of establishing a nationwide Egyptian consensus. METHODS: The consensus was developed through a modified Delphi methodology. A thorough review of the most recent relevant literature and international guidelines was performed to evaluate their applicability to the Egyptian situation. Afterward, several remote and live rounds were scheduled to reach a final agreement for all listed statements. RESULTS: Of 278 statements reviewed in the first round, 256 achieved ≥80% agreement. Live discussion and refinement of the 22 statements that did not reach consensus during the first round took place, followed by final live voting then consensus was achieved for all remaining statements. SIGNIFICANCE: With the implementation of these unified recommendations, we believe this will bring about substantial improvements in both the quality of care and treatment outcomes for persons with epilepsy in Egypt. PLAIN LANGUAGE SUMMARY: This work represents the efforts of a group of medical experts to reach an agreement on the best medical practice related to people with epilepsy based on previously published recommendations while taking into consideration applicable options in resource-limited countries. The publication of this document is expected to minimize many malpractice issues and pave the way for better healthcare services on both individual and governmental levels.
ABSTRACT
Skin cancer, specifically melanoma, is a serious health issue that arises from the melanocytes, the cells that produce melanin, the pigment responsible for skin color. With skin cancer on the rise, the timely identification of skin lesions is crucial for effective treatment. However, the similarity between some skin lesions can result in misclassification, which is a significant problem. It is important to note that benign skin lesions are more prevalent than malignant ones, which can lead to overly cautious algorithms and incorrect results. As a solution, researchers are developing computer-assisted diagnostic tools to detect malignant tumors early. First, a new model based on the combination of "you only look once" (YOLOv5) and "ResNet50" is proposed for melanoma detection with its degree using humans against a machine with 10,000 training images (HAM10000). Second, feature maps integrate gradient change, which allows rapid inference, boosts precision, and reduces the number of hyperparameters in the model, making it smaller. Finally, the current YOLOv5 model is changed to obtain the desired outcomes by adding new classes for dermatoscopic images of typical lesions with pigmented skin. The proposed approach improves melanoma detection with a real-time speed of 0.4 MS of non-maximum suppression (NMS) per image. The performance metrics average is 99.0%, 98.6%, 98.8%, 99.5, 98.3%, and 98.7% for the precision, recall, dice similarity coefficient (DSC), accuracy, mean average precision (MAP) from 0.0 to 0.5, and MAP from 0.5 to 0.95, respectively. Compared to current melanoma detection approaches, the provided approach is more efficient in using deep features.
