Deep learning enhances acute lymphoblastic leukemia diagnosis and classification using bone marrow images.

Acute lymphoblastic leukemia (ALL) poses a significant health challenge, particularly in pediatric cases, requiring precise and rapid diagnostic approaches. This comprehensive review explores the transformative capacity of deep learning (DL) in enhancing ALL diagnosis and classification, focusing on bone marrow image analysis. Examining ten studies conducted between 2013 and 2023 across various countries, including India, China, KSA, and Mexico, the synthesis underscores the adaptability and proficiency of DL methodologies in detecting leukemia. Innovative DL models, notably Convolutional Neural Networks (CNNs) with Cat-Boosting, XG-Boosting, and Transfer Learning techniques, demonstrate notable approaches. Some models achieve outstanding accuracy, with one CNN reaching 100% in cancer cell classification. The incorporation of novel algorithms like Cat-Swarm Optimization and specialized CNN architectures contributes to superior classification accuracy. Performance metrics highlight these achievements, with models consistently outperforming traditional diagnostic methods. For instance, a CNN with Cat-Boosting attains 100% accuracy, while others hover around 99%, showcasing DL models' robustness in ALL diagnosis. Despite acknowledged challenges, such as the need for larger and more diverse datasets, these findings underscore DL's transformative potential in reshaping leukemia diagnostics. The high numerical accuracies accentuate a promising trajectory toward more efficient and accurate ALL diagnosis in clinical settings, prompting ongoing research to address challenges and refine DL models for optimal clinical integration.

Evaluation of growth in low-body-weight kidney transplant Egyptian children: 25-year experience.

BACKGROUND: Identification of problems associated with kidney transplantation in low-body-weight children is an essential step toward improving graft function and patient survival as well as quality of life. PATIENTS AND METHODS: This study comprised 63 renal transplant children weighing 25 kg or less at time of renal transplantation. All children received a living donor renal allotransplant between December 1984 and March 2009. These children were retrospectively evaluated regarding their survival, graft survival as well as physical growth. RESULTS: Our patient and graft survival rates at 1, 5 and 10 years were 98.4%, 96.8% and 96.8%, and 94.9%, 82.6% and 58.4%, respectively. Significant risk factors for growth retardation post renal transplant were identified and included older age at time of transplant (p=0.019), female sex (p=0.010), retarded growth at time of transplant (p=0.011, by univariate analysis, and p=0.028, by multivariate analysis), incidence of chronic rejection (p=0.012), higher steroid cumulative dose (p=0.013) and graft dysfunction (p=0.009, by multivariate analysis). CONCLUSION: The current final height of low-body-weight transplant Egyptian children has remained suboptimal. The management of growth retardation posttransplant is multifactorial and should start early before transplantation, with optimal care of growth in children with chronic kidney disease. Moreover, expedited transplantation, whenever indicated, and optimization of posttransplant graft function with minimal steroid exposure are essential factors which were shown to be possible using immunosuppression based on tacrolimus plus mycophenolate mofetil, after basiliximab induction.