RESUMO
Diagnosing endometrial carcinoma correctly is essential for appropriate treatment, as it is a major health risk. As machine learning (ML) and artificial intelligence (AI) have grown in popularity, so has interest in their potential to improve cancer diagnosis accuracy. In the context of endometrial cancer, this study attempts to examine the efficacy as well as the accuracy of AI-assisted diagnostic approaches. Additionally, it aims to methodically evaluate the contribution of AI and ML techniques to the improvement of endometrial cancer diagnosis. Following PRISMA guidelines, we performed a thorough search of numerous databases, including Medline via Ovid, PubMed, Scopus, Web of Science, and Google Scholar. Ten years were searched, encompassing both basic and advanced research. Peer-reviewed papers and original research studies that explicitly looked at the application of AI/ML in endometrial cancer diagnosis were the main targets of the well-defined selection criteria. Using the Critical Appraisal Skills Programme (CASP) methodology, two independent researchers conducted a thorough screening process and quality assessment of included studies. The review found a notable inclination towards the effective use of AI in endometrial carcinoma diagnostics, namely in the identification and categorization of endometrial cancer. Artificial intelligence models, particularly Convolutional Neural Networks (CNNs) and deep learning algorithms have shown remarkable precision in detecting endometrial cancer. They frequently achieve or even exceed the diagnostic proficiency of human specialists. The use of artificial intelligence in medical diagnostics signifies revolutionary progress in the field of oncology. AI-assisted diagnostic tools have demonstrated the potential to improve the precision and effectiveness of cancer diagnosis, namely in cases of endometrial carcinoma. This innovation not only enhances the quality of patient care but also indicates a transition towards more individualized and efficient treatment approaches in the field of oncology. The advancement of AI technology is expected to play a crucial role in medical diagnostics, particularly in the field of cancer detection and treatment, perhaps leading to a significant transformation in the approach to these areas.
RESUMO
Introduction Severe uncontrolled asthma is challenging to manage and impacts lung function and symptoms. Biologic agents targeting inflammatory pathways have transformed asthma management. This retrospective chart review aimed to assess biologic therapy in severe uncontrolled asthma patients and evaluate outcomes. Methods The study analyzed medical records of 30 patients receiving biologic therapy for severe asthma at a tertiary care center in Peshawar, Pakistan, from December 2022 to Jun 2023. Ethical approval was obtained, and patient demographics, biologic agent usage, and clinical parameters were collected. Clinical outcomes were evaluated after six months, including forced expiratory volume in the first second (FEV1), eosinophil count, IgE levels, and exacerbation rates. Results After six months, biologic treatment significantly improved FEV1 (48.7% to 62.4%), reduced eosinophils (540 cells/µL to 290 cells/µL) and IgE levels (410 IU/mL to 280 IU/mL), and decreased exacerbations (4.6 to 1.9). Subgroup analysis based on age and sex showed consistent lung function improvements. Conclusion Biologic agents effectively targeted inflammatory pathways, improving asthma control in severe uncontrolled asthma patients. This study provides valuable insights into biologic therapy for severe asthma, offering new possibilities for patient outcomes. Larger studies are needed to validate findings and optimize personalized treatment strategies.