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
BACKGROUND: Arthroscopy of the knee is preferably done under spinal anaesthesia. The optimal analgesia for effective postoperative pain control is important to permit early discharge, comfort and mobility of the patient. Objective of the study is to assess the efficacy of ketorolac and lignocaine administered intra-articularly for postoperative pain following knee arthroscopic surgery. METHODS: A total of 133 patients were randomized into two groups with one group receiving intra-articular Ketorolac and the other group receiving intra-articular Lignocaine. Postoperative pain was then assessed using the Visual Analog Scale (VAS) at 4, 8, 12 and 24 hours after surgery. RESULTS: Both the groups had effective analgesia at 4 hours. The best analgesia was seen in the group that received Ketorolac Intra-articularly and it was found statistically significant. CONCLUSIONS: Administration of intra-articular Ketorolac injection is safe and effective way of achieving postoperative pain relief after arthroscopic knee surgery.
