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Objective: To compare long-term outcomes between laparoscopic and robotic total mesorectal excisions (TMEs) for rectal cancer in a tertiary center. Background: Laparoscopic rectal cancer surgery has comparable long-term outcomes to the open approach, with several advantages in short-term outcomes. However, it has significant technical limitations, which the robotic approach aims to overcome. Methods: We included patients undergoing laparoscopic and robotic TME surgery between 2013 and 2021. The groups were compared after propensity-score matching. The primary outcome was 5-year overall survival (OS). Secondary outcomes were local recurrence (LR), distant recurrence (DR), disease-free survival (DFS), and short-term surgical and patient-related outcomes. Results: A total of 594 patients were included, and after propensity-score matching 215 patients remained in each group. There was a significant difference in 5-year OS (72.4% for laparoscopy vs 81.7% for robotic, P = 0.029), but no difference in 5-year LR (4.7% vs 5.2%, P = 0.850), DR (16.9% vs 13.5%, P = 0.390), or DFS (63.9% vs 74.4%, P = 0.086). The robotic group had significantly less conversion (3.7% vs 0.5%, P = 0.046), shorter length of stay [7.0 (6.0-13.0) vs 6.0 (4.0-8.0), P < 0.001), and less postoperative complications (63.5% vs 50.7%, P = 0.010). Conclusions: This study shows a correlation between higher 5-year OS and comparable long-term oncological outcomes for robotic TME surgery compared to the laparoscopic approach. Furthermore, lower conversion rates, a shorter length of stay, and a less minor postoperative complications were observed. Robotic rectal cancer surgery is a safe and favorable alternative to the traditional approaches.
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COVID-19, caused by SARS-CoV-2, has been declared as a global pandemic by WHO. Early diagnosis of COVID-19 patients may reduce the impact of coronavirus using modern computational methods like deep learning. Various deep learning models based on CT and chest X-ray images are studied and compared in this study as an alternative solution to reverse transcription-polymerase chain reactions. This study consists of three stages: planning, conduction, and analysis/reporting. In the conduction stage, inclusion and exclusion criteria are applied to the literature searching and identification. Then, we have implemented quality assessment rules, where over 75 scored articles in the literature were included. Finally, in the analysis/reporting stage, all the papers are reviewed and analysed. After the quality assessment of the individual papers, this study adopted 57 articles for the systematic literature review. From these reviews, the critical analysis of each paper, including the represented matrix for the model evaluation, existing contributions, and motivation, has been tracked with suitable illustrations. We have also interpreted several insights of each paper with appropriate annotation. Further, a set of comparisons has been enumerated with suitable discussion. Convolutional neural networks are the most commonly used deep learning architecture for COVID-19 disease classification and identification from X-ray and CT images. Various prior studies did not include data from a hospital setting nor did they consider data preprocessing before training a deep learning model.
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Data analytics and artificial intelligence (AI) have been used to predict patient outcomes after colorectal cancer surgery. A prospectively maintained colorectal cancer database was used, covering 4336 patients who underwent colorectal cancer surgery between 2003 and 2019. The 47 patient parameters included demographics, peri- and post-operative outcomes, surgical approaches, complications, and mortality. Data analytics were used to compare the importance of each variable and AI prediction models were built for length of stay (LOS), readmission, and mortality. Accuracies of at least 80% have been achieved. The significant predictors of LOS were age, ASA grade, operative time, presence or absence of a stoma, robotic or laparoscopic approach to surgery, and complications. The model with support vector regression (SVR) algorithms predicted the LOS with an accuracy of 83% and mean absolute error (MAE) of 9.69 days. The significant predictors of readmission were age, laparoscopic procedure, stoma performed, preoperative nodal (N) stage, operation time, operation mode, previous surgery type, LOS, and the specific procedure. A BI-LSTM model predicted readmission with 87.5% accuracy, 84% sensitivity, and 90% specificity. The significant predictors of mortality were age, ASA grade, BMI, the formation of a stoma, preoperative TNM staging, neoadjuvant chemotherapy, curative resection, and LOS. Classification predictive modelling predicted three different colorectal cancer mortality measures (overall mortality, and 31- and 91-days mortality) with 80-96% accuracy, 84-93% sensitivity, and 75-100% specificity. A model using all variables performed only slightly better than one that used just the most significant ones.
