Aims and Objective: The aim of the present article is to highlight how reconstruction with free flaps is different and difficult in cases with robotic head-and-neck cancer surgery. It also highlights the technical guidelines on how to manage the difficulties. Materials and Methods: Eleven patients with oropharyngeal cancer having undergone tumour excision followed by free-flap reconstruction been reviewed here. Nine patients had tumour excision done robotically through intraoral route while neck dissection done with transverse neck crease incision. There is a problem of difficult flap inset in this group of patient. Two patients had intraoral excision of tumour followed by robotic neck dissection via retroauricular incision. With no incision directly on the neck, microvascular anastomosis is challenging in this set of patients. Free flap was used in all the cases to reconstruct the defect. Results: Successful reconstruction with free flap was done in all the cases with good outcome both functionally and aesthetically. Conclusion: Free-flap reconstruction is possible in robotic head-and-neck cancer surgery despite small and difficult access, but it does need practice and some technical modifications for good outcome.

Background There is a steep learning curve to attain a consistently good result in microvascular surgery. The venous anastomosis is a critical step in free-tissue transfer. The margin of error is less and the outcome depends on the surgeon’s skill and technique. Mechanical anastomotic coupling device (MACD) has been proven to be an effective alternative to hand-sewn (HS) technique for venous anastomosis, as it requires lesser skill. However, its feasibility of application in emerging economy countries is yet to be established. Material and Method We retrospectively analyzed the data of patients who underwent free-tissue transfer for head and neck reconstruction between July 2015 and October 2020. Based on the technique used for the venous anastomosis, the patients were divided into an HS technique and MACD group. Patient characteristics and outcomes were measured. Result A total of 1694 venous anastomoses were performed during the study period. There were 966 patients in the HS technique group and 719 in the MACD group. There was no statistically significant difference between the two groups in terms of age, sex, prior radiotherapy, prior surgery, and comorbidities. Venous thrombosis was noted in 62 (6.4%) patients in the HS technique group and 7 (0.97%) in the MACD group (p = 0.000). The mean time taken for venous anastomosis in the HS group was 17 ± 4 minutes, and in the MACD group, it was 5 ± 2 minutes (p = 0.0001). Twenty-five (2.56%) patients in the HS group and 4 (0.55%) patients in MACD group had flap loss (p = 0.001). Conclusion MACD is an effective alternative for HS technique for venous anastomosis. There is a significant reduction in anastomosis time, flap loss, and return to operation theater due to venous thrombosis. MACD reduces the surgeon’s strain, especially in a high-volume center. Prospective randomized studies including economic analysis are required to prove the cost-effectiveness of coupler devices.

No abstract

The intention of the paper is to improve a neural network methodology to accomplish enhanced predictions of the sales market. The data downloaded by Kaggle, data is surveyed for more than six months and the data was collected through prevalent markets for online and offline analysis with results of data visualization and prediction to illustrate sales forecasting. The traditional model like arima, RNN, and long short-term memory are not effective to provide sales forecasting with consideration of numerous constraints of the market and predict the sales incorrectly, because the RNN model suffers from vanishing gradient problems and LSTM are prone to overfitting. Therefore, these models are intensely prone to erroneous forecasts. The author suggests the “Long Term Short Memory (LSTM)” with three layers which are dropout layers, early stop layers, and simplifying layers to reduce overfitting. The result shows that the adapted “LSTM '' with the inclusion of three layers is an improved version as compared with traditional ''LSTM ``. The accuracy of the proposed model is 82%

No abstract