Search

Purpose: The tracheoesophageal puncture for the voice prosthesis (VP) placement is the recognized gold standard in post-laryngectomy voice rehabilitation. Despite the development of specific intraoperative techniques, a subset of patients will suffer from poor functional outcomes due to pharyngoesophageal spasms (PES). This paper evaluates the functional outcomes after transcutaneous botulinum toxin type A (BTX-A) infiltration for PES with a videofluoroscopy-guided technique. Methods: Since 2022, eight consecutive patients with VP and affected by PES were treated with BTX-A injection by a standard videofluoroscopic guided technique at the European Institute of Oncology, IRCCS (IEO) in Milan. A lidocaine test was performed pre-operatively to evaluate the potential effect of chemical neurectomy. All patients with positive lidocaine tests were injected with 50 IU of BTX-A (Allergan, Irvine, CA) according to the sites marked during the videofluoroscopy. Reported symptoms (VHI, SECEL), perceptual (INFVo), aerodynamic (MPT) and manometric parameters were collected before and after treatment. Results: In all cases, BTX-A was performed as an outpatient procedure without complications. For seven patients, only one BTX-A injection was needed, while one patient required a re-injection. Subjective and perceptive improvement after BTX-A was significant for VHI, SECEL and INFVo. MPT showed significant improvement after a chemical neurectomy. After a mean follow-up of 6 months, all patients maintained a good TES quality. Conclusion: The videofluoroscopic guided BTX-A injection of the pharyngoesophageal tract showed to be a feasible and reproducible technique in all cases. The pharyngoesophageal videofluoroscopy allows defining of patients' anatomical landmarks that help the surgeon to perform a homogeneous injection, empowered by post-injection massage. [ABSTRACT FROM AUTHOR]

As a widely known disease diabetes mellitus makes the human body produce quite less hormone and also tend to cause increased glucose that results in abnormal metabolism of varied organs in the body like eyes, kidneys, etc. Diabetic analysis has attracted the research community to treat some missing values and class imbalance issues. The performance of diabetes mellitus classification by the usage of machine learning techniques is comparatively low. We suggest this paper on imbalanced dataset with missing values, an efficient prediction algorithm for diabetes mellitus classification using Deep 1D-Convolutional Neural Network values. The outlier detection is used for removing missing values first. Then, oversampling method (SMOTE) is used to reduce the influence of imbalance class on prediction performance. Finally, predictions are produced using a DCNN classifier and are evaluated using a selective set of evaluation indicators. Experiments on the Pima Indian diabetes dataset (PIDD) from UCI Repository (University of California at Irvine) have yielded positive results. Our proposed DCNN algorithm has been shown to be successful and superior. [ABSTRACT FROM AUTHOR]

This paper presents a workbench to get simple neural classification models based on product evolutionary networks via a prior data preparation at attribute level by means of filter-based feature selection. Therefore, the computation to build the classifier is shorter, compared to a full model without data pre-processing, which is of utmost importance since the evolutionary neural models are stochastic and different classifiers with different seeds are required to get reliable results. Feature selection is one of the most common techniques for pre-processing the data within any kind of learning task. Six filters have been tested to assess the proposal. Fourteen (binary and multi-class) difficult classification data sets from the University of California repository at Irvine have been established as the test bed. An empirical study between the evolutionary neural network models obtained with and without feature selection has been included. The results have been contrasted with nonparametric statistical tests and show that the current proposal improves the test accuracy of the previous models significantly. Moreover, the current proposal is much more efficient than the previous methodology; the time reduction percentage is above 40%, on average. Our approach has also been compared with several classifiers both with and without feature selection in order to illustrate the performance of the different filters considered. Lastly, a statistical analysis for each feature selector has been performed providing a pairwise comparison between machine learning algorithms. [ABSTRACT FROM AUTHOR]