A unique method of intubating an unusual neck injury with knife-in-situ

A young male, aged 32 years, was brought to the emergency operation theatre, with a household knife-in-situ, in the neck. A detailed history revealed psychiatric illness to be the cause of this self inflicted injury. His vitals were found to be stable and he had no respiratory embarrassment and was conversing comfortably. Intubating him with a knife-in-situ was a great challenge. A simple technique using two endotracheal tubes was used which helped in securing the airway avoiding any further injury with the knife-in-situ

Transient aphonia following spinal anesthesia in a parturient: A case report

Spinal anesthesia is the preferred technique of administering anesthesia for elective cesarean section (CS). Hypotension, failed spinal anesthesia, postdural-puncture headache, cauda equina syndrome are a few complications that may occur but neurological complications particularly aphonia are quite rare. The use of lipophilic opioids as adjuvants with local anesthetics are considered as culprit but the exact mechanism remains unidentified. We report such presentation in our patient and discuss the likely cause

Per-Operative Kinking of a Reinforced Endotracheal Tube: An Unforeseen Complication

Reinforced tubes are routinely used in Oro-maxillary surgeries. In spite of its advantages, any intra-operative deformity in reinforced tubes can at times lead to occlusion of a patent airway. To change this tube intraoperatively with distorted oral anatomy could be an anaesthetic challenge

Improved Handwritten Digit Recognition Using Convolutional Neural Networks (CNN)

Traditional systems of handwriting recognition have relied on handcrafted features and a large amount of prior knowledge. Training an Optical character recognition (OCR) system based on these prerequisites is a challenging task. Research in the handwriting recognition field is focused around deep learning techniques and has achieved breakthrough performance in the last few years. Still, the rapid growth in the amount of handwritten data and the availability of massive processing power demands improvement in recognition accuracy and deserves further investigation. Convolutional neural networks (CNNs) are very effective in perceiving the structure of handwritten characters/words in ways that help in automatic extraction of distinct features and make CNN the most suitable approach for solving handwriting recognition problems. Our aim in the proposed work is to explore the various design options like number of layers, stride size, receptive field, kernel size, padding and dilution for CNN-based handwritten digit recognition. In addition, we aim to evaluate various SGD optimization algorithms in improving the performance of handwritten digit recognition. A network’s recognition accuracy increases by incorporating ensemble architecture. Here, our objective is to achieve comparable accuracy by using a pure CNN architecture without ensemble architecture, as ensemble architectures introduce increased computational cost and high testing complexity. Thus, a CNN architecture is proposed in order to achieve accuracy even better than that of ensemble architectures, along with reduced operational complexity and cost. Moreover, we also present an appropriate combination of learning parameters in designing a CNN that leads us to reach a new absolute record in classifying MNIST handwritten digits. We carried out extensive experiments and achieved a recognition accuracy of 99.87% for a MNIST dataset

A prospective study to evaluate and compare laryngeal mask airway ProSeal and i-gel airway in the prone position

Background: Prone position is commonly used to provide surgical access to a variety of surgeries. In view of the advantages of induction of anesthesia in the prone position, we conducted a randomized study to evaluate and compare ProSeal laryngeal mask airway (LMA) and i-gel in the prone position. Materials and Methods: Totally, 40 patients of either sex as per American Society of Anesthesiologists physical status I or II, between 16 and 60 years of age, scheduled to undergo surgery in prone position were included in the study. After the patients positioned themselves prone on the operating table, anesthesia was induced by the standard technique. LMA ProSeal was used as an airway conduit in group 1 while i-gel was used in group 2. At the end of surgery, the airway device was removed in the same position. Results: Insertion of airway device was successful in first attempt in 16, and 17 cases in ProSeal laryngeal mask airway (PLMA) and i-gel groups, respectively. A second attempt was required to secure the airway in 4 and 3 patients in PLMA and i-gel groups, respectively. The mean insertion time was 21.8 ± 2.70 s for group 1 and 13.1 ± 2.24 s for group 2, the difference being statistically significant (P < 0.05). The mean seal pressure in group 1 was 36 ± 6.22 cm H 2 O and in group 2 was 25.4 ± 3.21 cm H 2 O. The difference was statistically significant (P < 0.05). 13 patients in group 1 had fiberoptic bronchoscopy (FOB) grade 1 while it was 6 for group 2. The remaining patients in both groups had FOB grade 2. Conclusion: Insertion of supraglottic airways and conduct of anesthesia with them is feasible in the prone position. The PLMA has a better seal while insertion is easier with i-gel

A Deep Learning-Based Framework for Retinal Disease Classification

This study addresses the problem of the automatic detection of disease states of the retina. In order to solve the abovementioned problem, this study develops an artificially intelligent model. The model is based on a customized 19-layer deep convolutional neural network called VGG-19 architecture. The model (VGG-19 architecture) is empowered by transfer learning. The model is designed so that it can learn from a large set of images taken with optical coherence tomography (OCT) and classify them into four conditions of the retina: (1) choroidal neovascularization, (2) drusen, (3) diabetic macular edema, and (4) normal form. The training datasets (taken from publicly available sources) consist of 84,568 instances of OCT retinal images. The datasets exhibit all four classes of retinal disease mentioned above. The proposed model achieved a 99.17% classification accuracy with 0.995 specificities and 0.99 sensitivity, making it better than the existing models. In addition, the proper statistical evaluation is done on the predictions using such performance measures as (1) area under the receiver operating characteristic curve, (2) Cohen’s kappa parameter, and (3) confusion matrix. Experimental results show that the proposed VGG-19 architecture coupled with transfer learning is an effective technique for automatically detecting the disease state of a retina