Comparison Of Chula Formula With Standard 21/23 Rule For Estimation Of Orotracheal Tube Depth In Intubated Adult Patients: A Sindh Institute Of Urology And Transplantation Experience.

BACKGROUND: The proper estimation of depth of the orotracheal tube (OTT) in intubated patients is difficult. Several methods have been developed for proper estimation of the depth of OTT. The purpose of this study was to compare two commonly used formulae (21/23 rule and Chula formula) for proper estimation of depth of OTT in our Pakistani population. METHODS: In this randomized interventional study, we included 74 adult patients. The study was conducted in the Intensive care unit of a tertiary care hospital in Karachi, Pakistan, from October 2021 to April 2022. Patients were intubated using either the 21/23 rule (OTT was fixed at 21 cm in females and 23 cm in males from the right incisor) or the Chula formula (OTT was fixed at the right incisor according to the height based formula, {(height in cm/10)+4}). The distance between the carina and the OTT tip was measured using the digital chest x-ray with a PACS software. RESULTS: A total of 74 patients were intubated in which 32 were intubated using 21/23 rule and 42 were intubated using the Chula formula. Four female patients in 21/23 rule group encountered unsafe distance between the carina and the tip of the OTT (i.e., <2 cm) while no such complications were reported in Chula formula group (p-value 0.031). CONCLUSIONS: Chula formula was safe method for OTT placement in our study. Further studies with larger sample size are needed to assess the safety and efficacy of Chula formula for Pakistani Population.

Modification of Surfaces with Vaterite CaCO3 Particles.

Former studies have demonstrated a strong interest toward the crystallization of CaCO3 polymorphs in solution. Nowadays, CaCO3 crystallization on solid surfaces is extensively being studied using biomolecules as substrates for the control of the growth aiming at various applications of CaCO3. Calcium carbonate exists in an amorphous state, as three anhydrous polymorphs (aragonite, calcite and vaterite), and as two hydrated polymorphs (monohydrocalcite and ikaite). The vaterite polymorph is considered as one of the most attractive forms due to its large surface area, biocompatibility, mesoporous nature, and other features. Based on physical or chemical immobilization approaches, vaterite can be grown directly on solid surfaces using various (bio)molecules, including synthetic polymers, biomacromolecules such as proteins and peptides, carbohydrates, fibers, extracellular matrix components, and even biological cells such as bacteria. Herein, the progress on the modification of solid surfaces by vaterite CaCO3 crystals is reviewed, focusing on main findings and the mechanism of vaterite growth initiated by various substances mentioned above, as well as the discussion of the applications of such modified surfaces.

Detection of glaucoma using retinal fundus images: A comprehensive review.

Content-based image analysis and computer vision techniques are used in various health-care systems to detect the diseases. The abnormalities in a human eye are detected through fundus images captured through a fundus camera. Among eye diseases, glaucoma is considered as the second leading case that can result in neurodegeneration illness. The inappropriate intraocular pressure within the human eye is reported as the main cause of this disease. There are no symptoms of glaucoma at earlier stages and if the disease remains unrectified then it can lead to complete blindness. The early diagnosis of glaucoma can prevent permanent loss of vision. Manual examination of human eye is a possible solution however it is dependant on human efforts. The automatic detection of glaucoma by using a combination of image processing, artificial intelligence and computer vision can help to prevent and detect this disease. In this review article, we aim to present a comprehensive review about the various types of glaucoma, causes of glaucoma, the details about the possible treatment, details about the publicly available image benchmarks, performance metrics, and various approaches based on digital image processing, computer vision, and deep learning. The review article presents a detailed study of various published research models that aim to detect glaucoma from low-level feature extraction to recent trends based on deep learning. The pros and cons of each approach are discussed in detail and tabular representations are used to summarize the results of each category. We report our findings and provide possible future research directions to detect glaucoma in conclusion.

Modeling global geometric spatial information for rotation invariant classification of satellite images.

The classification of high-resolution satellite images is an open research problem for computer vision research community. In last few decades, the Bag of Visual Word (BoVW) model has been used for the classification of satellite images. In BoVW model, an orderless histogram of visual words without any spatial information is used as image signature. The performance of BoVW model suffers due to this orderless nature and addition of spatial clues are reported beneficial for scene and geographical classification of images. Most of the image representations that can compute image spatial information as are not invariant to rotations. A rotation invariant image representation is considered as one of the main requirement for satellite image classification. This paper presents a novel approach that computes the spatial clues for the histograms of BoVW model that is robust to the image rotations. The spatial clues are calculated by computing the histograms of orthogonal vectors. This is achieved by calculating the magnitude of orthogonal vectors between Pairs of Identical Visual Words (PIVW) relative to the geometric center of an image. The comparative analysis is performed with recently proposed research to obtain the best spatial feature representation for the satellite imagery. We evaluated the proposed research for image classification using three standard image benchmarks of remote sensing. The results and comparisons conducted to evaluate this research show that the proposed approach performs better in terms of classification accuracy for a variety of datasets based on satellite images.

A Hybrid Geometric Spatial Image Representation for scene classification.

The recent development in the technology has increased the complexity of image contents and demand for image classification becomes more imperative. Digital images play a vital role in many applied domains such as remote sensing, scene analysis, medical care, textile industry and crime investigation. Feature extraction and image representation is considered as an important step in scene analysis as it affects the image classification performance. Automatic classification of images is an open research problem for image analysis and pattern recognition applications. The Bag-of-Features (BoF) model is commonly used to solve image classification, object recognition and other computer vision-based problems. In BoF model, the final feature vector representation of an image contains no information about the co-occurrence of features in the 2D image space. This is considered as a limitation, as the spatial arrangement among visual words in image space contains the information that is beneficial for image representation and learning of classification model. To deal with this, researchers have proposed different image representations. Among these, the division of image-space into different geometric sub-regions for the extraction of histogram for BoF model is considered as a notable contribution for the extraction of spatial clues. Keeping this in view, we aim to explore a Hybrid Geometric Spatial Image Representation (HGSIR) that is based on the combination of histograms computed over the rectangular, triangular and circular regions of the image. Five standard image datasets are used to evaluate the performance of the proposed research. The quantitative analysis demonstrates that the proposed research outperforms the state-of-art research in terms of classification accuracy.

Image classification by addition of spatial information based on histograms of orthogonal vectors.

The Bag-of-Visual-Words (BoVW) model is widely used for image classification, object recognition and image retrieval problems. In BoVW model, the local features are quantized and 2-D image space is represented in the form of order-less histogram of visual words. The image classification performance suffers due to the order-less representation of image. This paper presents a novel image representation that incorporates the spatial information to the inverted index of BoVW model. The spatial information is added by calculating the global relative spatial orientation of visual words in a rotation invariant manner. For this, we computed the geometric relationship between triplets of identical visual words by calculating an orthogonal vector relative to each point in the triplets of identical visual words. The histogram of visual words is calculated on the basis of the magnitude of these orthogonal vectors. This calculation provides the unique information regarding the relative position of visual words when they are collinear. The proposed image representation is evaluated by using four standard image benchmarks. The experimental results and quantitative comparisons demonstrate that the proposed image representation outperforms the existing state-of-the-art in terms of classification accuracy.