Prediction of Mortality Using the Sequential Organ Failure Assessment Score in Critically Ill COVID-19 Patients.

OBJECTIVE:  To determine the accuracy and reliability of the sequential organ failure assessment (SOFA) score in predicting the risk of mortality in ICU-admitted COVID-19 patients. STUDY DESIGN: Cross-sectional study. Place and Duration of the Study: COVID Intensive Care Unit (ICU), The Aga Khan University Hospital, Karachi, from January to June 2022. METHODOLOGY: A total of 62 patients with a positive RT-PCR for COVID-19, admitted into the intensive care unit (ICU), were included in this descriptive cross-sectional study. Written informed consent was obtained after explaining the risks and benefits of the study to the patients / next of kin. SOFA score at the time of admission and 48 hours after admission was calculated. The outcome variable, i.e., mortality, was assessed in association with the SOFA score.  Results: The study had a predominantly male population of 54.8% (n = 34). The SOFA score >7 at admission and 48 hours after admission was observed in 46.8% (n = 29) patients. Among 62 COVID-19 patients, the majority were found to have a severe nature of the disease, i.e., 69.4% (n = 43), followed by moderate / mild cases 30.6% (n = 19). Depending on the requirement of the patient, 74.2% (n = 46) were invasively ventilated while 77.4% (n = 48) were on non-invasive ventilation. Overall the mortality rate of the present study was 43.5% (n = 27). The scores both at the time of admission and 48 hours after admission for the survivors had a significant difference (p = 0.001) with the non-survivors. CONCLUSION:  The SOFA score on admission and 48 hours after had a significant positive association with the severity of COVID-19 infection and its risk of mortality. KEY WORDS: COVID-19, Mortality prediction, SOFA score.

Dorsalis pedis cannulation: An overlooked option-A case report.

Radial artery is mostly used for arterial cannulation because of its location, collateral circulation, and less complications. Dorsalis pedis artery can be an alternative for arterial cannulation in difficult radial or brachial arteries cannulation situations as it is mostly overlooked. We present a case of a 45-year-old female planned for supratentorial craniotomy for excision of meningioma. After induction of anesthesia, the invasive access couldn't be attained after multiple attempts under ultrasound guidance by five senior anesthesiologists. The surgery was abandoned, and the patient awakened. The case was rescheduled after 2 days. The new anesthesia team attained the arterial access in the right dorsalis pedis artery and the central venous access in the right internal jugular vein in the first attempt. No complications were noted post-operatively. The dorsalis pedis artery can be safely used for arterial cannulation when radial artery cannulation is not possible.

Effect of Coca-Cola on the Dissolution of Persimmon-Related Phytobezoar in a Tertiary Care Hospital.

Introduction Bezoars, masses of indigestible foreign bodies formed in the gastrointestinal tract, pose challenges in their management. Phytobezoars are particularly problematic due to their difficult diagnosis and resilience towards treatment. Recently, Coca-Cola has emerged as a potential solution due to its acidic composition and mucolytic properties. However, existing evidence is limited, highlighting the need for comprehensive studies. This research explores the efficacy of Coca-Cola in dissolving persimmon-related phytobezoars, aiming to contribute valuable insights to non-invasive treatment options. Material and methods Conducted as a descriptive case series, this study employed gastric cola lavage using non-probability purposive sampling. Patients aged 18-70 with persimmon-related phytobezoars were included. Two nasogastric tubes were inserted for cola lavage over 12 hours, utilizing three liters of cola until the disappearance of symptoms. When the bezoar disappeared, it was considered as complete success to the treatment. Results Out of 31 patients, 45.2% were male and 54.8% were female, with a mean age of 56.77 ± 9.01 years. Efficacy was noted in 54.8% of cases. Age less than 50 and no history of diabetes mellitus were associated with higher chances of treatment success (p-value ≤0.05). Conclusion Ingestion of Coca-Cola was highly effective, safe, and reliable for the dissolution of persimmon-related phytobezoars, as the frequency of efficacy was high in our study. Coca-Cola ingestion is a non-invasive and cost-effective mode of phytobezoar dissolution that should be taken as a first-line initial treatment option to attain desired outcomes.

Dataset of cattle biometrics through muzzle images.

The Cattle Biometrics Dataset is the result of a rigorous process of data collecting, encompassing a wide range of cattle photographs obtained from publicly accessible cattle markets and farms. The dataset provided contains a comprehensive collection of more than 8,000 annotated samples derived from several cow breeds. This dataset represents a valuable asset for conducting research in the field of biometric recognition. The diversity of cattle in this context includes a range of ages, genders, breeds, and environmental conditions. Every photograph is taken from different quality cameras is thoroughly annotated, with special attention given to the muzzle of the cattle, which is considered an excellent biometric characteristic. In addition to its obvious practical benefits, this dataset possesses significant potential for extensive reuse. Within the domain of computer vision, it serves as a catalyst for algorithmic advancements, whereas in the agricultural sector, it augments practises related to cattle management. Machine learning aficionados highly value the use of machine learning for the construction and experimentation of models, especially in the context of transfer learning. Interdisciplinary collaboration is actively encouraged, facilitating the advancement of knowledge at the intersections of agriculture, computer science, and data science. The Cattle Biometrics Dataset represents a valuable resource that has the potential to stimulate significant advancements in various academic disciplines, fostering ground breaking research and innovation.

A Systematic Evaluation of Feature Encoding Techniques for Gait Analysis Using Multimodal Sensory Data.

This paper addresses the problem of feature encoding for gait analysis using multimodal time series sensory data. In recent years, the dramatic increase in the use of numerous sensors, e.g., inertial measurement unit (IMU), in our daily wearable devices has gained the interest of the research community to collect kinematic and kinetic data to analyze the gait. The most crucial step for gait analysis is to find the set of appropriate features from continuous time series data to accurately represent human locomotion. This paper presents a systematic assessment of numerous feature extraction techniques. In particular, three different feature encoding techniques are presented to encode multimodal time series sensory data. In the first technique, we utilized eighteen different handcrafted features which are extracted directly from the raw sensory data. The second technique follows the Bag-of-Visual-Words model; the raw sensory data are encoded using a pre-computed codebook and a locality-constrained linear encoding (LLC)-based feature encoding technique. We evaluated two different machine learning algorithms to assess the effectiveness of the proposed features in the encoding of raw sensory data. In the third feature encoding technique, we proposed two end-to-end deep learning models to automatically extract the features from raw sensory data. A thorough experimental evaluation is conducted on four large sensory datasets and their outcomes are compared. A comparison of the recognition results with current state-of-the-art methods demonstrates the computational efficiency and high efficacy of the proposed feature encoding method. The robustness of the proposed feature encoding technique is also evaluated to recognize human daily activities. Additionally, this paper also presents a new dataset consisting of the gait patterns of 42 individuals, gathered using IMU sensors.

A Comparative Study of Feature Selection Approaches for Human Activity Recognition Using Multimodal Sensory Data.

Human activity recognition (HAR) aims to recognize the actions of the human body through a series of observations and environmental conditions. The analysis of human activities has drawn the attention of the research community in the last two decades due to its widespread applications, diverse nature of activities, and recording infrastructure. Lately, one of the most challenging applications in this framework is to recognize the human body actions using unobtrusive wearable motion sensors. Since the human activities of daily life (e.g., cooking, eating) comprises several repetitive and circumstantial short sequences of actions (e.g., moving arm), it is quite difficult to directly use the sensory data for recognition because the multiple sequences of the same activity data may have large diversity. However, a similarity can be observed in the temporal occurrence of the atomic actions. Therefore, this paper presents a two-level hierarchical method to recognize human activities using a set of wearable sensors. In the first step, the atomic activities are detected from the original sensory data, and their recognition scores are obtained. Secondly, the composite activities are recognized using the scores of atomic actions. We propose two different methods of feature extraction from atomic scores to recognize the composite activities, and they include handcrafted features and the features obtained using the subspace pooling technique. The proposed method is evaluated on the large publicly available CogAge dataset, which contains the instances of both atomic and composite activities. The data is recorded using three unobtrusive wearable devices: smartphone, smartwatch, and smart glasses. We also investigated the performance evaluation of different classification algorithms to recognize the composite activities. The proposed method achieved 79% and 62.8% average recognition accuracies using the handcrafted features and the features obtained using subspace pooling technique, respectively. The recognition results of the proposed technique and their comparison with the existing state-of-the-art techniques confirm its effectiveness.

Exploiting Superpixels for Multi-Focus Image Fusion.

Multi-focus image fusion is the process of combining focused regions of two or more images to obtain a single all-in-focus image. It is an important research area because a fused image is of high quality and contains more details than the source images. This makes it useful for numerous applications in image enhancement, remote sensing, object recognition, medical imaging, etc. This paper presents a novel multi-focus image fusion algorithm that proposes to group the local connected pixels with similar colors and patterns, usually referred to as superpixels, and use them to separate the focused and de-focused regions of an image. We note that these superpixels are more expressive than individual pixels, and they carry more distinctive statistical properties when compared with other superpixels. The statistical properties of superpixels are analyzed to categorize the pixels as focused or de-focused and to estimate a focus map. A spatial consistency constraint is ensured on the initial focus map to obtain a refined map, which is used in the fusion rule to obtain a single all-in-focus image. Qualitative and quantitative evaluations are performed to assess the performance of the proposed method on a benchmark multi-focus image fusion dataset. The results show that our method produces better quality fused images than existing image fusion techniques.

Marker-Based Movement Analysis of Human Body Parts in Therapeutic Procedure.

Movement analysis of human body parts is momentous in several applications including clinical diagnosis and rehabilitation programs. The objective of this research is to present a low-cost 3D visual tracking system to analyze the movement of various body parts during therapeutic procedures. Specifically, a marker based motion tracking system is proposed in this paper to capture the movement information in home-based rehabilitation. Different color markers are attached to the desired joints' locations and they are detected and tracked in the video to encode their motion information. The availability of this motion information of different body parts during the therapy can be exploited to achieve more accurate results with better clinical insight, which in turn can help improve the therapeutic decision making. The proposed framework is an automated and inexpensive motion tracking system with execution speed close to real time. The performance of the proposed method is evaluated on a dataset of 10 patients using two challenging matrices that measure the average accuracy by estimating the joints' locations and rotations. The experimental evaluation and its comparison with the existing state-of-the-art techniques reveals the efficiency of the proposed method.

Lung Nodule Detection in CT Images Using Statistical and Shape-Based Features.

The lung tumor is among the most detrimental kinds of malignancy. It has a high occurrence rate and a high death rate, as it is frequently diagnosed at the later stages. Computed Tomography (CT) scans are broadly used to distinguish the disease; computer aided systems are being created to analyze the ailment at prior stages productively. In this paper, we present a fully automatic framework for nodule detection from CT images of lungs. A histogram of the grayscale CT image is computed to automatically isolate the lung locale from the foundation. The results are refined using morphological operators. The internal structures are then extracted from the parenchyma. A threshold-based technique is proposed to separate the candidate nodules from other structures, e.g., bronchioles and blood vessels. Different statistical and shape-based features are extracted for these nodule candidates to form nodule feature vectors which are classified using support vector machines. The proposed method is evaluated on a large lungs CT dataset collected from the Lung Image Database Consortium (LIDC). The proposed method achieved excellent results compared to similar existing methods; it achieves a sensitivity rate of 93.75%, which demonstrates its effectiveness.

Multi-Focus Image Fusion: Algorithms, Evaluation, and a Library.

Image fusion is a process that integrates similar types of images collected from heterogeneous sources into one image in which the information is more definite and certain. Hence, the resultant image is anticipated as more explanatory and enlightening both for human and machine perception. Different image combination methods have been presented to consolidate significant data from a collection of images into one image. As a result of its applications and advantages in variety of fields such as remote sensing, surveillance, and medical imaging, it is significant to comprehend image fusion algorithms and have a comparative study on them. This paper presents a review of the present state-of-the-art and well-known image fusion techniques. The performance of each algorithm is assessed qualitatively and quantitatively on two benchmark multi-focus image datasets. We also produce a multi-focus image fusion dataset by collecting the widely used test images in different studies. The quantitative evaluation of fusion results is performed using a set of image fusion quality assessment metrics. The performance is also evaluated using different statistical measures. Another contribution of this paper is the proposal of a multi-focus image fusion library, to the best of our knowledge, no such library exists so far. The library provides implementation of numerous state-of-the-art image fusion algorithms and is made available publicly at project website.

Detection of Infantile Movement Disorders in Video Data Using Deformable Part-Based Model.

Movement analysis of infants' body parts is momentous for the early detection of various movement disorders such as cerebral palsy. Most existing techniques are either marker-based or use wearable sensors to analyze the movement disorders. Such techniques work well for adults, however they are not effective for infants as wearing such sensors or markers may cause discomfort to them, affecting their natural movements. This paper presents a method to help the clinicians for the early detection of movement disorders in infants. The proposed method is marker-less and does not use any wearable sensors which makes it ideal for the analysis of body parts movement in infants. The algorithm is based on the deformable part-based model to detect the body parts and track them in the subsequent frames of the video to encode the motion information. The proposed algorithm learns a model using a set of part filters and spatial relations between the body parts. In particular, it forms a mixture of part-filters for each body part to determine its orientation which is used to detect the parts and analyze their movements by tracking them in the temporal direction. The model is represented using a tree-structured graph and the learning process is carried out using the structured support vector machine. The proposed framework will assist the clinicians and the general practitioners in the early detection of infantile movement disorders. The performance evaluation of the proposed method is carried out on a large dataset and the results compared with the existing techniques demonstrate its effectiveness.

A computer vision-based system for monitoring Vojta therapy.

A neurological illness is t he disorder in human nervous system that can result in various diseases including the motor disabilities. Neurological disorders may affect the motor neurons, which are associated with skeletal muscles and control the body movement. Consequently, they introduce some diseases in the human e.g. cerebral palsy, spinal scoliosis, peripheral paralysis of arms/legs, hip joint dysplasia and various myopathies. Vojta therapy is considered a useful technique to treat the motor disabilities. In Vojta therapy, a specific stimulation is given to the patient's body to perform certain reflexive pattern movements which the patient is unable to perform in a normal manner. The repetition of stimulation ultimately brings forth the previously blocked connections between the spinal cord and the brain. After few therapy sessions, the patient can perform these movements without external stimulation. In this paper, we propose a computer vision-based system to monitor the correct movements of the patient during the therapy treatment using the RGBD data. The proposed framework works in three steps. In the first step, patient's body is automatically detected and segmented and two novel techniques are proposed for this purpose. In the second step, a multi-dimensional feature vector is computed to define various movements of patient's body during the therapy. In the final step, a multi-class support vector machine is used to classify these movements. The experimental evaluation carried out on the large captured dataset shows that the proposed system is highly useful in monitoring the patient's body movements during Vojta therapy.

Urothelial adenocarcinoma in a non-functioning bladder.

Urinary diversion is a common final outcome in patients with refractory long-term incontinence. It is even more common in young patients with neurogenic bladders and in such cases the bladder is disconnected and left in situ. We present a unique case of adenocarcinoma of the bladder which occurred 31 years following such a diversion procedure. We ask whether, despite the comorbidity associated with cystectomy, the patient is in danger of developing effectively silent tumours within these non-functioning bladders.