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Abstract The spread of antimicrobial resistance (AMR) leads to challenging complications and losses of human lives plus medical resources, with a high expectancy of deterioration in the future if the problem is not controlled. From a machine learning perspective, data-driven models could aid clinicians and microbiologists by anticipating the resistance beforehand. Our study serves as the first attempt to harness deep learning (DL) techniques and the multimodal data available in electronic health records (EHR) for predicting AMR. In this work, we utilize and preprocess the MIMIC-IV database extensively to produce separate structured input sources for time-invariant and time-series data customized to the AMR task. Then, a multimodality fusion approach merges the two modalities with clinical notes to determine resistance based on an antibiotic or a pathogen. To efficiently predict AMR, our approach builds the foundation for deploying multimodal DL techniques in clinical practice, leveraging the existing patient data.

Advancements in lung health assessment, a critical component in diagnosing respiratory conditions, have gained prominence in medical research. This is especially true with the advent of non-invasive techniques such as spirometry. Central to this diagnostic method are three key metrics: Forced Vital Capacity (FVC), Forced Expiratory Volume in 1 second (FEV1), and Peak Expiratory Flow (PEF). In light of the increasing need for accurate and reliable assessment tools, developing comprehensive datasets is imperative for advancing research in this field. Our paper presents RespiroDynamics: A Comprehensive Multimodal Respiratory Dataset, a total of more than 2k samples, compiled from 60 participants, covering a wide range of age, weight, height and others. This dataset incorporates various data types, including Red-Green-Blue (RGB) and Thermal videos, Heart Rate (HR), ECG readings and metadata, all synchronized with observed respiratory activities. We elaborate on the data collection methodology, post-processing techniques employed, and the analytical approach to extract meaningful patterns and insights. Our evaluation, using a 5-fold cross-validation method on binary classification models for FEV1/FVC, revealed remarkable accuracies: 99.7% for the RGB model and a perfect 100% for the thermal model. For the PEF 3-class model, accuracies were 97.14% for both RGB and 96.0% for thermal models. This study aims to underscore the dataset’s potential in establishing and enhancing a robust deep-learning model for lung health diagnostics.

AbstractThis paper investigates the process of designing HyFlex classrooms at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), a graduate-level research university located in Abu Dhabi, United Arab Emirates, underpinned by the application of the EDUCAUSE Learning Space Rating System (LSRS). This investigation takes the form of a case-study and specifically focuses on the rationale, planning, design, and technology behind the implementation of the flexible HyFlex spaces as deployed in several classroom environments at MBZUAI. Iterations’ performance was assessed with the LSRS—V3. The findings should make an important contribution to the field of HyFlex learning spaces and technology-enhanced classroom design at graduate level.

Abstract Accurate estimation of gestational age is an essential component of good obstetric care and informs clinical decision-making throughout pregnancy. As the date of the last menstrual period is often unknown or uncertain, ultrasound measurement of fetal size is currently the best method for estimating gestational age. The calculation assumes an average fetal size at each gestational age. The method is accurate in the first trimester, but less so in the second and third trimesters as growth deviates from the average and variation in fetal size increases. Consequently, fetal ultrasound late in pregnancy has a wide margin of error of at least ±2 weeks’ gestation. Here, we utilise state-of-the-art machine learning methods to estimate gestational age using only image analysis of standard ultrasound planes, without any measurement information. The machine learning model is based on ultrasound images from two independent datasets: one for training and internal validation, and another for external validation. During validation, the model was blinded to the ground truth of gestational age (based on a reliable last menstrual period date and confirmatory first-trimester fetal crown rump length). We show that this approach compensates for increases in size variation and is even accurate in cases of intrauterine growth restriction. Our best machine-learning based model estimates gestational age with a mean absolute error of 3.0 (95% CI, 2.9–3.2) and 4.3 (95% CI, 4.1–4.5) days in the second and third trimesters, respectively, which outperforms current ultrasound-based clinical biometry at these gestational ages. Our method for dating the pregnancy in the second and third trimesters is, therefore, more accurate than published methods.

Presents corrections to the paper, RespiroDynamics: A Multifaceted Dataset for Enhanced Lung Health Assessment Using Deep Learning.

Diabetic Retinopathy (DR) is a degenerative disease that impacts the eyes and is a consequence of Diabetes mellitus, where high blood glucose levels induce lesions on the eye retina. Diabetic Retinopathy is regarded as the leading cause of blindness for diabetic patients, especially the working-age population in developing nations. Treatment involves sustaining the patient’s current grade of vision since the disease is irreversible. Early detection of Diabetic Retinopathy is crucial in order to sustain the patient’s vision effectively. The main issue involved with DR detection is that the manual diagnosis process is very time, money, and effort consuming and involves an ophthalmologist’s examination of eye retinal fundus images. The latter also proves to be more difficult, particularly in the early stages of the disease when disease features are less prominent in the images. Machine learning-based medical image analysis has proven competency in assessing retinal fundus images, and the utilization of deep learning algorithms has aided the early diagnosis of Diabetic Retinopathy (DR). This paper reviews and analyzes state-of-the-art deep learning methods in supervised, self-supervised, and Vision Transformer setups, proposing retinal fundus image classification and detection. For instance, referable, non-referable, and proliferative classifications of Diabetic Retinopathy are reviewed and summarized. Moreover, the paper discusses the available retinal fundus datasets for Diabetic Retinopathy that are used for tasks such as detection, classification, and segmentation. The paper also assesses research gaps in the area of DR detection/classification and addresses various challenges that need further study and investigation.

Medical image segmentation is a vital healthcare endeavor requiring precise and efficient models for appropriate diagnosis and treatment. Vision transformer (ViT)-based segmentation models have shown great performance in accomplishing this task. However, to build a powerful backbone, the self-attention block of ViT requires large-scale pre-training data. The present method of modifying pre-trained models entails updating all or some of the backbone parameters. This paper proposes a novel fine-tuning strategy for adapting a pretrained transformer-based segmentation model on data from a new medical center. This method introduces a small number of learnable parameters, termed prompts, into the input space (less than 1% of model parameters) while keeping the rest of the model parameters frozen. Extensive studies employing data from new unseen medical centers show that the prompt-based fine-tuning of medical segmentation models provides excellent performance regarding the new-center data with a negligible drop regarding the old centers. Additionally, our strategy delivers great accuracy with minimum re-training on new-center data, significantly decreasing the computational and time costs of fine-tuning pre-trained models. Our source code will be made publicly available.

Flower classification is a challenging task due to the wide range of flower species, which have a similar shape, appearance or surrounding objects such as leaves and grass. In this study, the authors propose a novel two‐step deep learning classifier to distinguish flowers of a wide range of species. First, the flower region is automatically segmented to allow localisation of the minimum bounding box around it. The proposed flower segmentation approach is modelled as a binary classifier in a fully convolutional network framework. Second, they build a robust convolutional neural network classifier to distinguish the different flower types. They propose novel steps during the training stage to ensure robust, accurate and real‐time classification. They evaluate their method on three well known flower datasets. Their classification results exceed 97% on all datasets, which are better than the state‐of‐the‐art in this domain.

Giant kelp (Macrocystis pyrifera) is the most widely distributed kelp species on the planet, constituting one of the richest and most productive ecosystems on Earth, but detailed information on its distribution is entirely missing in some marine ecoregions, especially in the high latitudes of the Southern Hemisphere. Here, we present an algorithm based on a series of filter thresholds to detect giant kelp employing Sentinel-2 imagery. Given the overlap between the reflectances of giant kelp and intertidal green algae (Ulvophyceae), the latter are also detected on shallow rocky intertidal areas. The kelp filter algorithm was applied separately to vegetation indices, the Floating Algae Index (FAI), the Normalised Difference Vegetation Index (NDVI), and a novel formula (the Kelp Difference, KD). Training data from previously surveyed kelp forests and other coastal and ocean features were used to identify reflectance threshold values. This procedure was validated with independent field data collected with UAV imagery at a high spatial resolution and point-georeferenced sites at a low spatial resolution. When comparing UAV with Sentinel data (high-resolution validation), an average overall accuracy ≥ 0.88 and Cohen’s kappa ≥ 0.64 coefficients were found in all three indices for canopies reaching the surface with extensions greater than 1 hectare, with the KD showing the highest average kappa score (0.66). Measurements between previously surveyed georeferenced points and remotely-sensed kelp grid cells (low-resolution validation) showed that 66% of the georeferenced points had grid cells indicating kelp presence within a linear distance of 300 m. We employed the KD in our kelp filter algorithm to estimate the global extent of giant kelp and intertidal green algae per marine ecoregion and province, producing a high-resolution global map of giant kelp and intertidal green algae, powered by Google Earth Engine.

Introduction Persons with disabilities (PWDs) are among the most vulnerable communities to suffer the serious consequences of COVID-19, and accepting COVID-19 vaccination is one of the recommended health advisories for them. Unwillingness to receive vaccines is a concerning issue, especially in the countries of Southeast Asia. The study aims to find out the COVID-19 vaccination rate of persons with disabilities (PWDs) in Bangladesh, the rate of unwillingnessor inaccessibility of vaccination for PWDs, and predict the possible reasons for unwillingness. Methods A descriptive cross-sectional survey of PWDs aged 12 years or more was conducted in 12 rehabilitation centers in Bangladesh between February 2022 and May 2022. A self-developed structured questionnaire on socio-demographic, disability and health indicators, acceptance and accessibility of COVID-19 vaccines, and knowledge and attitude towards vaccination was used for the survey. The study has been conducted according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for a cross-sectional survey. Results With a 69% response rate, 241 PWDs participated in the study. PWDs with a spinal cord injury were the major respondents (62.7%). A total of 186 (77.2%) PWDs had taken one or more doses of COVID-19 vaccines of three doses supplied, and 55 (22.8%) were unwilling to have a vaccination. All vaccine recipients reported good accessibility to vaccination booths. The predictive factors of unwillingness were spinal cord injury type of disability ( OR .36, POR .44, POR .78, POR.24, P Conclusion More than one-fifth of the persons with disabilities aged between 12 and 80 years were unwilling to receive the COVID-19 vaccination despite the accessibility of information and availability of the COVID-19 vaccine centers, mainly due to poor knowledge and mobility issues.

Recent literature on Islam and the digital covers a wide range of topics and themes; however, what is yet to be developed from an Islamic perspective is a broader philosophical framework that accounts for the nature, exigencies and affordances of contemporary digital technologies. In advance of such a framework, this article is an attempt to open the way to philosophical engagement with issues of digital ethics from an Islamic perspective. After a brief review of recent literature on Islam and the digital and a significantly earlier work by Ziauddin Sardar, in which he proposed an information strategy for the Muslim world, this paper provides some background to Islamic ethics and the wide field of ethical theories in the Islamic tradition. This paper then proceeds by identifying and outlining examples of contemporary themes in Islam and the digital which conceal underlying philosophical and theological issues. Finally, this paper considers the significant scale and scope of the transformations involved in the ongoing transposition to the 'onlife' and highlights several areas where Islamic perspectives may be seen to converge or diverge with other strands of scholarship on digital ethics., Author(s): Mohammad Yaqub Chaudhary [sup.1] Author Affiliations: (1) grid.479284.4, Cambridge Muslim College, , Cambridge, UK Introduction In 'The Ethics of Information', Luciano Floridi writes that information ethics (IE), as construed [...]