COVID‐19 is a fatal disease caused by the SARS‐CoV‐2 virus that has caused around 5.3 Million deaths globally as of December 2021. The detection of this disease is a time taking process that have worsen the situation around the globe, and the disease has been identified as a world pandemic by the WHO. Deep learning‐based approaches are being widely used to diagnose the COVID‐19 cases, but the limitation of immensity in the publicly available dataset causes the problem of model over‐fitting. Modern artificial intelligence‐based techniques can be used to increase the dataset to avoid from the over‐fitting problem. This research work presents the use of various deep learning models along with the state‐of‐the‐art augmentation methods, namely, classical and generative adversarial network‐ (GAN‐) based data augmentation. Furthermore, four existing deep convolutional networks, namely, DenseNet‐121, InceptionV3, Xception, and ResNet101 have been used for the detection of the virus in X‐ray images after training on augmented dataset. Additionally, we have also proposed a novel convolutional neural network (QuNet) to improve the COVID‐19 detection. The comparative analysis of achieved results reflects that both QuNet and Xception achieved high accuracy with classical augmented dataset, whereas QuNet has also outperformed and delivered 90% detection accuracy with GAN‐based augmented dataset.

(1) Background: Fetal growth restriction is a relatively common disorder in pregnant patients with thrombophilia. New artificial intelligence algorithms are a promising option for the prediction of adverse obstetrical outcomes. The aim of this study was to evaluate the predictive performance of a Feed-Forward Back Propagation Network (FFBPN) for the prediction of small for gestational age (SGA) newborns in a cohort of pregnant patients with thrombophilia. (2) Methods: This observational retrospective study included all pregnancies in women with thrombophilia who attended two tertiary maternity hospitals in Romania between January 2013 and December 2020. Bivariate associations of SGA and each predictor variable were evaluated. Clinical and paraclinical predictors were further included in a FFBPN, and its predictive performance was assessed. (3) Results: The model had an area under the curve (AUC) of 0.95, with a true positive rate of 86.7%, and a false discovery rate of 10.5%. The overall accuracy of our model was 90%. (4) Conclusion: This is the first study in the literature that evaluated the performance of a FFBPN for the prediction of pregnant patients with thrombophilia at a high risk of giving birth to SGA newborns, and its promising results could lead to a tailored prenatal management.

Increasing importance in the field of artificial intelligence has led to huge progress in remote sensing. Deep learning approaches have made tremendous progress in hyperspectral image (HSI) classification. However, the complexity in classifying the HSI data using a common convolutional neural network is still a challenge. Further, the network architecture becomes more complex when different spatial–spectral feature information is extracted. Usually, CNN has a large number of trainable parameters, which increases the computational complexity of HSI data. In this paper, an optimized squeeze–excitation AdaBound dense network (SE-AB-DenseNet) is designed to emphasize the significant spatial–spectral features of HSI data. The dense network is combined with the AdaBound and squeeze–excitation modules to give lower computation costs and better classification performance. The AdaBound optimizer gives the proposed model the ability to improve its stability and enhance its classification accuracy by approximately 2%. Additionally, the cutout regularization technique is used for HSI spatial–spectral classification to overcome the problem of overfitting. The experiments were carried out on two commonly used hyperspectral datasets (Indian Pines and Salinas). The experiment results on the datasets show a competitive classification accuracy when compared with state-of-the-art methods with limited training samples. From the SE-AB-DenseNet with the cutout model, the overall accuracies for the Indian Pines and Salinas datasets were observed to be 99.37 and 99.78, respectively.

Personal health records (PHRs) are digital repositories that allow individuals to access, manage, and share their health information. By enabling patients to track their health over time and communicate effectively with healthcare providers, personal health records support more personalized care and improve the quality of healthcare. Their integration with emerging technologies, such as the Internet of Things (IoT), artificial intelligence (AI), and blockchain, enhances the utility and security of health data management, facilitating continuous health monitoring, automated decision support, and secure, decentralized data exchange. Despite their potential, PHR systems face significant challenges, including privacy concerns, security issues, and digital accessibility problems. This paper discusses the fundamental concepts, requirements, system architectures, and data sources that underpin modern PHR implementations, highlighting how they enable continuous health monitoring through the integration of wearable sensors; mobile health applications; and IoT-enabled medical devices that collect, process, and transmit data to support proactive care and personalized treatments. The benefits and limitations of PHR systems are also discussed in detail, with a focus on interoperability, adoption drivers, and the role of advanced technologies in supporting the development of secure and scalable health information systems for a smarter world.

The recent use of digital Distributed Ledger Technology (DLT) in the healthcare domain can surpass the existing limitations in the centralized IT systems, such as the lack of security, access control or immutability of the electronic health information. If we discuss about clinical information, this innovative informatics advance gives back the control to the data owner. In decentralized environments, smart contracts allow trustable agreements grounded by irreversible transactions, which permit transparency and traceability. Moreover, smart contracts are the living heart of the decentralized applications that run on DTL. In this work, our attention is focused on medical equipments that helps health staff to diagnose and treat patients keeping much of their clinical data taken in dynamics. Hence, we propose a smart contract-based decentralized application framework for the management of devices, targeting also medical services, meant to facilitate the interaction of the involved entities. Our testing environment is the Ethereum platform, extensively used recently in the healthcare domain, being itself a smart operating system that allows decentralized applications to run on it.

Background and Objectives: College life is a time when students take more responsibility for health-related behaviors. Aim: To determine the oral health behaviors of dental students, to motivate their transformation into cyanogenic behaviors by applying the planned behavior model, and to determine the degree to which students can modify behavior for oral health. Material and Methods: The results of the initial and final assessment (4 months apart) of the bacterial plaque present by means of the Quigley Hein and API indexes were collected from the student files. The Health-Promoting Lifestyle Profile II -HPLP II and Generalized Self-Efficacy Scale (GSE) questionnaires were used to determine the variables with predictor values that influence the cognitive-perceptual factors. Results: For the “health responsibility” domain, the average total value was 2.53 ± 0.9 (SD). For the “interpersonal relationships” domain, the average value was 2.82 ± 0.79. In the case of the “nutrition” domain, the desire to change the behavior of the participants was moderate (2.53 ± 1.19). “Physical activity” represents an important field in the everyday life of young adults. The results of the study indicated a moderate desire for change (2.52 ± 0.97). In the case of “spiritual growth”, in which self-esteem, the way the individual perceives the environment and the adaptation to it were evaluated, a great desire for change was observed (2.74 ± 0.82). Scale GSE questionnaire showed an α coefficient of 28.52 ± 0.82, a value that indicated a moderate to a high level of efficacy and self-confidence. Conclusions: The results of the study showed that students have a moderate tendency to change in all areas, except for interpersonal relationships, where the desire to change something is increased. The participants have a moderate to a high level of self-efficacy and self-confidence.