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Background: Segmenting tumors in MRI scans is a difficult and time-consuming task for radiologists. This is because tumors come in different shapes, sizes, and textures, making them hard to identify visually., Objective: This study proposes a new method called the enhanced regularized ensemble encoder-decoder network (EREEDN) for more accurate brain tumor segmentation., Methods: The EREEDN model first preprocesses the MRI data by normalizing the intensity levels. It then uses a series of autoencoder networks to segment the tumor. These autoencoder networks are trained using back-propagation and gradient descent. To prevent overfitting, the EREEDN model also uses L2 regularization and dropout mechanisms., Results: The EREEDN model was evaluated on the BraTS 2020 dataset. It achieved high performance on various metrics, including accuracy, sensitivity, specificity, and dice coefficient score. The EREEDN model outperformed other methods on the BraTS 2020 dataset., Conclusion: The EREEDN model is a promising new method for brain tumor segmentation. It is more accurate and efficient than previous methods. Future studies will focus on improving the performance of the EREEDN model on complex tumors., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Artificial Intelligence (AI) is expanding with colossal applications in various sectors. In the healthcare sector, it is booming to make life simpler with utmost accuracy by predicting, diagnosing and up to care with the help of Machine Learning (ML) and Deep Learning (DL) applications. Modern computer algorithms have attained accuracy levels comparable to those of human specialists in medical sciences, although computers often do jobs more quickly than people do. It is also expected that there will not be a mandate for humans to be present for the jobs that machines can do, and it is gaining the highest peak because of good trained artificial models in the medical field. ML enhances the therapeutic process and improves health by encouraging more patient participation. ML may get more accurate patient data when used with the Internet of Medical Things (IoMT) and automate message notifications that prompt patients to respond at certain times. The motivation behind this article is to make a comprehensive review of the on-going implementation of ML in medical science, what challenges it is facing now, and how it can be simplified for future researchers to contribute better to medical sciences while applying it to the practitioners' jobs easier. In this review, we have extensively mined the data and brought up systematised applications of AI in healthcare, what challenges have been faced by the experts, and what ethical responsibilities are liable to them while taking the data. We also tabulated which algorithms will be helpful for what kind of data and disease conditions will be useful for future researchers and developers. This article will provide a better insight into AI and ML for the beginner to the advanced developer and researcher to understand the concepts from the basics., Competing Interests: The authors declare there is no potential competing interest., (© 2022 Biomedical Informatics.)

Background: Little attention has been given to the development of lower respiratory tract infections (LRTIs) in patients with pulmonary tuberculosis (PTB) during their anti-tuberculosis (anti-TB) treatment and how that might affect patients' health status. Here, the prevalence and etiologies of other LRTIs in a cohort of PTB patients were determined, and the clinical features and outcomes were described., Methods: Adult patients with PTB between 2015 and 2020 were recruited and monitored during their anti-TB treatment for the presence of LRTIs. Clinical data were retrospectively collected from patients' medical records., Results: Data from 76 PTB patients (57 [75%] males) were reviewed. The median age was 61.0 (interquartile range 83.5-35.5) years, and other LRTIs were detected in 45 (59.2%) PTB patients. Of the 126 episodes of LRTIs, 84 (66.7%) were due to bacterial infections, 37 (29.4%) were due to fungal infections, and 5 (3.9%) were due to viral infections. The development of LRTIs was significantly more common in older ( P = 0.012) and hypertensive patients with PTB ( P = 0.019). Patients with PTB and LRTIs experienced significantly more frequent extrapulmonary infections ( P = 0.0004), bloodstream infections ( P = 0.001), intensive care unit stays ( P = 0.001), and invasive mechanical ventilation use ( P = 0.03) than patients who did not develop LRTI., Conclusions: The identification of host-related risk factors for LRTI development among patients with PTB could be used to develop a prediction model for LRTI development. Hence, initiating antimicrobials early, in parallel with appropriate anti-TB treatment, may mitigate PTB-related health and economic consequences., Competing Interests: There are no conflicts of interest., (Copyright: © 2022 Annals of Thoracic Medicine.)

The COVID-19 pandemic has been a disastrous event that has elevated several psychological issues such as depression given abrupt social changes and lack of employment. At the same time, social scientists and psychologists have gained significant interest in understanding the way people express emotions and sentiments at the time of pandemics. During the rise in COVID-19 cases with stricter lockdowns, people expressed their sentiments on social media. This offers a deep understanding of human psychology during catastrophic events. By exploiting user-generated content on social media such as Twitter, people's thoughts and sentiments can be examined, which aids in introducing health intervention policies and awareness campaigns. The recent developments of natural language processing (NLP) and deep learning (DL) models have exposed noteworthy performance in sentiment analysis. With this in mind, this paper presents a new sunflower optimization with deep-learning-driven sentiment analysis and classification (SFODLD-SAC) on COVID-19 tweets. The presented SFODLD-SAC model focuses on the identification of people's sentiments during the COVID-19 pandemic. To accomplish this, the SFODLD-SAC model initially preprocesses the tweets in distinct ways such as stemming, removal of stopwords, usernames, link punctuations, and numerals. In addition, the TF-IDF model is applied for the useful extraction of features from the preprocessed data. Moreover, the cascaded recurrent neural network (CRNN) model is employed to analyze and classify sentiments. Finally, the SFO algorithm is utilized to optimally adjust the hyperparameters involved in the CRNN model. The design of the SFODLD-SAC technique with the inclusion of an SFO algorithm-based hyperparameter optimizer for analyzing people's sentiments on COVID-19 shows the novelty of this study. The simulation analysis of the SFODLD-SAC model is performed using a benchmark dataset from the Kaggle repository. Extensive, comparative results report the promising performance of the SFODLD-SAC model over recent state-of-the-art models with maximum accuracy of 99.65%.