Federated Learning for Protecting Medical Data Privacy

Deep learning is one of the most advanced machine learning techniques, and its prominence has increased in recent years. Language processing, predictions in medical research and pattern recognition are few of the numerous fields in which it is widely utilized. Numerous modern medical applications benefit greatly from the implementation of machine learning (ML) models and the disruptive innovations in the entire modern health care system. It is extensively used for constructing accurate and robust statistical models from large volumes of medical data collected from a variety of sources in contemporary healthcare systems [1]. Due to privacy concerns that restrict access to medical data, these Deep learning techniques have yet to completely exploit medical data despite their immense potential benefits. Many data proprietors are unable to benefit from large-scale deep learning due to privacy and confidentiality concerns associated with data sharing. However, without access to sufficient data, Deep Learning will not be able to realize its maximum potential when transitioning from the research phase to clinical practice [2]. This project addresses this problem by implementing Federated Learning and Encrypted Computations on text data, such as Multi Party Computation. SyferText, a Python library for privacy-protected Natural Language Processing that leverages PySyft to conduct Federated Learning, is used in this context

Brain MRI Image Classification for Cancer Detection Using Deep Wavelet Autoencoder-Based Deep Neural Network

Technology and the rapid growth in the area of brain imaging technologies have forever made for a pivotal role in analyzing and focusing the new views of brain anatomy and functions. The mechanism of image processing has widespread usage in the area of medical science for improving the early detection and treatment phases. Deep neural networks (DNN), till date, have demonstrated wonderful performance in classification and segmentation task. Carrying this idea into consideration, in this paper, a technique for image compression using a deep wavelet autoencoder (DWA), which blends the basic feature reduction property of autoencoder along with the image decomposition property of wavelet transform is proposed. The combination of both has a tremendous effect on sinking the size of the feature set for enduring further classification task by using DNN. A brain image dataset was taken and the proposed DWA-DNN image classifier was considered. The performance criterion for the DWA-DNN classifier was compared with other existing classifiers such as autoencoder-DNN or DNN, and it was noted that the proposed method outshines the existing methods

Nanoscale dihydroartemisinin@zeolitic imidazolate frameworks for enhanced antigiardial activity and mechanism analysis

An artificial semisynthetic material can be derived from artemisinin (ART) called dihydroartemisinin (DHA). Although DHA has enhanced antigiardial potential, its clinical application is limited because of its poor selectivity and low solubility. The drug’s absorption has a direct impact on the cell, and mechanism research is limited to its destruction of the cytoskeleton. In this study, we used the zeolitic imidazolate framework-8 and loaded it with DHA (DHA@Zif-8) to improve its antigiardial potential. DHA@Zif-8 can enhance cellular uptake, increase antigiardial proliferation and encystation, and expand the endoplasmic reticulum compared with the DHA-treated group. We used RNA sequencing (RNA-seq) to investigate the antigiardial mechanism. We found that 126 genes were downregulated and 123 genes were upregulated. According to the KEGG and GO pathway analysis, the metabolic functions in G. lamblia are affected by DHA@Zif-8 NPs. We used real-time quantitative reverse transcription polymerase chain reaction to verify our results using the RNA-seq data. DHA@Zif-8 NPs significantly enhanced the eradication of the parasite from the stool in vivo. In addition, the intestinal mucosal injury caused by G. lamblia trophozoites markedly improved in the intestine. This research provided the potential of utilizing DHA@Zif-8 to develop an antiprotozoan drug for clinical applications

A Systematic Review of Artificial Intelligence in Assistive Technology for People with Visual Impairment

Recent advances in artificial intelligence (AI) have led to the development of numerous successful applications that utilize data to significantly enhance the quality of life for people with visual impairment. AI technology has the potential to further improve the lives of visually impaired individuals. However, accurately measuring the development of visual aids continues to be challenging. As an AI model is trained on larger and more diverse datasets, its performance becomes increasingly robust and applicable to a variety of scenarios. In the field of visual impairment, deep learning techniques have emerged as a solution to previous challenges associated with AI models. In this article, we provide a comprehensive and up-to-date review of recent research on the development of AI-powered visual aides tailored to the requirements of individuals with visual impairment. We adopt the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, meticulously gathering and appraising pertinent literature culled from diverse databases. A rigorous selection process was undertaken, appraising articles against precise inclusion and exclusion criteria. Our meticulous search yielded a trove of 322 articles, and after diligent scrutiny, 12 studies were deemed suitable for inclusion in the ultimate analysis. The study's primary objective is to investigate the application of AI techniques to the creation of intelligent devices that aid visually impaired individuals in their daily lives. We identified a number of potential obstacles that researchers and developers in the field of visual impairment applications might encounter. In addition, opportunities for future research and advancements in AI-driven visual aides are discussed. This review seeks to provide valuable insights into the advancements, possibilities, and challenges in the development and implementation of AI technology for people with visual impairment. By examining the current state of the field and designating areas for future research, we expect to contribute to the ongoing progress of improving the lives of visually impaired individuals through the use of AI-powered visual aids