Medical Data Transfer Research Articles

Standardized Usage of Electronic Patient-Reported Outcome Measurements is Time-Efficient and Feasible.

(1) Background: Digitization is of the utmost importance in improving the transfer of medical data. In order to emphasize the need for the greater implementation of digital solutions, we compared analog PROMs (aPROMs) to electronic PROMs (ePROMs) to emphasize the time benefits for clinical everyday life. (2) Methods: This prospective, observational study compared the evaluation of SF-36 in patients between 18 and 80 years old with musculoskeletal pathologies. We performed an age-independent and age-dependent analysis. (3) Results: After the import of aPROMs data, ePROMs took significantly less time (11.97 ± 3.00 min vs. 9.41 ± 3.12 min, p = 0.002, d = 0.797). There were no significant differences associated with age for aPROMs (7.23 ± 2.57 min vs. 8.38 ± 2.71 min, p = 0.061, d = -0.607) or ePROMs (8.72 ± 2.19 min vs. 10.09 ± 3.80 min, p = 0.130, d = -0.436), respectively. (4) Conclusions: This study indicates that ePROMs are a time-feasible method for collecting data to guide patient-personalized treatment approaches.

Status of MyHealthWay and Suggestions for Widespread Implementation, Emphasizing the Utilization and Practical Use of Personal Medical Data.

In the Fourth Industrial Revolution, there is a focus on managing diverse medical data to improve healthcare and prevent disease. The challenges include tracking detailed medical records across multiple institutions and the necessity of linking domestic public medical entities for efficient data sharing. This study explores MyHealthWay, a Korean healthcare platform designed to facilitate the integration and transfer of medical data from various sources, examining its development, importance, and legal implications. To evaluate the management status and utilization of MyHealthWay, we analyzed data types, security, legal issues, domestic versus international issues, and infrastructure. Additionally, we discussed challenges such as resource and infrastructure constraints, regulatory hurdles, and future considerations for data management. The secure sharing of medical information via MyHealthWay can reduce the distance between patients and healthcare facilities, fostering personalized care and self-management of health. However, this approach faces legal challenges, particularly relating to data standardization and access to personal health information. Legal challenges in data standardization and access, particularly for secondary uses such as research, necessitate improved regulations. There is a crucial need for detailed governmental guidelines and clear data ownership standards at institutional levels. This report highlights the role of Korea's MyHealthWay, which was launched in 2023, in transforming healthcare through systematic data integration. Challenges include data privacy and legal complexities, and there is a need for data standardization and individual empowerment in health data management within a systematic medical big data framework.

Super-resolution deep neural network (SRDNN) based multi-image steganography for highly secured lossless image transmission

Information exchange and communication through the Internet are one of the most crucial aspects of today’s information technology world. The security of information transmitted online has grown to be a critical concern, particularly in the transfer of medical data. To overcome this, the data must be delivered securely without being altered or lost. This can be possibly done by combining the principles of cryptography and steganography. In the recent past, steganography is used with simpler methods like the least significant bit manipulation technique, in order to encode a lower-resolution image into a higher-resolution image. Here, we attempt to use deep neural networks to combine many two-dimensional colour images of the same resolution into a single cover image with the same resolution. In this technique, many secret images are concealed inside a single cover image using deep neural networks. The embedded cover image is then encrypted using a 3D chaotic map for diffusion and elliptic curve cryptography (ECC) for confusion to increase security.Supporting the fact that neural networks experience losses, the proposed system recovers up to 93% of the hidden image concealed in the original image. As the secret image features are identified and combined along with the cover image, the time complexity involved in the security process is minimized by 78% compared to securing the original data.

Adaptive RPL Routing Optimization Model for Multimedia Data Transmission using IOT

Objectives: The main objectives of this research endeavor encompass the development of the Adaptive RPL Optimization (ARPLO) model to enhance data transmission efficiency within IoT networks. This includes constructing a grid-based network structure optimized for data transfer, selecting the most suitable nodes as grid head nodes to maximize network lifespan while minimizing energy consumption, implementing an innovative objective function-driven approach to optimize parent node selection, and integrating an Adaptive Deep Neural Network (ADNN) to accurately classify medical data. Methods: The research methodology entails several key steps. A grid-based network structure is established with IoT nodes and root nodes, where a DODAG process incorporating DIO messages is utilized for node ranking. To enhance energy efficiency, the Trickle algorithm is employed for control message optimization. Grid head nodes are chosen based on metrics such as root node fairness, residual energy, and load influence index. The novel Middle Order Optimal Routing (MOOR) objective function is utilized to optimize routing decisions. ADNN is implemented for precise medical data classification. The proposed model's performance is evaluated through simulation in a Python environment. Findings: The research findings demonstrate that the ARPLO model yields notable benefits compared to existing models. It achieves higher energy efficiency, improved throughput, enhanced packet delivery ratio (PDR), and an extended network lifespan. The Trickle algorithm contributes to efficient control message optimization. The MOOR-based routing approach improves multimedia medical data transfer. Moreover, the integration of ADNN enhances the accuracy of data classification, particularly in healthcare applications. The research outcomes align with the broader field's existing values and reports while offering novel insights that contribute to enhancing the existing knowledge base. ARPLO protocol performance reveals that there is increase of throughput of 31.2%, PDR by 7.12%, lifetime of 10.7 % with reduction of energy consumption by 12.72%, control overhead by 31.01% and end-to-end delay by 33.01%. Novelty: The novelty of this research lies in its comprehensive approach that integrates a grid-based network structure, MOOR-based optimization, and ADNN-based classification. The incorporation of the Trickle algorithm for energy-efficient communication is an innovative feature. The introduction of new metrics for grid head node selection, along with the application of the MOOR objective function for multimedia medical data routing, showcases the research's innovative contributions. Keywords: Internet of Things (IoT), RPL (Routing Protocol for Low­Power and Lossy Networks), Optimization, Routing, Multimedia, Healthcare

Mathematical analysis of wavelet-based multi-image compression in medical diagnostics

Accurate diagnosis and well-informed treatment choices are only possible with the help of sophisticated medical imaging technology. The introduction of digital imaging methods has led to a dramatic rise in the volume of data associated with medical images, which in turn has led to difficulties in their storage, transmission, and administration. It is crucial to utilize effective image compression techniques to address these challenges without compromising the diagnostic integrity of the pictures. The wavelet transform has matured into a potent method for striking a good compromise between picture quality and file size reduction while compressing. The safe and efficient transfer of medical image data is a major concern in today’s healthcare settings. In this academic investigation, we investigate how wavelet transform-based techniques may be used to enhance medical picture compression. The proposed techniques optimize compression ratios while maintaining diagnostic picture quality by making use of the wavelet transform’s multi-resolution and frequency localization features. To address the unique challenges given by medical image collections, various iterations of the wavelet transform and compression techniques are investigated. Through a series of detailed tests involving several medical picture modalities, the effectiveness of these technologies is thoroughly evaluated, demonstrating their effectiveness in achieving significant data reduction without sacrificing clinical information.

Guarding Digital Health: Deep Learning for Attack Detection in Medical IoT

The Internet of Medical Things (IoMT) has transformed the healthcare sector by allowing for the continuous gathering, transfer, and analysis of medical data via networked devices.However, as IoMT has become more integrated into healthcare systems, it has been vulnerable to cybersecurity attacks, posing dangers to patient safety and data privacy. This study tackles the essential challenge of safeguarding the IoMT ecosystem by implementing cutting-edge deep learning approaches for threat identification and avoidance.Our study begins by looking at a variety of cyber risks that the IoMT faces, including as backdoors, DDoS assaults, MITM assaults, ransomware and SQL injections.These hostile operations may impair medical services and jeopardize critical med- ical data.To combat these risks, we offer a strong intrusion detection system, that constantly monitors the IoMT architecture for any unusual activity.The findings of this research contribute significantly to the development of a secure and resilient medical IoT ecosystem.The performance metrics of the proposed intrusion detection system, including accuracy, precision, recall, and F1 Score, were evaluated for four different models: CNN, Autoencoder, Transformer Network, and LSTM Network.The LSTM Network ex- hibited exceptional performance, achieving an accuracy of 97%, precision of 93%, recall of 96%, and an F1 Score of 94%.The compiled model, named Adam-LSTM, leverages the strengths of the Adam optimizer and LSTM architecture. Comparative anal- ysis with alternative algorithms demonstrated the superiority of the proposed approach in accurately and efficiently identifying intrusions.The IoMT can supply dependable healthcare services to patients while preserving their sensitive medical information by proactively securing digital health through deep learning-based threat detection.This study provides the groundwork for future breakthroughs in medical IoT cybersecurity solutions and promotes the idea of a smarter and safer healthcare landscape.

Анализ требований к конфиденциальности и обмену данными цифрового здравоохранения

The article provides answers to immediate questions associated with the state regulation of confidentiality of information and exchange of data in the era of digital healthcare. The institute of medical confidentiality which originated in the ancient world is still evolving throughout the development of medical law. In the current era of global digitalization, however, the issues related to data confidentiality have become more relevant than ever. With all the modern technologies and digital health care platforms on the rise, new challenges associated with protection of these patients are emerging. To ensure the reliable protection of patient’s personal and medical information, doctors and medical institutions have to meet data security standards. It becomes vital to develop effective strategies and mechanisms to prevent unauthorized access and data leakage due to a larger volume of electronic medical records and digital data exchange. Strict rules and standards regulating collection, storage and transfer of medical data belong to a key aspect in this area. The Russian Federation is making great efforts to create the legislation which could protect the rights of patients and made medical establishments to follow the high standards of confidentiality, and to develop technical aids that provide data encryption and protection against hacker attacks.

Secure Healthcare Website using Blockchain

Blockchain technology is consistently being an interested topic of research for researchers, and multiple of businesses as organizations are highly benefitted from its features. Blockchain technology has many benefits for the healthcare sector, including security, privacy, secrecy, and decentralisation. Privacy and protection of medical data are some of the important issues to looked after while availing any medical services. Secure storage is something which people look up for. Efficient Healthcare System is which allows the users to communicate securely without any worries about their data. This study is about how the blockchain technology can be applied to change the EHR systems and potentially provide a fix for these problems. A framework integrating EHR and blockchain to enable a secure medical data transfer between patients and hospitals is the objective of this study.

Authentication of Medical Documentation with Privacy Protection and Sharing in Cloud

There is a growing need for better medical care as a result of the adoption of clever electronic gadgets and the development of cloud and cloudlet technologies. Data collection, storage, and dissemination are the three main steps in the medical data processing chain. The traditional healthcare system frequently requires the transfer of medical data to the cloud, resulting in the use of sensitive user data and communication energy. The exchange of medical information is a crucial and difficult problem. In this study, we suggest a brand-new human services framework that makes use of cloud let's flexibility. Security assurance, information sharing, and intrusion detection are among the components of cloudlets. We apply the Number Theory Research Unit (NTRU) technique to encrypt the user's bodily data gathered by wearable devices during the data gathering phase. This information is then efficiently transferred to nearby cloudlets. We also present a trust model to help users choose reliable partners that want to share cloudlet-stored data.

Perspectives of patients with colorectal cancer liver metastases on e-consultation in transmural care: a qualitative study

BackgroundComprehensive cancer networks have been established to deliver high-quality care for patients with cancer. Logistic challenges are faced, when patients need to be referred for specialized treatments. Despite strengthened privacy legislations, digital platforms are increasingly used to consult specialists from dedicated liver centers or refer patients with colorectal cancer liver metastases (CRLM) for local treatment strategies. This qualitative study aimed to explore the perspectives of patients with CRLM regarding e-consultation of transmural specialists.MethodsA focus group study was conducted. Patients referred from regional hospitals to an academic liver center for treatment of CRLM were asked to participate. Focus group discussions were audio-recorded and transcribed verbatim. A thematic content analysis of data was conducted, comprising open, axial, and selective coding of the transcripts. The consolidated criteria for reporting qualitative research (COREQ) were used.ResultsTwo focus groups were held, involving 11 patients and 8 relatives. Three major themes were identified with regard to e-consultation in transmural care: ‘data management’, ‘expertise’, and ‘information and coordination’. Confidence in the expertise of physicians appeared most important during the course of treatment, as patients experienced uncertainty after diagnosis of cancer. Despite the privacy risks, use of digital communication platforms to contact experts in the field were strongly endorsed to improve eligibility for potentially curative treatment. Moreover, e-consultation of specialists may reduce waiting times, due to effective coordination of care.ConclusionInitiatives to improve medical data transfer between care providers were encouraged to achieve effective coordination of oncological care. The potential hazard of privacy violation associated with digital data exchange is accepted by patients and their relatives, provided that use of digital data improves patient’s own health care, research or education.

Hernia registry as the first stage of the development of a ‘learning healthcare system'

This paper aims to assess the effectiveness of current approaches to information exchange among key actors in the healthcare system and their impact on healthcare delivery. Specifically, it compares existing approaches to collecting medical data in herniology and explores their potential use in ‘learning healthcare systems'. However, current nosological registries do not fully meet the needs of these systems. One solution is to modify medical information systems to serve as a source of medical data of sufficient quality to supplement existing medical data lakes. Achieving this requires collaboration among clinicians, researchers, medical information system engineers, and data scientists. Establishing legal regulations for the transfer of medical data to scientific and educational organizations is necessary to fully realize the potential of this new type of nosological registry for advancing science and medicine.

IoT based medical image encryption using linear feedback shift register – Towards ensuring security for teleradiology applications

Telemedicine gains prominence in today’s scenario and image security plays a vital role in the healthcare sector for authenticating data transfer. Medical data, when transferred through a cloud network should be encrypted to ensure security, this research work proposes a medical image encryption scheme using a linear feedback shift register (LFSR). The LFSR generates pseudo-random numbers and shuffles the pixel position. The encrypted image was transferred through a cloud platform, receiver side node decrypts the data to recover the original image. The Digital Imaging and Communications in Medicine (DICOM) computed tomography images are utilized in this research work and for IoT implementation, a Raspberry Pi B+ processor was used. The IoT implementation facilitates data transfer through the cloud network. The performance validation was done by metrics and the results reveal the proficiency of the encryption/decryption model. The decrypted image quality was evaluated in terms of the Peak to Signal Noise Ratio (PSNR) and Means Square Error (MSE), low value of the correlation coefficient proves the robustness of the encryption. The outcome of the research work paves the way for the researchers in the secure transfer of medical data through the cloud platform.

Chain code strategy for lossless storage and transfer of segmented binary medical data

Obtaining a 3D medical visualization is a tedious process requiring several processing steps (such as segmentation) and assigning various rendering parameters (such as color and opacity). Current systems use video/image exporting or snapshots to save results. Such vendor-dependent tools not only prevent the possibility of further interactions but also creates additional large-size data that is problematic to store in PACS over time and hard to transfer for teleradiology applications. To overcome, alternative strategies propose a representation of the visualizations, which only store segmentation masks that contains the binary form of segmented data. Unfortunately, existing compression methods are limited to effectively compress the volumetric data. In this study, lossless storage of binary segmented data is effectively performed by two newly-proposed chain code approaches. Particularly-two novel contributions are presented: 1. The dictionary of normalized angle difference is improved as a new chain symbol coding procedure, namely normalized angle difference, by adding new symbols to the dictionary aiming to generate a low-entropy symbol sequence for medical volumes. 2. A new volumetric approach that utilizes 26 symbols to encode volumetric data is developed. Each slice is visited, and the contour of the segmented object is codified such that eight different vectors for each slice (pointing to one of the four faces of each voxel, plus four towards one of its edges) are obtained. The developed methods are tested on diverse volumetric segmented data and compared to existing standards. It is shown that the proposed methods outperform well-established techniques.

A novel architecture of a secure medical data storage management system based on tangle

Electronic health information exchange allows providers to healthcare providers and patients to securely access vital medical information of the patient and share it in electronically with other healthcare providers. However, existing health systems for the exchange of medical data faced with a number of problems, in particular: security, confidentiality and fragmentation of medical data. Data information systems are isolated because each medical institution, laboratory, doctor use their own system. The patient cannot access their medical information in the right time, since patients still do not have access to these systems. And the exchange of medical information between doctors occurs through mail or patients carry their own medical records with them admission to reception. In healthcare, there is no single system that allows both doctors and patients to have permanent access to the history the patient's illness and prescribed treatment. The paper considers the electronic health system eHealth. In this system, all medical data is stored on central component, and physicians using information systems gets access to them. However, this system has its limitations: single point of failure, lack of a unified medical information systems and patients still do not have access to their medical data. Therefore, one of the solutions to these problems is to create systems for exchanging medical information using directed acyclic graph (DAG). DAG will allow freely exchange data with a guarantee of their integrity. The patient's medical records will be stored in a distributed ledger, and not on the server of the medical institution. So no one can change patient data or deny him access to his own data. The use of DAG is considered on the example of the transfer of medical patient data to the doctor. The process of creating and sending medical data to DAG is illustrated with a specific example.

Digital-Twin-Enabled IoMT System for Surgical Simulation Using rAC-GAN

A digital-twin (DT)-enabled Internet of Medical Things (IoMT) system for telemedical simulation is developed, systematically integrated with mixed reality (MR), 5G cloud computing, and a generative adversarial network (GAN) to achieve remote lung cancer implementation. Patient-specific data from 90 lung cancer with pulmonary embolism (PE)-positive patients, with 1372 lung cancer control groups, were gathered from Qujing and Dehong, and then transmitted and preprocessed using 5G. A novel robust auxiliary classifier GAN (rAC-GAN)-based intelligent network is employed to facilitate lung cancer with the PE prediction model. To improve the accuracy and immersion during remote surgical implementation, a real-time operating room perspective from the perception layer with a surgical navigation image is projected to the surgeon’s helmet in the application layer using the DT-based MR guide clue with 5G. The accuracies of the area under the curve (AUC) of our new intelligent IoMT system were 0.92 and 0.93. Furthermore, the pathogenic features learned from our rAC-GAN model are highly consistent with the statistical epidemiological results. The proposed intelligent IoMT system generates significant performance improvement to process substantial clinical data at cloud centers and shows a novel framework for remote medical data transfer and deep learning analytics for DT-based surgical implementation.

A novel architecture of a secure medical system using dag

Electronic health information exchange allows providers to healthcare providers and patients to securely access vital medical information of the patient and share it in electronically with other healthcare providers. The paper considers the electronic health system eHealth. The patient's medical records will be stored in a distributed ledger, and not on the server of the medical institution. So no one can change patient data or deny him access to his own data. The use of DAG is considered on the example of the transfer of medical patient data to the doctor. The process of creating and sending medical data to DAG is illustrated with a specific example.

Blockchain-Based Internet-of-Things for Secure Transmission of Medical Data in Rural Areas

Abstract Patients’ medical data are extremely sensitive information during storage and transfer, and it needs the highest security level. Furthermore, these records must frequently be linked to patient medical data, and then the linked medical data are securely transmitted to the healthcare center. In this study, a Blockchain-Based Traceable Data Sharing method is proposed to securely transfer the medical data. A Paillier homomorphic encryption method is used to prevent data theft or attacks from occurring in the cloud as a result of the transfer of medical data there. It prevents intravenous third parties, which executes arithmetic operations on the ciphertext. Then the encrypted data are stored in the cloud and to remove clone nodes in the gateway, a software-defined networking technology is introduced. Then a Blockchain-Based Traceable Data Sharing is proposed to ensure data privacy and authenticity while maintaining data privacy at the point of data transmission. Data are then encrypted using a new Enhanced Cipher Text-Policy Encryption Attribute-based Encryption (E-CP-ABE). Private blockchain transfers are carried out on the chain, supporting fine-grain access control with flexible access policies and creating a private key in E-CP-ABE. The presented technique is executed in Matlab software of version R2020a. The performance parameters are encryption, and decryption time, mean square error (MSE), peak signal-to-noise ratio (PSNR), sensitivity, respectively. The encryption process function is nearly 8% superior than the existing methods and the decryption time is 14% greater than other methods. As a result, this study shows that the research approach outperformed in terms of encryption time and decryption time, as well as PSNR, MSE, and sensitivity. This technique outperforms other state-of-the-art algorithms in terms of imperceptibility and robustness against various attacks. Consequently, this approach is more reliable than previous methods for the transmission of medical data.

Medical Image Encryption using Latin Image Cipher Algorithm

Image processing has significant applications in the health care sector, medical data processing, analysis, storage, and transfer. The Latin Image Cipher algorithm was proposed in this work for the encryption of medical images. The encryption algorithm proposed in this research work comprises Latin square whitening, substitution, and permutation. The efficiency of the algorithm was also validated by inducing noise in the input images. The performance validation of the proposed algorithm was validated by the histogram analysis and correlation analysis. The information entropy measure also validates the proficiency of the encryption/decryption algorithm. The algorithm was developed on MATLAB 20021 and tested on COVID 19 CT images.

Security and privacy issues in federated healthcare – An overview

Abstract Securing medical records is a significant task in Healthcare communication. The major setback during the transfer of medical data in the electronic medium is the inherent difficulty in preserving data confidentiality and patients’ privacy. The innovation in technology and improvisation in the medical field has given numerous advancements in transferring the medical data with foolproof security. In today’s healthcare industry, federated network operation is gaining significance to deal with distributed network resources due to the efficient handling of privacy issues. The design of a federated security system for healthcare services is one of the intense research topics. This article highlights the importance of federated learning in healthcare. Also, the article discusses the privacy and security issues in communicating the e-health data.

Feasibility study of multi-site split learning for privacy-preserving medical systems under data imbalance constraints in COVID-19, X-ray, and cholesterol dataset

It seems as though progressively more people are in the race to upload content, data, and information online; and hospitals haven’t neglected this trend either. Hospitals are now at the forefront for multi-site medical data sharing to provide ground-breaking advancements in the way health records are shared and patients are diagnosed. Sharing of medical data is essential in modern medical research. Yet, as with all data sharing technology, the challenge is to balance improved treatment with protecting patient’s personal information. This paper provides a novel split learning algorithm coined the term, “multi-site split learning”, which enables a secure transfer of medical data between multiple hospitals without fear of exposing personal data contained in patient records. It also explores the effects of varying the number of end-systems and the ratio of data-imbalance on the deep learning performance. A guideline for the most optimal configuration of split learning that ensures privacy of patient data whilst achieving performance is empirically given. We argue the benefits of our multi-site split learning algorithm, especially regarding the privacy preserving factor, using CT scans of COVID-19 patients, X-ray bone scans, and cholesterol level medical data.