Hasil untuk "Medical technology"

C. Rose, A. Parker, B. Jefferson et al.

The safe disposal of human excreta is of paramount importance for the health and welfare of populations living in low income countries as well as the prevention of pollution to the surrounding environment. On-site sanitation (OSS) systems are the most numerous means of treating excreta in low income countries, these facilities aim at treating human waste at source and can provide a hygienic and affordable method of waste disposal. However, current OSS systems need improvement and require further research and development. Development of OSS facilities that treat excreta at, or close to, its source require knowledge of the waste stream entering the system. Data regarding the generation rate and the chemical and physical composition of fresh feces and urine was collected from the medical literature as well as the treatability sector. The data were summarized and statistical analysis was used to quantify the major factors that were a significant cause of variability. The impact of this data on biological processes, thermal processes, physical separators, and chemical processes was then assessed. Results showed that the median fecal wet mass production was 128 g/cap/day, with a median dry mass of 29 g/cap/day. Fecal output in healthy individuals was 1.20 defecations per 24 hr period and the main factor affecting fecal mass was the fiber intake of the population. Fecal wet mass values were increased by a factor of 2 in low income countries (high fiber intakes) in comparison to values found in high income countries (low fiber intakes). Feces had a median pH of 6.64 and were composed of 74.6% water. Bacterial biomass is the major component (25–54% of dry solids) of the organic fraction of the feces. Undigested carbohydrate, fiber, protein, and fat comprise the remainder and the amounts depend on diet and diarrhea prevalence in the population. The inorganic component of the feces is primarily undigested dietary elements that also depend on dietary supply. Median urine generation rates were 1.42 L/cap/day with a dry solids content of 59 g/cap/day. Variation in the volume and composition of urine is caused by differences in physical exertion, environmental conditions, as well as water, salt, and high protein intakes. Urine has a pH 6.2 and contains the largest fractions of nitrogen, phosphorus, and potassium released from the body. The urinary excretion of nitrogen was significant (10.98 g/cap/day) with urea the most predominant constituent making up over 50% of total organic solids. The dietary intake of food and fluid is the major cause of variation in both the fecal and urine composition and these variables should always be considered if the generation rate, physical, and chemical composition of feces and urine is to be accurately predicted.

Aleksandra A. Kolodziejczyk, Jong Kyoung Kim, Valentine Svensson et al.

A. Ekblaw, Asaph Azaria, J. Halamka et al.

C. Lee, H. Yoon

The concept of big data, commonly characterized by volume, variety, velocity, and veracity, goes far beyond the data type and includes the aspects of data analysis, such as hypothesis-generating, rather than hypothesis-testing. Big data focuses on temporal stability of the association, rather than on causal relationship and underlying probability distribution assumptions are frequently not required. Medical big data as material to be analyzed has various features that are not only distinct from big data of other disciplines, but also distinct from traditional clinical epidemiology. Big data technology has many areas of application in healthcare, such as predictive modeling and clinical decision support, disease or safety surveillance, public health, and research. Big data analytics frequently exploits analytic methods developed in data mining, including classification, clustering, and regression. Medical big data analyses are complicated by many technical issues, such as missing values, curse of dimensionality, and bias control, and share the inherent limitations of observation study, namely the inability to test causality resulting from residual confounding and reverse causation. Recently, propensity score analysis and instrumental variable analysis have been introduced to overcome these limitations, and they have accomplished a great deal. Many challenges, such as the absence of evidence of practical benefits of big data, methodological issues including legal and ethical issues, and clinical integration and utility issues, must be overcome to realize the promise of medical big data as the fuel of a continuous learning healthcare system that will improve patient outcome and reduce waste in areas including nephrology.

M. Bakator, D. Radosav

In this review the application of deep learning for medical diagnosis is addressed. A thorough analysis of various scientific articles in the domain of deep neural networks application in the medical field has been conducted. More than 300 research articles were obtained, and after several selection steps, 46 articles were presented in more detail. The results indicate that convolutional neural networks (CNN) are the most widely represented when it comes to deep learning and medical image analysis. Furthermore, based on the findings of this article, it can be noted that the application of deep learning technology is widespread, but the majority of applications are focused on bioinformatics, medical diagnosis and other similar fields.

Yi Chen, Shuai Ding, Zheng Xu et al.

G. Currie, K. Hawk, E. Rohren et al.

Artificial intelligence (AI) in medical imaging is a potentially disruptive technology. An understanding of the principles and application of radiomics, artificial neural networks, machine learning, and deep learning is an essential foundation to weave design solutions that accommodate ethical and regulatory requirements, and to craft AI-based algorithms that enhance outcomes, quality, and efficiency. Moreover, a more holistic perspective of applications, opportunities, and challenges from a programmatic perspective contributes to ethical and sustainable implementation of AI solutions.

M. Attaran

ABSTRACT Patients and healthcare practitioners are faced with the challenge of accessing, managing, integrating, and sharing health records securely. Patients should be able to manage their health records anywhere in the world, keep track of medical background, give access to data, and share those with any healthcare professional securely. Direct access to data for patients and a more robust data-sharing infrastructure could better prepare the healthcare system to manage public health threats during the emergence of deadly disease outbreak such as COVID-19. Current technologies in use by the healthcare industry do not adequately address these requirements due to limitations related to privacy, security, and full ecosystem interoperability. This paper conducted a literature review to find out the pivotal roles blockchain technology play in solving some of the most critical and challenging issues facing the healthcare industry. This paper identifies challenges and opportunities for implementing blockchain technology in healthcare and summarizes health-related blockchain products and key players offering solutions across different applications. In doing this, our research extends and complements existing blockchain research in healthcare.

Alexander Fleming, John R. Petrie, R. Bergenstal et al.

Poshmaal Dhar, T. Rocks, Rasika M. Samarasinghe et al.

ABSTRACT Augmented reality (AR) is a relatively new technology that allows for digitally generated three-dimensional representations to be integrated with real environmental stimuli. AR can make use of smart phones, tablets, or other devices to achieve a highly stimulating learning environment and hands-on immersive experience. The use of AR in industry is becoming widespread with applications being developed for use not just for entertainment and gaming but also healthcare, retail and marketing, education, military, travel and tourism, automotive industry, manufacturing, architecture, and engineering. Due to the distinct learning advantages that AR offers, such as remote learning and interactive simulations, AR-based teaching programs are also increasingly being adopted within medical schools across the world. These advantages are further highlighted by the current COVID-19 pandemic, which has caused an even greater shift towards online learning. In this review, we investigate the use of AR in medical training/education and its effect on students’ experiences and learning outcomes. This includes the main goals of AR-based learning, such as to simplify the delivery and enhance the comprehension of complex information. We also describe how AR can enhance the experiences of medical students, by improving knowledge and understanding, practical skills and social skills. These concepts are discussed within the context of specific AR medical training programs, such as HoloHuman, OculAR SIM, and HoloPatient. Finally, we discuss the challenges of AR in learning and teaching and propose future directions for the use of this technology in medical education.

S. V. M. Sagheer, S. N. George

Abstract Over the past two decades, medical imaging and diagnostic techniques have gained immense attraction due to the rapid development in computing, internet, data storage and wireless technology. The reflection of these advancements has become evident in the field of medicine and medical sciences which enables the diagnosis and treatment of various diseases in a more fruitful manner. Furthermore, medical imaging is frequently justified in the follow up of a disease which is already diagnosed and treated. Medical images like any other form of imaging techniques are susceptible to noise and artifacts. Noise can be random or white noise with an even frequency distribution or frequency dependent noise introduced by a device's mechanism or signal processing algorithms. The presence of noise makes the images unclear and may perplex the identification and analysis of diseases which may result heavy losses including deaths. Hence, denoising of medical images is a mandatory and essential pre-processing technique for further medical image processing stages. The aim of this paper is to conduct a detailed analysis of the different denoising techniques used for medical imaging modalities which include the 2D/3D Ultrasound (US), Magnetic Resonance (MR), Computed Tomography (CT) and Positron Emission Tomography (PET) images.

Xiaoyan Xu, Atheer Awad, Pamela Robles-Martinez et al.

Three-dimensional (3D) printing is transforming manufacturing paradigms within healthcare. Vat photopolymerization 3D printing technology combines the benefits of high resolution and favourable printing speed, offering a sophisticated approach to fabricate bespoke medical devices and drug delivery systems. Herein, an overview of the vat polymerization techniques, their unique applications in the fields of drug delivery and medical device fabrication, material examples and the advantages they provide within healthcare, is provided. The outstanding challenges and drawbacks presented by this technology are also discussed. It is forecast that the adoption of 3D printing could pave the way for a personalised health system, advancing from traditional treatments pathways towards digital healthcare and streamlining a new cyber era.

Israa Abu-elezz, Asma Hassan, Anjanarani Nazeemudeen et al.

BACKGROUND The application of blockchain technology is being explored to improve the interoperability of patient health information between healthcare organisations while maintaining the privacy and security of data. OBJECTIVES The objective of this scoping review is to explore and categorise the benefits and threats of blockchain technology application in a healthcare system. METHODS Databases such as PubMed, CINAHL, IEEE, Springer, and ScienceDirect were searched using a combination of terms related to blockchain, healthcare, benefits and threats. Backward-reference list checking was conducted to identify other relevant references. Study selection process was performed in three steps based on PRISMA flow diagram. Extracted data were synthesised and presented narratively using tables and figures. RESULTS The search resulted in 84 relevant studies that have been conducted of which only 37 unique studies were included in this review. Eight benefits of blockchain were categorised in either patient related-benefits (security and authorisation, personalised healthcare, patients' health data tracking, and patient's health status monitoring) or organisational-related benefits (health information exchange, pharmaceutical supply chain, clinical trials, and medical insurance management). Meanwhile, eight threats of blockchain were categorised into three groups: organisational threats (installation and transaction costs, interoperability issues, and lack of technical skills), social threats (social acceptance and regulations issues), and technological threats (scalability issues, authorisation and security issues, high energy consumption, and slow processing speeds). CONCLUSION Blockchain is a viable technology that can improve the healthcare data sharing and storing system owing to its decentralisation, immutability, transparency and traceability features. However, many healthcare organisations remain hesitant to adopt blockchain technology due to threats such as security and authorisation issues, interoperability issues and lack of technical skills related to blockchain technology.

T. Baniasadi, S. M. Ayyoubzadeh, N. Mohammadzadeh

Despite the benefits of using virtual reality (VR) in medical education and treatment, some challenges and limitations result in the uselessness or misuse of this technology. Therefore, recognizing potential challenges related to VR might be helpful in the strategic decision-making process to implement and develop this technology in the healthcare field. Accordingly, our review aimed to determine the challenges associated with the application of VR in the field of medical education and treatment. We searched Science Direct, Google Scholar, and PubMed databases for relevant papers using a defined search query. We restricted the search to articles in English or Persian language published by the end of 2018. The main challenges of developing and using VR with educational and therapeutic objectives are categorized as general and specific. General challenges include reduced face-to-face communications, education, cost challenges, users’ attitudes, and specific challenges such as designing, safety considerations, VR side effects, evaluation, and validation of VR applications. Challenges related to VR will have different effects, thus identifying each of them helps to determine the solutions for each challenge. Also, it is suggested to develop and update laws, standards, and protocols, which play an important role in increasing the effective application of VR at the national level.

F. Tuma

Students often feel overwhelmed by the volume and complexity of knowledge and skills required to learn. Along with this challange, educational technology has been gradually introduced in medical education to facilitate learning and improve outcomes. It became an essential part of communication, storing and transferring information, audio-visual media use and production, and knowledge sharing. Technology's role has been expanding from a mere tool of study and inquiry to an approach and integrated use in education. Its use in medical education is continuously evolving. However, the impact and optimal use of various technology applications are not clearly defined. There are multiple challenges facing educators to choose the right application for the specific educational purpose. Hence, studies and evaluation reviews are needed to inform the better-defined use of educational technology. This review aims to discuss and evaluate various educational technology applications in medical education, focusing on interactive learning during lectures. Lectures and other group learning sessions are common activities used by medical schools. Promoting interactive learning in large groups is known to be challenging. The advances in technology to facilitate communication and promote interaction is a promising adjunct for lectures interactivity.

Junlong Cheng, Sheng Tian, Long Yu et al.

In recent years, deep learning technology has shown superior performance in different fields of medical image analysis. Some deep learning architectures have been proposed and used for computational pathology classification, segmentation, and detection tasks. Due to their simple, modular structure, most downstream applications still use ResNet and its variants as the backbone network. This paper proposes a modular group attention block that can capture feature dependencies in medical images in two independent dimensions: channel and space. By stacking these group attention blocks in ResNet-style, we obtain a new ResNet variant called ResGANet. The stacked ResGANet architecture has 1.51-3.47 times fewer parameters than the original ResNet and can be directly used for downstream medical image segmentation tasks. Many experiments show that the proposed ResGANet is superior to state-of-the-art backbone models in medical image classification tasks. Applying it to different segmentation networks can improve the baseline model in medical image segmentation tasks without changing the network architecture. We hope that this work provides a promising method for enhancing the feature representation of convolutional neural networks (CNNs) in the future.

Elena A Wood, B. Ange, D. Miller

Background: The effects of Artificial Intelligence (AI) technology applications are already felt in healthcare in general and in the practice of medicine in the disciplines of radiology, pathology, ophthalmology, and oncology. The expanding interface between digital data science, emerging AI technologies and healthcare is creating a demand for AI technology literacy in health professions. Objective: To assess medical student and faculty attitudes toward AI, in preparation for teaching AI foundations and data science applications in clinical practice in an integrated medical education curriculum. Methods: An online 15-question semi-structured survey was distributed among medical students and faculty. The questionnaire consisted of 3 parts: participant’s background, AI awareness, and attitudes toward AI applications in medicine. Results: A total of 121 medical students and 52 clinical faculty completed the survey. Only 30% of students and 50% of faculty responded that they were aware of AI topics in medicine. The majority of students (72%) and faculty (59%) learned about AI from the media. Faculty were more likely to report that they did not have a basic understanding of AI technologies (χ2, P = .031). Students were more interested in AI in patient care training, while faculty were more interested in AI in teaching training (χ2, P = .001). Additionally, students and faculty reported comparable attitudes toward AI, limited AI literacy and time constraints in the curriculum. There is interest in broad and deep AI topics. Our findings in medical learners and teaching faculty parallel other published professional groups’ AI survey results. Conclusions: The survey conclusively proved interest among medical students and faculty in AI technology in general, and in its applications in healthcare and medicine. The study was conducted at a single institution. This survey serves as a foundation for other medical schools interested in developing a collaborative programming approach to address AI literacy in medical education.

Peng Zhang, Chang-Bo Zheng, Zhen Chen et al.

Ping Chen, Zicheng Huang, Xiangming Wang et al.

We propose VL-DUN, a principled framework for joint All-in-One Medical Image Restoration and Segmentation (AiOMIRS) that bridges the gap between low-level signal recovery and high-level semantic understanding. While standard pipelines treat these tasks in isolation, our core insight is that they are fundamentally synergistic: restoration provides clean anatomical structures to improve segmentation, while semantic priors regularize the restoration process. VL-DUN resolves the sub-optimality of sequential processing through two primary innovations. (1) We formulate AiOMIRS as a unified optimization problem, deriving an interpretable joint unfolding mechanism where restoration and segmentation are mathematically coupled for mutual refinement. (2) We introduce a frequency-aware Mamba mechanism to capture long-range dependencies for global segmentation while preserving the high-frequency textures necessary for restoration. This allows for efficient global context modeling with linear complexity, effectively mitigating the spectral bias of standard architectures. As a pioneering work in the AiOMIRS task, VL-DUN establishes a new state-of-the-art across multi-modal benchmarks, improving PSNR by 0.92 dB and the Dice coefficient by 9.76\%. Our results demonstrate that joint collaborative learning offers a superior, more robust solution for complex clinical workflows compared to isolated task processing. The codes are provided in https://github.com/cipi666/VLDUN.

Davide Luglietto, Alessandro De Benedictis, Alessandra Marasi et al.

Over the last decade, the quality of neurosurgical procedures dramatically improved, also thanks to the development and increased accessibility of several technological recourses (e.g., imaging, neuronavigation, neurophysiology, microscopy), allowing to plan increasingly complex approaches, while reducing the risk of postoperative complications. Among these resources, three-dimensional rendering and simulation systems, such as virtual and augmented reality, provide a high-quality visual reconstruction of brain structures and interaction with advanced anatomical models. Although the usefulness of these systems is now widely recognized, the additional availability of proprioceptive (haptic) feedback might help to further enhance the realism of surgical simulation. A systematic literature review on the application of haptic technology in simulation of cranial neurosurgical procedures was made. Inclusion criteria were the usage of simulators with haptic feedback for specific neurosurgical procedures whereas the studies that did not include an evaluation of the surgical simulation system by a surgeon were excluded. According to inclusion and exclusion criteria, 10 studies were selected. Simulation in neurosurgery still lacks a system capable of rehearsing the entire procedure—from skin incision to skin closure—while providing both visual and proprioceptive feedback. Consequently, further advancements in this area are necessary.