Hasil untuk "Medical technology"

J. Ruiz, M. Mintzer, R. Leipzig

E. Groos

M. Javaid, Abid Haleem

Abstract Background A significant number of the research paper on Medical cases using Additive manufacturing studied. Different applications of additive manufacturing technologies in the medical area analysed for providing the state of the art and direction of the development. The aim of work To illustrate the Additive Manufacturing technology as being used in medical and its benefits along-with contemporary and future applications. Materials and methods Literature Review based study on Additive Manufacturing that are helpful in various ways to address medical problems along with bibliometric analysis been done. Result Briefly described the review of forty primary applications of AM as used for medical purposes along with their significant achievement. Process chain development in the application of AM is identified and tabulated for every process chain member, its achievement and limitations for various references. There are five criteria which one can achieve through medical model when made through AM technology. To support the achievements and limitations of every criterion proper references are provided. The ongoing research is also classified according to the application of AM in medical with criteria, achievement and references. Eight major medical areas where AM is implemented have been identified along with primary references, objectives and advantages. Conclusion Paper deals with the literature review of the Medical application of Additive Manufacturing and its future. Medical models which are customised and sourced from data of an individual patient, which vary from patient to patient can well be modified and printed. Medical AM involves resources of human from the field of reverse engineering, medicine and biomaterial, design and manufacturing of bones, implants, etc. Additive Manufacturing can help solve medical problems with extensive benefit to humanity.

Karthik K. Tappa, Udayabhanu M. Jammalamadaka

The success of an implant depends on the type of biomaterial used for its fabrication. An ideal implant material should be biocompatible, inert, mechanically durable, and easily moldable. The ability to build patient specific implants incorporated with bioactive drugs, cells, and proteins has made 3D printing technology revolutionary in medical and pharmaceutical fields. A vast variety of biomaterials are currently being used in medical 3D printing, including metals, ceramics, polymers, and composites. With continuous research and progress in biomaterials used in 3D printing, there has been a rapid growth in applications of 3D printing in manufacturing customized implants, prostheses, drug delivery devices, and 3D scaffolds for tissue engineering and regenerative medicine. The current review focuses on the novel biomaterials used in variety of 3D printing technologies for clinical applications. Most common types of medical 3D printing technologies, including fused deposition modeling, extrusion based bioprinting, inkjet, and polyjet printing techniques, their clinical applications, different types of biomaterials currently used by researchers, and key limitations are discussed in detail.

C. Culmone, G. Smit, P. Breedveld

Abstract Goal Additive manufacturing, also known as 3D printing, has begun to play a significant role in the field of medical devices. This review aims to provide a comprehensive overview and classification of additively manufactured medical instruments for diagnostics and surgery by identifying medical and technical aspects. Methods A scientific literature search on additively manufactured medical instruments was conducted using the Scopus database. Results We categorized the relevant articles (71) by considering the novelty of each proposed instrument and its clinical application. Then, we analyzed the relevant articles by examining the reasons behind choosing additive manufacturing technology to produce instruments for diagnostics and surgery. Possible customization (27%) and Cost-effectiveness (23%) were the main reasons expressed. Technical specifications of the additive manufacturing technology and the material used were also analyzed, and a tendency of using material extrusion technology (35% of the applications) and polymeric materials (86% of the applications) was shown. Conclusions Additive manufacturing is opening the door to a new approach in the production of medical devices, which allows the complexity of their designs to be pushed to the extreme. However, we found that technical limitations need to be tackled and important aspects such as sterilization or debris contamination are still not considered to be relevant factors during the design and fabrication process. Keeping in mind the challenges of such a new field, additive manufacturing technology can be considered as a great opportunity to provide easy access to healthcare in developing countries as well as an important step toward patient-specific medicine.

Y. Miedany

Jiumeng Zhang, Qipeng Hu, Shuai Wang et al.

An additive manufacturing technology based on projection light, digital light processing (DLP), three-dimensional (3D) printing, has been widely applied in the field of medical products production and development. The precision projection light, reflected by a digital micromirror device of million pixels instead of one focused point, provides this technology both printing accuracy and printing speed. In particular, this printing technology provides a relatively mild condition to cells due to its non-direct contact. This review introduces the DLP-based 3D printing technology and its applications in medicine, including precise medical devices, functionalized artificial tissues, and specific drug delivery systems. The products are particularly discussed for their significance in medicine. This review indicates that the DLP-based 3D printing technology provides a potential tool for biological research and clinical medicine. While, it is faced to the challenges of scale-up of its usage and waiting period of regulatory approval.

A. Sakudo, Y. Yagyu, T. Onodera

Recent studies have shown that plasma can efficiently inactivate microbial pathogens such as bacteria, fungi, and viruses in addition to degrading toxins. Moreover, this technology is effective at inactivating pathogens on the surface of medical and dental devices, as well as agricultural products. The current practical applications of plasma technology range from sterilizing therapeutic medical devices to improving crop yields, as well as the area of food preservation. This review introduces recent advances and future perspectives in plasma technology, especially in applications related to disinfection and sterilization. We also introduce the latest studies, mainly focusing on the potential applications of plasma technology for the inactivation of microorganisms and the degradation of toxins.

Jin Sun, Xiaomin Yao, Shangping Wang et al.

Electronic medical records can help people prevent diseases, improve cure rates, provide a significant basis for medical institutions and pharmaceutical companies, and provide legal evidence for medical negligence and medical disputes. However, the integrity and security problems of electronic medical data still intractable. In this paper, based on the ciphertext policy attribute-based encryption system and IPFS storage environment, combined with blockchain technology, we constructed an attribute-based encryption scheme for secure storage and efficient sharing of electronic medical records in IPFS storage environment. Our scheme is based on ciphertext policy attribute encryption, which effectively controls the access of electronic medical data without affecting efficient retrieval. Meanwhile, we store the encrypted electronic medical data in the decentralized InterPlanetary File System (IPFS), which not only ensures the security of the storage platform but also solves the problem of the single point of failure. Besides, we leverage the non-tamperable and traceable nature of blockchain technology to achieve secure storage and search for medical data. The security proof shows that our scheme achieves selective security for the choose keyword attacks. Performance analysis and real data set simulation experiments shows that our scheme is efficient and feasible.

Anthony Jnr. Bokolo

Telemedicine and eHealth refer to the use of information and communication technology (ICT) embedded in software programs with highspeed telecommunications systems for delivery, management, and monitoring of healthcare services. Application of telemedicine have become timely while providing great potentials to protect both medical practitioners and patients, as well as limit social mobility of patients contributing to reduce the spread of the virus. This study employs data from the existing literature to describe the application of telemedicine and eHealth as a proactive measure to improve clinical care. Findings from this study present the significance of telemedicine and current applications adopted during the pandemic. More importantly, the findings present practical application of telemedicine and eHealth for clinical services. Also, polices initiated across the world to promote management of COVID-19 are discussed. Respectively, this study suggests that telemedicine and eHealth can be adopted in times of health emergency, as a convenient, safe, scalable, effective, and green method of providing clinical care.

Priti Tagde, Sandeep Tagde, Tanima Bhattacharya et al.

Blockchain and artificial intelligence technologies are novel innovations in healthcare sector. Data on healthcare indices are collected from data published on Web of Sciences and other Google survey from various governing bodies. In this review, we focused on various aspects of blockchain and artificial intelligence and also discussed about integrating both technologies for making a significant difference in healthcare by promoting the implementation of a generalizable analytical technology that can be integrated into a more comprehensive risk management approach. This article has shown the various possibilities of creating reliable artificial intelligence models in e-Health using blockchain, which is an open network for the sharing and authorization of information. Healthcare professionals will have access to the blockchain to display the medical records of the patient, and AI uses a variety of proposed algorithms and decision-making capability, as well as large quantities of data. Thus, by integrating the latest advances of these technologies, the medical system will have improved service efficiency, reduced costs, and democratized healthcare. Blockchain enables the storage of cryptographic records, which AI needs.

Zhongying Deng, Cheng Tang, Ziyan Huang et al.

Foundation models have demonstrated remarkable success across diverse domains and tasks, primarily due to the thrive of large-scale, diverse, and high-quality datasets. However, in the field of medical imaging, the curation and assembling of such medical datasets are highly challenging due to the reliance on clinical expertise and strict ethical and privacy constraints, resulting in a scarcity of large-scale unified medical datasets and hindering the development of powerful medical foundation models. In this work, we present the largest survey to date of medical image datasets, covering over 1,000 open-access datasets with a systematic catalog of their modalities, tasks, anatomies, annotations, limitations, and potential for integration. Our analysis exposes a landscape that is modest in scale, fragmented across narrowly scoped tasks, and unevenly distributed across organs and modalities, which in turn limits the utility of existing medical image datasets for developing versatile and robust medical foundation models. To turn fragmentation into scale, we propose a metadata-driven fusion paradigm (MDFP) that integrates public datasets with shared modalities or tasks, thereby transforming multiple small data silos into larger, more coherent resources. Building on MDFP, we release an interactive discovery portal that enables end-to-end, automated medical image dataset integration, and compile all surveyed datasets into a unified, structured table that clearly summarizes their key characteristics and provides reference links, offering the community an accessible and comprehensive repository. By charting the current terrain and offering a principled path to dataset consolidation, our survey provides a practical roadmap for scaling medical imaging corpora, supporting faster data discovery, more principled dataset creation, and more capable medical foundation models.

Mark A. Engelhardt

Health is the foundation of an engaged and happy life, and modern humans have been the fortunate beneficiaries of great advances in medical technology (Collins, 2015). With each new technology, more clues become available to decipher the problems that plague our well-being. The advent of individualized information from cheaper genome sequencing, the Internet of Things, and widespread collection of health data may enable researchers to solve formerly inaccessible health problems. However, when this massive quantity of data is spread out with limited access, is in forms not conducive to sharing, cannot be easily packaged for computational methods, or does not exist as a complete record, it is impossible to perform the complex data analysis required to arrive at solutions.

R. Ahmad, K. Salah, R. Jayaraman et al.

Objective. Telehealth and telemedicine systems aim to deliver remote healthcare services to mitigate the spread of COVID-9. Also, they can help to manage scarce healthcare resources to control the massive burden of COVID-19 patients in hospitals. However, a large portion of today's telehealth and telemedicine systems are centralized and fall short of providing necessary information security and privacy, operational transparency, health records immutability, and traceability to detect frauds related to patients’ insurance claims and physician credentials. Methods. The current study has explored the potential opportunities and adaptability challenges for blockchain technology in telehealth and telemedicine sector. It has explored the key role that blockchain technology can play to provide necessary information security and privacy, operational transparency, health records immutability, and traceability to detect frauds related to patients’ insurance claims and physician credentials. Results. Blockchain technology can improve telehealth and telemedicine services by offering remote healthcare services in a manner that is decentralized, tamper-proof, transparent, traceable, reliable, trustful, and secure. It enables health professionals to accurately identify frauds related to physician educational credentials and medical testing kits commonly used for home-based diagnosis. Conclusions. Wide deployment of blockchain in telehealth and telemedicine technology is still in its infancy. Several challenges and research problems need to be resolved to enable the widespread adoption of blockchain technology in telehealth and telemedicine systems.

P. Sahi, D. Mishra, T. Singh

The coronavirus pandemic has shaken the mankind to its core. Social distancing is the most important preventive strategy for the spread of this contagion, short of a vaccine. Implementation of the same has forced many countries in to a complete lock-down. Closure of schools and universities has made education uncertain at all levels. Medical education is no exception. In this pandemic, the need for uninterrupted generation of future doctors is felt more than ever in our living memory. Continuity of medical education is thus imperative. While “Live” patient contact is an irreplaceable tenet of clinical teaching, these extraordinary times demand exceptional measures. Pedagogical innovations involving technology and simulation based teaching (Online lectures, video case vignettes, virtual simulators, webcasting, online chat-rooms) need to be brought to the forefront. Since the medical educators have been pushed inevitably to rely on technology-based learning, they should not only embrace it but also develop and evaluate its sustainability and application in preclinical and clinical setting. Meanwhile, the students, whose medical education is stuck in this pandemic time, should realize that there is no better teacher than a first-hand experience, and they are eyewitnesses to the making of history.

Lemar Abdi, Francisco Caetano, Amaan Valiuddin et al.

In medical imaging, unsupervised out-of-distribution (OOD) detection offers an attractive approach for identifying pathological cases with extremely low incidence rates. In contrast to supervised methods, OOD-based approaches function without labels and are inherently robust to data imbalances. Current generative approaches often rely on likelihood estimation or reconstruction error, but these methods can be computationally expensive, unreliable, and require retraining if the inlier data changes. These limitations hinder their ability to distinguish nominal from anomalous inputs efficiently, consistently, and robustly. We propose a reconstruction-free OOD detection method that leverages the forward diffusion trajectories of a Stein score-based denoising diffusion model (SBDDM). By capturing trajectory curvature via the estimated Stein score, our approach enables accurate anomaly scoring with only five diffusion steps. A single SBDDM pre-trained on a large, semantically aligned medical dataset generalizes effectively across multiple Near-OOD and Far-OOD benchmarks, achieving state-of-the-art performance while drastically reducing computational cost during inference. Compared to existing methods, SBDDM achieves a relative improvement of up to 10.43% and 18.10% for Near-OOD and Far-OOD detection, making it a practical building block for real-time, reliable computer-aided diagnosis.

Yuwen Chen, Zafer Yildiz, Qihang Li et al.

Manual annotation of volumetric medical images, such as magnetic resonance imaging (MRI) and computed tomography (CT), is a labor-intensive and time-consuming process. Recent advancements in foundation models for video object segmentation, such as Segment Anything Model 2 (SAM 2), offer a potential opportunity to significantly speed up the annotation process by manually annotating one or a few slices and then propagating target masks across the entire volume. However, the performance of SAM 2 in this context varies. Our experiments show that relying on a single memory bank and attention module is prone to error propagation, particularly at boundary regions where the target is present in the previous slice but absent in the current one. To address this problem, we propose Short-Long Memory SAM 2 (SLM-SAM 2), a novel architecture that integrates distinct short-term and long-term memory banks with separate attention modules to improve segmentation accuracy. We evaluate SLM-SAM 2 on four public datasets covering organs, bones, and muscles across MRI, CT, and ultrasound videos. We show that the proposed method markedly outperforms the default SAM 2, achieving an average Dice Similarity Coefficient improvement of 0.14 and 0.10 in the scenarios when 5 volumes and 1 volume are available for the initial adaptation, respectively. SLM-SAM 2 also exhibits stronger resistance to over-propagation, reducing the time required to correct propagated masks by 60.575% per volume compared to SAM 2, making a notable step toward more accurate automated annotation of medical images for segmentation model development.

Xiao Wang, Wenxuan Shi, Yu Jin et al.

Abstract The escalating hazards posed by bacterial infections underscore the imperative for pioneering advancements in next-generation antibacterial modalities and treatments. Present therapeutic methodologies are frequently impeded by the constraints of insufficient biofilm infiltration and the absence of precision in pathogen-specific targeting. In this current study, we have used chlorin e6 (Ce6), zeolitic imidazolate framework-8 (ZIF-8), polydopamine (PDA), and UBI peptide to formulate an innovative nanosystem meticulously engineered to confront bacterial infections and effectually dismantle biofilm architectures through the concerted mechanism of photodynamic therapy (PDT)/photothermal therapy (PTT) therapies, including in-depth research, especially for oral bacteria and oral biofilm. Ce6@ZIF-8-PDA/UBI nanosystem, with effective adhesion and bacteria-targeting, affords a nuanced bacterial targeting strategy and augments penetration depth into oral biofilm matrices. The Ce6@ZIF-8-PDA/UBI nanosystem potentiated bacterial binding and aggregation. Upon exposure to red-light (RL) irradiation, Ce6@ZIF-8-PDA/UBI showed excellent antibacterial effect on S. aureus, E. coli, F. nucleatum, and P. gingivalis and exceptional light-driven antibiofilm activity to P. gingivalis biofilm, which was a result of the efficient bacterial localization mediated by PDA/UBI, as well as the PDT/PTT facilitated by Ce6/PDA interactions. Collectively, these versatile nanoplatforms augur a promising and strategic avenue for controlling infection and biofilm, thereby holding significant potential for future integration into clinical paradigms. The original application of the developed nanosystem in oral biofilms also provides a new strategy for effective oral infection treatment.

Nafees Ahmed, Vishwas Gaur, Madhu Kamle et al.

Lakshay Kumar, Subhabrata Maiti

Aim: Facial imaging technology has become a pivotal tool in modern medical practice, particularly within fields such as maxillofacial prosthodontics, orthodontics, and smile design. The creation of digital twins, or virtual patients, enhances diagnostic accuracy, aids in treatment planning, and improves outcome prediction. The aim of the study was to assess the accuracy of various facial scanners, determine overall accuracy of each scanner, and identify which scanner demonstrates superior accuracy in specific facial regions. Settings and Design: An observational crossover study. Materials and Methods: Cone beam computed tomography volumetric scan was used as a control group, as it has been considered as a gold standard in terms of accuracy. For comparison, scan data were obtained from three different scanners, namely Carestream facial scanner, Medit intraoral scanner for facial scan, and MetiSmile face scanner. The standard tessellation language files thus obtained were compared for accuracy in Geomagic X software by superimposition technique and were evaluated for their accuracy using various reference points on the face. Statistical Analysis Used: Normality was confirmed using the Shapiro–Wilk test. One-way analysis of variance for comparison among groups and Tukey test for pairwise comparison was used using SPSS software (IBM SPSS version 29 USA). Results: The study concluded that MetiSmile was the best facial scanner among the three groups with a mean discrepancy of (0.35 ± 0.33) mm and P = 0.001, indicating significant difference between the scanners. Conclusion: Each scanner evaluated demonstrated acceptable performance, with notable variations attributable to their distinct scanning methodologies. Among these, the MetiSmile scanner emerged as the most accurate, delivering the most favorable results in terms of accuracy.