Hasil untuk "Medical technology"

Carsten Lexa

This chapter presents an assessment of the rapidly evolving state of health-related technology and its developing impact on health care, medical education, patient care, and care delivery. This is collectively referred to as the digital health movement in medicine. This chapter provides a broader understanding of how digital health is changing not only the practice of medicine, but the consumer market that pertains to health care and medicine at large. The authors discuss the current state of digital health in medicine, the challenges of conventionally assessing digital health-related competencies, and the relative difficulty of adapting contemporary medical education to include digital health modalities into traditional undergraduate medical education. This chapter also showcases three unique case studies of early-adopting medical institutions that have created digital health learning opportunities for their undergraduate medical student population.

B. Shastri, A. Tait, T. F. D. Lima et al.

Research in photonic computing has flourished due to the proliferation of optoelectronic components on photonic integration platforms. Photonic integrated circuits have enabled ultrafast artificial neural networks, providing a framework for a new class of information processing machines. Algorithms running on such hardware have the potential to address the growing demand for machine learning and artificial intelligence in areas such as medical diagnosis, telecommunications, and high-performance and scientific computing. In parallel, the development of neuromorphic electronics has highlighted challenges in that domain, particularly related to processor latency. Neuromorphic photonics offers sub-nanosecond latencies, providing a complementary opportunity to extend the domain of artificial intelligence. Here, we review recent advances in integrated photonic neuromorphic systems, discuss current and future challenges, and outline the advances in science and technology needed to meet those challenges. Photonics offers an attractive platform for implementing neuromorphic computing due to its low latency, multiplexing capabilities and integrated on-chip technology.

Sangbae Kim, Cecilia Laschi, B. Trimmer

Sudeep Tanwar, Karan Parekh, Richard Evans

Abstract Modern healthcare systems are characterized as being highly complex and costly. However, this can be reduced through improved health record management, utilization of insurance agencies, and blockchain technology. Blockchain was first introduced to provide distributed records of money-related exchanges that were not dependent on centralized authorities or financial institutions. Breakthroughs in blockchain technology have led to improved transactions involving medical records, insurance billing, and smart contracts, enabling permanent access to and security of data, as well as providing a distributed database of transactions. One significant advantage of using blockchain technology in the healthcare industry is that it can reform the interoperability of healthcare databases, providing increased access to patient medical records, device tracking, prescription databases, and hospital assets, including the complete life cycle of a device within the blockchain infrastructure. Access to patients’ medical histories is essential to correctly prescribe medication, with blockchain being able to dramatically enhance the healthcare services framework. In this paper, several solutions for improving current limitations in healthcare systems using blockchain technology are explored, including frameworks and tools to measure the performance of such systems, e.g., Hyperledger Fabric, Composer, Docker Container, Hyperledger Caliper, and the Wireshark capture engine. Further, this paper proposes an Access Control Policy Algorithm for improving data accessibility between healthcare providers, assisting in the simulation of environments to implement the Hyperledger-based eletronic healthcare record (EHR) sharing system that uses the concept of a chaincode. Performance metrics in blockchain networks, such as latency, throughput, Round Trip Time (RTT). have also been optimized for achieving enhanced results. Compared to traditional EHR systems, which use client-server architecture, the proposed system uses blockchain for improving efficiency and security.

J. Alander, I. Kaartinen, A. Laakso et al.

The purpose of this paper is to give an overview of the recent surgical intraoperational applications of indocyanine green fluorescence imaging methods, the basics of the technology, and instrumentation used. Well over 200 papers describing this technique in clinical setting are reviewed. In addition to the surgical applications, other recent medical applications of ICG are briefly examined.

Wei Zhang, C. Mi

M. Modat, G. Ridgway, Z. Taylor et al.

F. H. Adler

S. Issenberg, W. McGaghie, I. R. Hart et al.

Reinhard C. Laubenbacher, B. Mehrad, Ilya Shmulevich et al.

Orrin I. Franko, Timothy F. Tirrell

Katharina Eckstein, Constantin Ulrich, Michael Baumgartner et al.

Large-scale pre-training holds the promise to advance 3D medical object detection, a crucial component of accurate computer-aided diagnosis. Yet, it remains underexplored compared to segmentation, where pre-training has already demonstrated significant benefits. Existing pre-training approaches for 3D object detection rely on 2D medical data or natural image pre-training, failing to fully leverage 3D volumetric information. In this work, we present the first systematic study of how existing pre-training methods can be integrated into state-of-the-art detection architectures, covering both CNNs and Transformers. Our results show that pre-training consistently improves detection performance across various tasks and datasets. Notably, reconstruction-based self-supervised pre-training outperforms supervised pre-training, while contrastive pre-training provides no clear benefit for 3D medical object detection. Our code is publicly available at: https://github.com/MIC-DKFZ/nnDetection-finetuning.

Shuangyu Chen, Wenqian Chen, Tinghui Xu et al.

Gastric cancer (GC) remains one of the leading causes of cancer-related mortality worldwide, with limited responses to immune checkpoint blockade (ICB) therapies in most patients. Increasing evidence indicates that the tumor immune microenvironment (TIME) plays a crucial role in immunotherapy outcomes. Among various metabolic abnormalities in the TIME, dysregulated lipid metabolism has emerged as a critical determinant of immune cell fate, differentiation, and function. In this review, we comprehensively summarize the current understanding of the immune landscape in GC, focusing on how altered lipid metabolism reshapes immune cell populations—including tumor-associated macrophages (TAMs), dendritic cells (DCs), regulatory T cells (Tregs), myeloid-derived suppressor cells (MDSCs), and cytotoxic CD8+ T cells. We highlight key metabolic pathways such as fatty acid oxidation(FAO), cholesterol homeostasis, and lipid uptake that impact immune cell activity, contributing to immune evasion and therapeutic resistance. Importantly, we explore emerging therapeutic strategies targeting lipid metabolism, including inhibitors of cluster of differentiation 36 (CD36), fatty acid synthase (FASN), and sterol regulatory element-binding protein 1 (SREBP1) and discuss their synergistic potential when combined with ICB therapies. In conclusion, lipid metabolic reprogramming represents a promising yet underexplored axis in modulating antitumor immunity in GC. Integrating metabolic intervention with immunotherapy holds potential to overcome current treatment limitations and improve clinical outcomes. Future studies incorporating spatial omics and single-cell profiling will be essential to elucidate cell-type specific metabolic dependencies and foster translational breakthroughs.

Gen Chen, Hao Tang, Yang Yang et al.

Objectives: The quantity of regions of interest (ROIs) constitutes the primary determinant of the time investment in image analysis. In the context of proton density fat-fraction (PDFF) magnetic resonance imaging (MRI) conducted on liver grafts in ex vivo conditions, this research systematically examines various ROI sampling strategies. The findings of this study furnish essential insights, offering a foundation for optimizing time efficiency while ensuring precise assessment of hepatic steatosis before the crucial process of liver transplantation. Methods: This was a retrospective analysis of a prospective study and included 35 liver grafts with histopathological steatosis that underwent 3T PDFF MRI in ex vivo. One ROI of 1 cm2 was selected for each hepatic segment, and any combination of ROIs in 1–8 liver segments was used, resulting in 511 combinations. Using intraclass correlation coefficients (ICCs) and Bland-Altman analyses, the PDFFs of all these combinations were compared with the 9-ROI average PDFF. There was a moderate correlation between the average PDFF and the histological findings (R = 0.47, P＜0.01). Results: The average 9-ROI PDFF of all liver grafts was 4.07 ± 4.35 % (0.870–20.904). All strategies with ≥5 ROIs had intraclass correlation coefficient (ICC) ≥ 0.995 and absolute limits of agreement (|LOA|)≤ 1.5 %. Overall, 54 of 84 (67.5 %) 3-ROI sampling strategy had ICC ≥0.995, and 70 of 84 (70 %) had |LOA|≤ 1.5 %. A total of 111 of 126 (88.1 %) 4-ROI sampling strategy had ICC ≥0.995, and 125 of 126 (99.2 %) had |LOA| ≤ 1.5 %. Conclusions: The employment of the 5-ROI sampling strategy proves instrumental in both time conservation and precise assessment of hepatic steatosis within liver grafts during the ex vivo phase preceding liver transplantation.

Tim Schneider, Dirk Weyhe, Merle Schlender et al.

IntroductionThe primary objective of this study was to evaluate the efficiency and ergonomic benefits of a novel surgical lighting system developed within the SmartOT project. The developed system aims to automatically prevent shadows on the surgical field, eliminating the need for frequent manual adjustments, which is common with conventional surgical lights. Additionally, the study seeks to explore the feasibility of using EEG recordings as an objective method for assessing workplace strain in clinical settings, thereby laying the groundwork for future studies focused on reducing the workload of medical personnel.MethodsTo achieve these objectives, we conducted a passive Oddball experiment involving EEG measurements to assess the impact of the new lighting system on workplace strain. Participants performed a task requiring them to identify specific LEGO® pieces. The study involved 30 participants (13 females, 17 males), with errors being tracked as an additional measure of cognitive load. The experimental setup was informed by previous research, which established a method for objectively determining workload generated by AR and VR technologies in clinical settings. In that research, EEG signals were recorded during surgical planning under different conditions, revealing trends in cognitive load and validating the utility of EEG for workload assessment.ResultsThe NASA Task Load Index (NASA-TLX) analysis revealed significantly lower mental demand, temporal demand, effort, and frustration scores for the smart surgical lamp compared to the manual lamp conditions, with mandatory and optional adjustments. However, there were no significant differences between the smart and conventional lamp in the dimensions of physical demand and performance. Similarly, EEG recordings indicated a higher P300 amplitude at electrode Fz following the smart lamp condition (p = 0.037), reflecting less cognitive load; latencies did not differ between conditions. Error analysis confirmed fewer errors and shorter processing times for the smart lamp.ConclusionsThe measurements of NASA-TLX and EEG after running simulated surgical tasks showed that the SmartOT prototype significantly reduced errors and workload compared to the conventional surgical lamp. These findings reflect the capability of smart surgical lighting in improving patient safety and efficiency within operating theaters.

Chi-Fen Chang, Lih-Chyang Chen, Yei-Tsung Chen et al.

Background: Prostate cancer is a major global health burden, with biochemical recurrence (BCR) following radical prostatectomy affecting 20–40% of patients and posing significant challenges to prognosis and treatment. Emerging evidence suggests a critical role for differentially expressed in normal and neoplastic cell (DENN) domain-containing genes in oncogenesis; however, their implications in prostate cancer and BCR risk remain underexplored. Methods: This study systematically evaluated 151 single-nucleotide polymorphisms in DENN domain-containing genes in 458 patients with prostate cancer and BCR, followed by validation in an independent cohort of 185 patients. Results: Multivariate Cox regression analyses identified DENND2D rs610261 G>A as significantly associated with improved BCR-free survival in both cohorts (adjusted hazard ratio = 0.39, 95% confidence interval = 0.23–0.66, p = 0.001). Functional analysis revealed rs610261’s regulatory potential, with the protective A allele correlating with increased DENND2D expression in various human tissues. Compared to normal prostate tissues, DENND2D expression was reduced in prostate cancer, with higher expression being linked to favorable patient prognosis (p = 0.03). Gene set enrichment analysis revealed an association between DENND2D expression and the negative regulation of MYC target genes, including MAD2L1, ERH, and CLNS1A, which are overexpressed in prostate cancer and associated with poor survival. Furthermore, the elevated DENND2D expression promotes immune infiltration in prostate cancer, supporting its role in immune modulation. Conclusions: DENND2D is a prognostic biomarker for BCR in prostate cancer and offers new avenues for personalized treatment strategies.

Lie Cai, Andre Pfob

Background: Using artificial intelligence (AI) techniques for computational medical image analysis has shown promising results. However, colored images are often not readily available for AI analysis because of different coloring thresholds used across centers and physicians as well as the removal of clinical annotations. We aimed to develop an open-source tool that can adapt different color thresholds of HSV-colored medical images and remove annotations with a simple click. Materials and Methods: We built a function using MATLAB and used multi-center international shear wave elastography data (NCT 02638935) to test the function. We provide step-by-step instructions with accompanying code lines. Results: We demonstrate that the newly developed pre-processing function successfully removed letters and adapted different color thresholds of HSV-colored medical images. Conclusion: We developed an open-source tool for removing letters and adapting different color thresholds in HSV-colored medical images. We hope this contributes to advancing medical image processing for developing robust computational imaging algorithms using diverse multi-center big data. The open-source Matlab tool is available at https://github.com/cailiemed/image-threshold-adapting.

Xuehui Li, Pan Tang, Xinping Pang et al.

Abstract Alzheimer's disease (AD) is a neurodegenerative disorder characterized primarily by cognitive impairment. The motivation of this paper is to explore the impact of the visual information transmission pathway (V–H pathway) on AD, and the following feature were observed: Hemoglobin expression on the V–H pathway becomes dysregulated as AD occurs so as to the pathway becomes dysfunctional. According to the feature, the following conclusion was proposed: As AD occurs, abnormal tau proteins penetrate bloodstream and arrive at the brain regions of the pathway. Then the tau proteins or other toxic substances attack hemoglobin molecules. Under the attack, hemoglobin expression becomes more dysregulated. The dysfunction of V–H pathway has an impact on early symptoms of AD, such as spatial recognition disorder and face recognition disorder.

Fei Xiong, Jian Jiang, Yu-tong Li et al.

Purpose: To explore the feasibility of using wide-body detector Computed Tomography (CT) combined with Cardiac Motion Correction (CMC) technology for coronary Computed Tomography angiography (CCTA) in free breathing state and its impact on image quality. Methods: 120 patients who underwent CCTA scans at our institution from March 2023 to December 2023 were collected in this retrospective study. Recorded the coronary artery images before applying CMC technology as the control group, and the images after applying CMC technology as the experimental group. Patients were divided into two groups by different heart rate (HR), Group A (HR > 70 bpm) and Group B (HR ≤ 70 bpm), with 66 patients in group A and 54 patients in group B. Subjective image quality assessments were performed on the left anterior descending artery (LAD), left circumflex artery (LCX), and right coronary artery (RCA) proximal segments. These assessments were conducted by two senior radiologists using a double-blind approach and a 4-point scale (1 indicating poor image quality and 4 indicating excellent image quality). The subjective scores among the three coronary artery segments were analyzed by chi-square test, using Kappa coefficient to analyze the inter-observer agreement. Results: The control group had a subjective score of 3.37 ± 0.81/3.27 ± 0.83 for the LCX, 2.97 ± 0.85/2.93 ± 0.78 for the LAD, and 2.70 ± 0.75/2.80 ± 0.66 for the RCA. The experimental group had a subjective score of 3.90 ± 0.31/3.83 ± 0.38 for the LCX, 3.83 ± 0.38/3.87 ± 0.35 for the LAD, and 3.87 ± 0.35/3.83 ± 0.38 for the RCA. There was a great enhancement in the image quality observed in the experimental group when contrasted with the control group both in group A and group B (all P < 0.05). The inter-observer consistency of LCX, LAD and RCA were 0.726, 0.801 and 0.734 in control group and 0.769, 0.870 and 0.870 in experimental group. Conclusion: The utilization of CMC technology significantly enhances the quality of CCTA images acquired during free breathing.

Guoliang Liang, Guoliang Liang, Wenhao Zhang et al.

BackgroundAlthough a few studies have examined the correlation between low-density lipoprotein cholesterol (LDL-C) and mortality, no study has explored these associations in hypertensive populations. This study aims to investigate the relationship between low-density lipoprotein cholesterol and cardiovascular and all-cause mortality in adults with hypertension.MethodsHypertensive participants aged ≥18 years from the National Health and Nutrition Examination Survey 1999–2018 with blood lipid testing data and complete follow-up data until 31 December 2019 were enrolled in the analysis. Univariate and multivariate Cox regression were conducted for the calculation of hazard ratios and 95% confidence intervals. A restricted cubic spline curve was performed to visually represent the relationship between LDL-C and mortality. Kaplan–Meier survival analysis and stratification analysis were also carried out.ResultsWe finally analysed a cohort of 9,635 participants (49.6% male, mean age of 59.4 years). After a median follow-up of 98 months, there were 2,283 (23.7%) instances of all-cause fatalities, with 758 (7.9%) cases attributed to cardiovascular disease. Multivariate Cox regression analysis showed that lower levels of LDL-C were associated with a higher risk of all-cause and cardiovascular mortality; the LDL-C group’s lowest level (&lt;2.198 mmol/L) still showed a 19.6% increased risk of all-cause mortality (p = 0.0068) in the model that was completely adjusted. Both all-cause mortality and cardiovascular mortality showed a non-linear association with LDL-C concentration in restricted cubic spline regression analysis.ConclusionsIn individuals with hypertension, LDL-C was linked to cardiovascular and all-cause mortality. It was further demonstrated that this relationship was non-linear.