Since its renaissance, deep learning (DL) has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propelling us into the so-called artificial intelligence (AI) era. It is known that the success of AI is mostly attributed to the availability of big data with annotations for a single task and the advances in high-performance computing. However, medical imaging presents unique challenges that confront DL approaches. In this survey article, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in DL are addressing these issues. We cover the topics of network architecture, sparse and noisy labels, federating learning, interpretability, uncertainty quantification, and so on. Then, we present several case studies that are commonly found in clinical practice, including digital pathology and chest, brain, cardiovascular, and abdominal imaging. Rather than presenting an exhaustive literature survey, we instead describe some prominent research highlights related to these case study applications. We conclude with a discussion and presentation of promising future directions.
The integration of artificial intelligence (AI) into medical imaging has guided in an era of transformation in healthcare. This literature review explores the latest innovations and applications of AI in the field, highlighting its profound impact on medical diagnosis and patient care. The innovation segment explores cutting-edge developments in AI, such as deep learning algorithms, convolutional neural networks, and generative adversarial networks, which have significantly improved the accuracy and efficiency of medical image analysis. These innovations have enabled rapid and accurate detection of abnormalities, from identifying tumors during radiological examinations to detecting early signs of eye disease in retinal images. The article also highlights various applications of AI in medical imaging, including radiology, pathology, cardiology, and more. AI-based diagnostic tools not only speed up the interpretation of complex images but also improve early detection of disease, ultimately delivering better outcomes for patients. Additionally, AI-based image processing facilitates personalized treatment plans, thereby optimizing healthcare delivery. This literature review highlights the paradigm shift that AI has brought to medical imaging, highlighting its role in revolutionizing diagnosis and patient care. By combining cutting-edge AI techniques and their practical applications, it is clear that AI will continue shaping the future of healthcare in profound and positive ways.
Data mining technology can search for potentially valuable knowledge from a large amount of data, mainly divided into data preparation and data mining, and expression and analysis of results. It is a mature information processing technology and applies database technology. Database technology is a software science that researches manages, and applies databases. The data in the database are processed and analyzed by studying the underlying theory and implementation methods of the structure, storage, design, management, and application of the database. We have introduced several databases and data mining techniques to help a wide range of clinical researchers better understand and apply database technology.
Ana M. Barragán-Montero, U. Javaid, G. Valdes
et al.
Artificial intelligence (AI) has recently become a very popular buzzword, as a consequence of disruptive technical advances and impressive experimental results, notably in the field of image analysis and processing. In medicine, specialties where images are central, like radiology, pathology or oncology, have seized the opportunity and considerable efforts in research and development have been deployed to transfer the potential of AI to clinical applications. With AI becoming a more mainstream tool for typical medical imaging analysis tasks, such as diagnosis, segmentation, or classification, the key for a safe and efficient use of clinical AI applications relies, in part, on informed practitioners. The aim of this review is to present the basic technological pillars of AI, together with the state-of-the-art machine learning methods and their application to medical imaging. In addition, we discuss the new trends and future research directions. This will help the reader to understand how AI methods are now becoming an ubiquitous tool in any medical image analysis workflow and pave the way for the clinical implementation of AI-based solutions.
The isolation wards, institutional quarantine centers, and home quarantine are generating a huge amount of bio-medical waste (BMW) worldwide since the outbreak of novel coronavirus disease-2019 (COVID-19). The personal protective equipment, testing kits, surgical facemasks, and nitrile gloves are the major contributors to waste volume. Discharge of a new category of BMW (COVID-waste) is of great global concern to public health and environmental sustainability if handled inappropriately. It may cause exponential spreading of this fatal disease as waste acts as a vector for SARS-CoV-2, which survives up to 7 days on COVID-waste (like facemasks). Proper disposal of COVID-waste is therefore immediately requires to lower the threat of pandemic spread and for sustainable management of the environmental hazards. Henceforth, in the present article, disinfection technologies for handling COVID-waste from its separate collection to various physical and chemical treatment steps have been reviewed. Furthermore, policy briefs on the global initiatives for COVID-waste management including the applications of different disinfection techniques have also been discussed with some potential examples effectively applied to reduce both health and environmental risks. This article can be of great significance to the strategy development for preventing/controlling the pandemic of similar episodes in the future.
Md Ariful Islam Mozumder, Muhammad Mohsan Sheeraz, Ali Athar
et al.
Metaverse is defined as a collection of technology gadgets and metaverse connected to IoT, Blockchain, Artificial Intelligence, and all the other tech industries including the medical area. IoT and Metaverse are the digital twins, Metaverse is using maximum IoT devices in their virtual workstation. This data has a unique identifying tag and is used as traceable data in the blockchain-based Metaverse. In the Metaverse, such data is becoming a valuable resource for artificial intelligence. Metaverse uses artificial intelligence and blockchain technology to build a digital virtual world where you can safely and freely engage in social and economic activities that transcend the limits of the real world, and the application of these latest technologies will be expedited. In this paper, we are going to describe what technologies metaverse is using and metaverse potentiality in medical healthcare.
We would like to express our gratitude to all authors who contributed to the Special Issue of "Artificial Intelligence Advances for Medical Computer-Aided Diagnosis" by providing their excellent and recent research findings for AI-based medical diagnosis [...].
Abstract Background This study investigates how structural changes in deep medullary vein (DMV), glymphatic system dysfunction, and cognitive decline are interconnected in cerebral small vessel disease (CSVD), with a focus on whether impaired glymphatic function acts as a mediator in this relationship. Methods Clinical and MRI data from 93 CSVD patients were retrospectively analyzed. DMV burden was assessed using a semiquantitative scoring system (0–3 points per region), based on the visibility of DMVs in six anatomical regions on susceptibility-weighted imaging, yielding a total score ranging from 0 to 18. Glymphatic system function was evaluated using the diffusion tensor image analysis along the perivascular space (DTI-ALPS) index. Global cognitive function was assessed with the Montreal Cognitive Assessment (MoCA). Spearman correlation analysis, general linear modeling, and mediation analysis were conducted to examine the relationships among the variables. Results DMV scores(which higher scores indicate poorer venous visibility)were significantly negatively correlated with MoCA scores ( r = -0.48, p < 0.001) and with the DTI-ALPS index ( r = -0.28, p < 0.001), while the DTI-ALPS index was positively correlated with MoCA scores ( r = 0.35, p < 0.05). Mediation analysis indicated that the DTI-ALPS index partially mediated the effect of DMV burden on cognitive performance, accounting for 14.08% of the total effect. Conclusions This study suggests that DMV structural abnormalities may exacerbate CSVD-related cognitive impairment by disrupting glymphatic function. DMV scoring may serve as a potential imaging biomarker, providing a foundation for early identification and intervention.
Lee J. Evitts, Philip W. Miller, Chiara Da Pieve
et al.
Background: The emergence and growth of fusion technology enables investigative studies into its applications beyond typical power production facilities. This study seeks to determine the viability of medical isotope production with the neutrons produced in an example large fusion device. Using FISPACT-II (a nuclear inventory code) and a simulated fusion spectrum, the production yields of a significant number of potentially clinically relevant (both in use and novel) medical isotopes were calculated. Comparative calculations were also conducted against existing production routes. Results: Depending on the neutron flux of the fusion device, it could be an ideal technology to produce alpha-emitters such as 212Bi/212Pb, it may be able to contribute to the production of 99mTc/99Mo, and could offer an alternative route in the production a few Auger-emitting candidates. There is also a long list of beta-emitting nuclides where fusion technology may be best placed to produce over existing technologies including 67Cu, 90Y and 47Sc. Conclusions: It is theoretically viable to produce existing and novel medical isotopes with fusion technology. However, a significant number of assumptions form the basis of this study which would need to be studied further for any particular nuclide of interest.
Vision-language foundation models (VLMs) have shown great potential in feature transfer and generalization across a wide spectrum of medical-related downstream tasks. However, fine-tuning these models is resource-intensive due to their large number of parameters. Prompt tuning has emerged as a viable solution to mitigate memory usage and reduce training time while maintaining competitive performance. Nevertheless, the challenge is that existing prompt tuning methods cannot precisely distinguish different kinds of medical concepts, which miss essentially specific disease-related features across various medical imaging modalities in medical image classification tasks. We find that Large Language Models (LLMs), trained on extensive text corpora, are particularly adept at providing this specialized medical knowledge. Motivated by this, we propose incorporating LLMs into the prompt tuning process. Specifically, we introduce the CILMP, Conditional Intervention of Large Language Models for Prompt Tuning, a method that bridges LLMs and VLMs to facilitate the transfer of medical knowledge into VLM prompts. CILMP extracts disease-specific representations from LLMs, intervenes within a low-rank linear subspace, and utilizes them to create disease-specific prompts. Additionally, a conditional mechanism is incorporated to condition the intervention process on each individual medical image, generating instance-adaptive prompts and thus enhancing adaptability. Extensive experiments across diverse medical image datasets demonstrate that CILMP consistently outperforms state-of-the-art prompt tuning methods, demonstrating its effectiveness. Code is available at https://github.com/usr922/cilmp.
Introduction: Adult exposure to endocrine-disrupting chemicals (EDCs) may reduce muscle mass and strength; however, few studies considered EDC mixtures and their potential mechanisms. Objectives: We aimed to explore associations of EDC mixtures with adult muscle mass and strength, the modifying effects of diet and exercise, as well as the potential metabolic perturbations through plasma metabolome. Methods: We included 127 adults from a panel study that repeated measures across 3 seasons. We measured 110 EDCs spanning 12 groups in plasma and urine, along with the plasma metabolome. Bayesian kernel machine regression (BKMR), Bayesian weighted quantile sum regression, and quantile-based g-computation were employed to assess the mixture effects and relative contributions. Key EDCs were defined as those with weights exceeding the group average in at least two models. Stratified analyses were employed to investigate the modifying effects of diet and exercise. A meet-in-the-middle (MITM) approach was applied to characterize the underlying mechanisms. Results: BKMR results revealed significant negative associations between 7 EDC groups and both appendicular skeletal muscle mass (ASM) and hand-grip strength (HGS), namely per- and polyfluoroalkyl substances, polycyclic aromatic hydrocarbons, organophosphate pesticides, bisphenols, neonicotinoids, atrazine, and parabens. Three multi-exposure models identified 22 and 17 key EDCs linked to decreased ASM and HGS, respectively. Mixtures of these key EDCs were associated with decreases in both ASM and HGS, with significantly attenuated effects observed among participants with healthy diets or regular exercise. MITM approach identified overlapping pathways linking key EDC mixtures to ASM, including arachidonic acid, linoleic acid, and alpha-linolenic acid metabolism. Key EDC Mixtures were negatively associated with glycocyamine, which was positively associated with ASM. Conclusions: Adult exposure to EDC mixtures was linked to reduced ASM and HGS, whereas healthy diets and regular exercise mitigated such impairment. Downregulated glycocyamine and altered fatty acid metabolism were potential mechanisms underlying the decreased ASM.
Svetlana S. Konnova, Pavel A. Lepilin, Anastasia A. Zanishevskaya
et al.
Biosensor technologies in medicine, as in many other areas, are replacing labor-intensive methods of monitoring human health. This paper presents the results of experimental studies on label-free sensors based on a hollow core microstructured optical waveguide (HC-MOW) for human blood serum analysis. The MOWs with a hollow core of 247.5 µm in diameter were manufactured and used in our work. These parameters allow the hollow core to be filled with high-viscosity solutions due to the capillary properties of the fiber. Calculations of the spectral properties of the HC-MOW fiber were carried out and experimentally confirmed. Twenty-one blood serum samples from volunteers were analyzed using standard photometry (commercial kits) and an experimental biosensor. The obtained transmission spectra were processed by the principal component analysis method and conclusions were drawn about the possibility of using this biosensor in point-of-care medicine. A significant difference was shown between the blood serum of healthy patients and patients with confirmed diagnoses and a long history of cardiovascular system abnormalities. Algorithms for spectra processing using the Origin program are presented.
Anna Wajs-Bonikowska, Ewa Maciejczyk, Łukasz Szoka
et al.
This study investigates the essential oil (EO) isolated from the seeds and cones of Canadian hemlock (<i>Tsuga canadensis</i>), highlighting notable differences in their chemical composition and biological activities. The seed EO was uniquely dominated by oxygenated derivatives of monoterpene hydrocarbons, particularly bornyl acetate (40%), whereas the cone EO exhibited higher levels of monoterpene hydrocarbons such as α-pinene (23%), β-pinene (20%), and myrcene (23%). A significant finding was the strong cytotoxic activity of cone EO against melanoma cell lines, with IC<sub>50</sub> values as low as 0.104 ± 0.015 μL/mL, compared to the minimal effects of seed EO. Additionally, cone EO demonstrated stronger antimicrobial activity, with lower minimum inhibitory concentrations (MICs) against Gram-positive and Gram-negative bacteria, further highlighting its therapeutic potential. Lipophilic extracts from seeds were characterized by unsaturated fatty acids (linoleic, oleic, and sciadonic acids—specific to conifers) and bioactive molecules with high antioxidant and nutritional potential, such as β-tocopherol, β-sitosterol, and campestrol. These findings underscore the unique chemical composition of <i>T. canadensis</i> seed EO and its lipophilic extract, along with the potent cytotoxic and antimicrobial properties of cone EO, offering insights into their potential applications in natural products for pharmaceutical and therapeutic uses.
Hemalatha Kanakarajan, Wouter De Baene, Patrick Hanssens
et al.
Abstract Background and purpose Accurate segmentation of brain metastases on Magnetic Resonance Imaging (MRI) is tedious and time-consuming for radiologists that could be optimized with deep learning (DL). Previous studies assessed several DL algorithms focusing only on training and testing the models on the planning MRI only. The purpose of this study is to evaluate well-known DL approaches (nnU-Net and MedNeXt) for their performance on both planning and follow-up MRI. Materials and methods Pre-treatment brain MRIs were retrospectively collected for 255 patients at Elisabeth-TweeSteden Hospital (ETZ): 201 for training and 54 for testing, including follow-up MRIs for the test set. To increase heterogeneity, we added the publicly available MRI scans from the Mathematical oncology laboratory of 75 patients to the training data. The performance was compared between the two models, with and without the addition of the public data. To statistically compare the Dice Similarity Coefficient (DSC) of the two models trained on different datasets over multiple time points, we used Linear Mixed Models. Results All models obtained a good DSC (DSC > = 0.93) for planning MRI. MedNeXt trained with combined data provided the best DSC for follow-ups at 6, 15, and 21 months (DSC of 0.74, 0.74, and 0.70 respectively) and jointly the best DSC for follow-ups at three months with MedNeXt trained with ETZ data only (DSC of 0.78) and 12 months with nnU-Net trained with combined data (DSC of 0.71). On the other hand, nnU-Net trained with combined data provided the best sensitivity and FNR for most follow-ups. The statistical analysis showed that MedNeXt provides higher DSC for both datasets and the addition of public data to the training dataset results in a statistically significant increase in performance in both models. Conclusion The models achieved a good performance score for planning MRI. Though the models performed less effectively for follow-ups, the addition of public data enhanced their performance, providing a viable solution to improve their efficacy for the follow-ups. These algorithms hold promise as a valuable tool for clinicians for automated segmentation of planning and follow-up MRI scans during stereotactic radiosurgery treatment planning and response evaluations, respectively. Clinical trial number Not applicable.
BackgroundCervical cancer incidence and mortality rates in the United States have substantially declined over recent decades, primarily driven by reductions in squamous cell carcinoma cases. However, the trend in recent years remains unclear. This study aimed to explore the trends in cervical cancer incidence and mortality, stratified by demographic and tumor characteristics from 1975 to 2018.MethodsThe age-adjusted incidence, incidence-based mortality, and relative survival of cervical cancer were calculated using the Surveillance, Epidemiology, and End Results (SEER)-9 database. Trend analyses with annual percent change (APC) and average annual percent change (AAPC) calculations were performed using Joinpoint Regression Software (Version 4.9.1.0, National Cancer Institute).ResultsDuring 1975–2018, 49,658 cervical cancer cases were diagnosed, with 17,099 recorded deaths occurring between 1995 and 2018. Squamous cell carcinoma was the most common histological type, with 34,169 cases and 11,859 deaths. Over the study period, the cervical cancer incidence rate decreased by an average of 1.9% (95% CI: −2.3% to −1.6%) per year, with the APCs decreased in recent years (−0.5% [95% CI: −1.1 to 0.1%] in 2006–2018). Squamous cell carcinoma incidence trends closely paralleled overall cervical cancer patterns, but the incidence of squamous cell carcinoma in the distant stage increased significantly (1.1% [95% CI: 0.4 to 1.8%] in 1990–2018). From 1995 to 2018, the overall cervical cancer mortality rate decreased by 1.0% (95% CI: −1.2% to −0.8%) per year. But for distant-stage squamous cell carcinoma, the mortality rate increased by 1.2% (95% CI: 0.3 to 2.1%) per year.ConclusionFor cervical cancer cases diagnosed in the United States from 1975 to 2018, the overall incidence and mortality rates decreased significantly. However, there was an increase in the incidence and mortality of advanced-stage squamous cell carcinoma. These epidemiological patterns offer critical insights for refining cervical cancer screening protocols and developing targeted interventions for advanced-stage cases.
Mohammed Tahri Sqalli, Begali Aslonov, M. Gafurov
et al.
Eye tracking technology has emerged as a valuable tool in the field of medicine, offering a wide range of applications across various disciplines. This perspective article aims to provide a comprehensive overview of the diverse applications of eye tracking technology in medical practice. By summarizing the latest research findings, this article explores the potential of eye tracking technology in enhancing diagnostic accuracy, assessing and improving medical performance, as well as improving rehabilitation outcomes. Additionally, it highlights the role of eye tracking in neurology, cardiology, pathology, surgery, as well as rehabilitation, offering objective measures for various medical conditions. Furthermore, the article discusses the utility of eye tracking in autism spectrum disorders, attention-deficit/hyperactivity disorder (ADHD), and human-computer interaction in medical simulations and training. Ultimately, this perspective article underscores the transformative impact of eye tracking technology on medical practice and suggests future directions for its continued development and integration.