Hasil untuk "Computer applications to medicine. Medical informatics"

Marium Malik, M. Tariq, Maira Kamran et al.

A. Vellido

Andy Wai Kan Yeung, Anela Tosevska, E. Klager et al.

Background Virtual reality (VR) and augmented reality (AR) have recently become popular research themes. However, there are no published bibliometric reports that have analyzed the corresponding scientific literature in relation to the application of these technologies in medicine. Objective We used a bibliometric approach to identify and analyze the scientific literature on VR and AR research in medicine, revealing the popular research topics, key authors, scientific institutions, countries, and journals. We further aimed to capture and describe the themes and medical conditions most commonly investigated by VR and AR research. Methods The Web of Science electronic database was searched to identify relevant papers on VR research in medicine. Basic publication and citation data were acquired using the “Analyze” and “Create Citation Report” functions of the database. Complete bibliographic data were exported to VOSviewer and Bibliometrix, dedicated bibliometric software packages, for further analyses. Visualization maps were generated to illustrate the recurring keywords and words mentioned in the titles and abstracts. Results The analysis was based on data from 8399 papers. Major research themes were diagnostic and surgical procedures, as well as rehabilitation. Commonly studied medical conditions were pain, stroke, anxiety, depression, fear, cancer, and neurodegenerative disorders. Overall, contributions to the literature were globally distributed with heaviest contributions from the United States and United Kingdom. Studies from more clinically related research areas such as surgery, psychology, neurosciences, and rehabilitation had higher average numbers of citations than studies from computer sciences and engineering. Conclusions The conducted bibliometric analysis unequivocally reveals the versatile emerging applications of VR and AR in medicine. With the further maturation of the technology and improved accessibility in countries where VR and AR research is strong, we expect it to have a marked impact on clinical practice and in the life of patients.

Essam H. Houssein, Amr M. Gamal, Eman M. G. Younis et al.

Jamieson D Gray, Lukasz S Wylezinski, Charles F Spurlock

Objectives To develop and evaluate a machine learning (ML) model that predicts Crohn’s disease (CD) patients responsible for the top quartile of healthcare spending.Methods De-identified commercial claims (2016–2018) from ~267 000 continuously enrolled members in a Midwestern state were analysed, including 994 CD cases. Monthly data for each patient was aggregated into data points that included healthcare spending amounts, encounter interactions, demographics and binary flags for diagnoses, procedures and drug codes. Seven algorithm families were tuned using five-fold cross-validation (January 2016 to September 2017) and tested prospectively (November 2017 to February 2018). Monthly performance evaluations assessed the accuracy of predicting high-cost healthcare spending, using 4-month and 1-month historical cost analyses for comparison.Results ML models predicted an average of 80% of the dollars spent by top-quartile members during the 4-month evaluation period, compared with 67% for the 4-month baseline and 62% for the prior-month benchmark. The models identified an average of 51 new members entering the high-cost group each month, nearly double the yield of the 4-month historical method. These ML models more accurately anticipated inpatient encounters that drove excess spending.Discussion Claims-based ML offers actionable lead time for payers and clinicians to enhance monitoring, adjust biological therapy or schedule elective care before emergency admissions occur. Because this framework relies exclusively on standard claim fields, it can be quickly extended to other episodic, high-variance conditions.Conclusion Prospectively tested, claims-only ML models enhance short-term risk stratification in CD by identifying future high-cost patients. Future studies should confirm the clinical impact, cost savings and ensure equitable performance across diverse populations.

James C. L. Chow

Quantum computing (QC) represents a paradigm shift in computational power, offering unique capabilities for addressing complex problems that are infeasible for classical computers. This review paper provides a detailed account of the current state of QC, with a particular focus on its applications within medicine. It explores fundamental concepts such as qubits, superposition, and entanglement, as well as the evolution of QC from theoretical foundations to practical advancements. The paper covers significant milestones where QC has intersected with medical research, including breakthroughs in drug discovery, molecular modeling, genomics, and medical diagnostics. Additionally, key quantum techniques such as quantum algorithms, quantum machine learning (QML), and quantum-enhanced imaging are explained, highlighting their relevance in healthcare. The paper also addresses challenges in the field, including hardware limitations, scalability, and integration within clinical environments. Looking forward, the paper discusses the potential for quantum–classical hybrid systems and emerging innovations in quantum hardware, suggesting how these advancements may accelerate the adoption of QC in medical research and clinical practice. By synthesizing reliable knowledge and presenting it through a comprehensive lens, this paper serves as a valuable reference for researchers interested in the transformative potential of QC in medicine.

[1] . Tiwari M, Waoo AA. Transforming Healthcare: The Synergistic Fusion of AI and IoT for Intelligent, Personalized Well-Being. InRevolutionizing Healthcare: AI Integration with IoT for Enhanced Patient Outcomes 2024 Sep 24 (pp. 109-149). Cham: Springer Nature Switzerland. [2] . Sherani AM, Khan M, Qayyum MU, Hussain HK. Synergizing AI and Healthcare: Pioneering Advances in Cancer Medicine for Personalized Treatment. International Journal of Multidisciplinary Sciences and Arts. 2024 Feb 4;3(1):270-7. [3] . Chen JJ, Husnain A, Cheng WW. Exploring the Trade-Off Between Performance and Cost in Facial Recognition: Deep Learning Versus Traditional Computer Vision. InProceedings of SAI Intelligent Systems Conference 2023 Sep 7 (pp. 400-412). Cham: Springer Nature Switzerland. [4] . Khan M, Shiwlani A, Qayyum MU, Sherani AM, Hussain HK. Revolutionizing Healthcare with AI: Innovative Strategies in Cancer Medicine. International Journal of Multidisciplinary Sciences and Arts. 2024 May 26;3(1):316-24. [5] . Saeed A, Husnain A, Zahoor A, Gondal RM. A comparative study of cat swarm algorithm for graph coloring problem: Convergence analysis and performance evaluation. International Journal of Innovative Research in Computer Science and Technology (IJIRCST). 2024;12(4):1-9. [6] . Husnain, A., Alomari, G., & Saeed, A. (2024). AI-driven integrated hardware and software solution for EEG-based detection of depression and anxiety. International Journal for Multidisciplinary Research (IJFMR), 6(3), 1-24. https://doi.org/10.30574/ijfmr.2024.v06i03.22645 [7] . Husnain A, Saeed A. AI-enhanced depression detection and therapy: Analyzing the VPSYC system. IRE Journals, 8 (2), 162-168 [Internet]. 2024 [8] . Sherani AM, Qayyum MU, Khan M, Shiwlani A, Hussain HK. Transforming Healthcare: The Dual Impact of Artificial Intelligence on Vaccines and Patient Care. BULLET: Jurnal Multidisiplin Ilmu. 2024 May 27;3(2):270-80. [9] . Samad A, Jamal A. Transformative Applications of ChatGPT: A Comprehensive Review of Its Impact across Industries. Global Journal of Multidisciplinary Sciences and Arts. 2024;1:26-48. [10] . Patel H, Samad A, Hamza M, Muazzam A, Harahap MK. Role of artificial intelligence in livestock and poultry farming. Sinkron: jurnal dan penelitian teknik informatika. 2022 Oct 7;6(4):2425-9. [11] . Shihab SR, Sultana N, Samad A. Revisiting the use of ChatGPT in business and educational fields: possibilities and challenges. BULLET: Jurnal Multidisiplin Ilmu. 2023 Jun 4;2(3):534-45. [12] . Madani M, Behzadi MM, Nabavi S. The role of deep learning in advancing breast cancer detection using different imaging modalities: a systematic review. Cancers. 2022 Oct 29;14(21):5334. [13] . Dar RA, Rasool M, Assad A. Breast cancer detection using deep learning: Datasets, methods, and challenges ahead. Computers in biology and medicine. 2022 Oct 1;149:106073. [14] . Krittanawong C, Johnson KW, Hershman SG, Tang WW. Big data, artificial intelligence, and cardiovascular precision medicine. Expert Review of Precision Medicine and Drug Development. 2018 Sep 3;3(5):305-17. [15] . Krittanawong C, Johnson KW, Hershman SG, Tang WW. Big data, artificial intelligence, and cardiovascular precision medicine. Expert Review of Precision Medicine and Drug Development. 2018 Sep 3;3(5):305-17. [16] . Pryor DB, Shaw L, McCants CB, Lee KL, Mark DB, Harrell FE, Muhlbaier LH, Califf RM. Value of the history and physical in identifying patients at increased risk for coronary artery disease. Annals of internal medicine. 1993 Jan 15;118(2):81-90. [17] . Pryor DB, Shaw L, McCants CB, Lee KL, Mark DB, Harrell FE, Muhlbaier LH, Califf RM. Value of the history and physical in identifying patients at increased risk for coronary artery disease. Annals of internal medicine. 1993 Jan 15;118(2):81-90. [18] . Sharma R, Goel T, Tanveer M, Suganthan PN, Razzak I, Murugan R. Conv-ervfl: Convolutional neural network based ensemble RVFL classifier for Alzheimer's disease diagnosis. IEEE Journal of Biomedical and Health Informatics. 2022 Oct 19;27(10):4995-5003. [19] . Ibrahim AM, Mohammed MA. A comprehensive review on advancements in artificial intelligence approaches and future perspectives for early diagnosis of Parkinson's disease. International Journal of Mathematics, Statistics, and Computer Science. 2024 Jan 26;2:173-82. [20] . Vercellini P, Somigliana E, Viganò P, Abbiati A, Barbara G, Fedele L. Chronic pelvic pain in women: etiology, pathogenesis and diagnostic approach. Gynecological Endocrinology. 2009 Jan 1;25(3):149-58. [21] . Fan W, Liu J, Zhu S, Pardalos PM. Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS). Annals of Operations Research. 2020 Nov;294(1):567-92. [22] . Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nature medicine. 2019 Jan;25(1):44-56. [23] . Haleem A, Javaid M, Singh RP, Suman R. Medical 4.0 technologies for healthcare: Features, capabilities, and applications. Internet of Things and Cyber-Physical Systems. 2022 Jan 1;2:12-30. [24] . Lee D, Yoon SN. Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges. International journal of environmental research and public health. 2021 Jan;18(1):271. [25] . Turabian JL. The Care and Cure Process in General Medicine. Public Health and Epidemiological Implications. Medp Public Health Epidemiol. 2022;2(1). [26] . Yuan B, Li J. The policy effect of the General Data Protection Regulation (GDPR) on the digital public health sector in the European Union: an empirical investigation. International journal of environmental research and public health. 2019 Mar;16(6):1070. [27] . Schwartz R, Schwartz R, Vassilev A, Greene K, Perine L, Burt A, Hall P. Towards a standard for identifying and managing bias in artificial intelligence. US Department of Commerce, National Institute of Standards and Technology; 2022 Mar 15. [28] . Khan AH, Zainab H, Khan R, Hussain HK. Deep Learning in the Diagnosis and Management of Arrhythmias. Journal of Social Research. 2024 Dec 6;4(1):50-66. [29] . Zainab H, Khan R, Khan AH, Hussain HK. REINFORCEMENT LEARNING IN CARDIOVASCULAR THERAPY PROTOCOL: A NEW PERSPECTIVE. [30] . Al-Jaroodi J, Mohamed N, Abukhousa E. Health 4.0: on the way to realizing the healthcare of the future. Ieee Access. 2020 Nov 18;8:211189-210. [31] . Feijóo C, Kwon Y, Bauer JM, Bohlin E, Howell B, Jain R, Potgieter P, Vu K, Whalley J, Xia J. Harnessing artificial intelligence (AI) to increase wellbeing for all: The case for a new technology diplomacy. Telecommunications Policy. 2020 Jul 1;44(6):101988. [32] . Shen J, Zhang CJ, Jiang B, Chen J, Song J, Liu Z, He Z, Wong SY, Fang PH, Ming WK. Artificial intelligence versus clinicians in disease diagnosis: systematic review. JMIR medical informatics. 2019 Aug 16;7(3):e10010. [33] . Reddy MS, Sarisa M, Konkimalla S, Bauskar SR, Gollangi HK, Galla EP, Rajaram SK. Predicting tomorrow’s Ailments: How AI/ML Is Transforming Disease Forecasting. ESP Journal of Engineering & Technology Advancements. 2021;1(2):188-200. [34] . Bajwa J, Munir U, Nori A, Williams B. Artificial intelligence in healthcare: transforming the practice of medicine. Future healthcare journal. 2021 Jul 1;8(2):e188-94. [35] . Feldman RC, Aldana E, Stein K. Artificial intelligence in the health care space: how we can trust what we cannot know. Stan. L. & Pol'y Rev.. 2019;30:399. [36] . Gill AY, Saeed A, Rasool S, Husnain A, Hussain HK. Revolutionizing Healthcare: How Machine Learning is Transforming Patient Diagnoses-a Comprehensive Review of AI's Impact on Medical Diagnosis. Journal of World Science. 2023 Oct 27;2(10):1638-52.

Janey Sewell, Carole Kelly, Adamma Aghaizu et al.

BackgroundDue to advances in treatment, HIV is now a chronic condition with near-normal life expectancy. However, people with HIV continue to have a higher burden of mental and physical health conditions and are impacted by wider socioeconomic issues. Positive Voices is a nationally representative series of surveys of people with HIV in the United Kingdom. It monitors the physical, mental, and social health, well-being, and needs of this population so that they can be addressed. ObjectiveThis paper aimed to describe the methodology, recruitment strategies, and key sociodemographic features of participants recruited for the second national round of Positive Voices (PV2022). MethodsPV2022 was a national, cross-sectional questionnaire study that included people attending HIV care at 101 of the 178 clinics in the United Kingdom between April 2022 and March 2023. Data from the HIV and AIDS reporting system (HARS), a national surveillance database of people with HIV and attending care that is held at the UK Health Security Agency (UKHSA), was used as a sampling frame. The information collected in PV2022 included demographic and socioeconomic factors, HIV diagnoses and treatment, mental and physical health, health service use and satisfaction, social care and support, met and unmet needs, stigma and discrimination, quality of life, lifestyle factors, and additional challenges experienced due to the COVID-19 pandemic. Data linkage to HARS enabled the extraction of clinical information on antiretroviral therapy (ART), HIV viral load, and CD4 lymphocyte counts. Probabilistic sampling was used to provide a randomly selected, representative sample of people attending HIV care who could be invited to complete a paper or online questionnaire 'on the web' to online. At the start of 2023, the study was underrecruiting, mainly due to the mpox outbreak, and a separate sequential recruitment strategy was initiated in 14 of the largest and most demographically diverse clinics to increase participant numbers. ResultsOf the 4622 participants who completed the questionnaire, 3692 were recruited through probabilistic recruitment and 930 through sequential recruitment. The overall response rate (measured as the number of people who completed a questionnaire of those who either accepted or declined) was 50%. Survey respondents represented approximately 1 in 20 people diagnosed with HIV in England, Wales, and Scotland. The median age of participants was 52 years, 3428 of participants were male, 2991 were of White ethnicity, and 1121 were of Black ethnicity. ConclusionsPV2022 is currently the largest survey of people with HIV in the United Kingdom (as of September 2024). The PV2022 findings will be used to explore the health and well-being of the HIV population and examine associations with demographic, socioeconomic, lifestyle, and other HIV-related factors. International Registered Report Identifier (IRRID)RR1-10.2196/58531

Nima Fathi, Amar Kumar, Tal Arbel

Recent advancements in Large Language Models (LLMs) have catalyzed a paradigm shift from static prediction systems to agentic AI agents capable of reasoning, interacting with tools, and adapting to complex tasks. While LLM-based agentic systems have shown promise across many domains, their application to medical imaging remains in its infancy. In this work, we introduce AURA, the first visual linguistic explainability agent designed specifically for comprehensive analysis, explanation, and evaluation of medical images. By enabling dynamic interactions, contextual explanations, and hypothesis testing, AURA represents a significant advancement toward more transparent, adaptable, and clinically aligned AI systems. We highlight the promise of agentic AI in transforming medical image analysis from static predictions to interactive decision support. Leveraging Qwen-32B, an LLM-based architecture, AURA integrates a modular toolbox comprising: (i) a segmentation suite with phase grounding, pathology segmentation, and anatomy segmentation to localize clinically meaningful regions; (ii) a counterfactual image-generation module that supports reasoning through image-level explanations; and (iii) a set of evaluation tools including pixel-wise difference-map analysis, classification, and advanced state-of-the-art components to assess diagnostic relevance and visual interpretability.

Solha Kang, Joris Vankerschaver, Utku Ozbulak

With the advancements in self-supervised learning (SSL), transformer-based computer vision models have recently demonstrated superior results compared to convolutional neural networks (CNNs) and are poised to dominate the field of artificial intelligence (AI)-based medical imaging in the upcoming years. Nevertheless, similar to CNNs, unveiling the decision-making process of transformer-based models remains a challenge. In this work, we take a step towards demystifying the decision-making process of transformer-based medical imaging models and propose Token Insight, a novel method that identifies the critical tokens that contribute to the prediction made by the model. Our method relies on the principled approach of token discarding native to transformer-based models, requires no additional module, and can be applied to any transformer model. Using the proposed approach, we quantify the importance of each token based on its contribution to the prediction and enable a more nuanced understanding of the model's decisions. Our experimental results which are showcased on the problem of colonic polyp identification using both supervised and self-supervised pretrained vision transformers indicate that Token Insight contributes to a more transparent and interpretable transformer-based medical imaging model, fostering trust and facilitating broader adoption in clinical settings.

Wentao Chen, Tianming Xu, Weimin Zhou

Image denoising algorithms have been extensively investigated for medical imaging. To perform image denoising, penalized least-squares (PLS) problems can be designed and solved, in which the penalty term encodes prior knowledge of the object being imaged. Sparsity-promoting penalties, such as total variation (TV), have been a popular choice for regularizing image denoising problems. However, such hand-crafted penalties may not be able to preserve task-relevant information in measured image data and can lead to oversmoothed image appearances and patchy artifacts that degrade signal detectability. Supervised learning methods that employ convolutional neural networks (CNNs) have emerged as a popular approach to denoising medical images. However, studies have shown that CNNs trained with loss functions based on traditional image quality measures can lead to a loss of task-relevant information in images. Some previous works have investigated task-based loss functions that employ model observers for training the CNN denoising models. However, such training processes typically require a large number of noisy and ground-truth (noise-free or low-noise) image data pairs. In this work, we propose a task-based regularization strategy for use with PLS in medical image denoising. The proposed task-based regularization is associated with the likelihood of linear test statistics of noisy images for Gaussian noise models. The proposed method does not require ground-truth image data and solves an individual optimization problem for denoising each image. Computer-simulation studies are conducted that consider a multivariate-normally distributed (MVN) lumpy background and a binary texture background. It is demonstrated that the proposed regularization strategy can effectively improve signal detectability in denoised images.

K. V. Valkova

The subject of this study is the medico-sanitary discourse of the non-partisan socio-political newspaper Zhizn’ Altaya (Life of Altai, Barnaul) and its role in shaping the urban culture of health. The newspaper was not specialized in medicine, which makes it possible to examine how medical and sanitary topics were represented within the general informational space of a provincial publication. The chronological framework of 1911–1913 covers the formative stage of the newspaper’s section structure and thematic priorities. The aim of the study is to identify the patterns of formation and development of the medico-sanitary discourse in Zhizn’ Altaya through computer-aided content analysis, to determine the quantitative and semantic parameters of the publications, to reconstruct the forms of interaction between authorities, the professional community, and the population, and to assess the role of the provincial press as an instrument of sanitary education and urban modernization. The article combines computer-aided content and discourse analysis in MAXQDA 24 with traditional source-critical methods. The corpus includes 861 issues of Zhizn’ Altaya (1911–1913), manually coded according to a three-level scheme comprising five main categories and 99 subcodes, which made it possible to reveal quantitative and semantic regularities. The results showed that Zhizn’ Altaya developed a stable system of medical publications combining educational and communicative functions. The newspaper reflected the interaction between official and civic aspects of sanitary control, the growth of medical advertising, the participation of female professionals, and the social vulnerabilities of the urban environment. The scientific novelty lies in the first systematic application of MAXQDA to pre-revolutionary Siberian press for the reconstruction of medico-sanitary discourse. The computer-aided content analysis of the corpus made it possible to identify a stable thematic core (sanitary reviews, medical chronicles, epidemics, advertisements, and social narratives), to establish the absence of rigid inter-block correlations amid high event-driven reactivity, and to visualize the interaction between the sections Sanitary Inspections and Unsanitary Conditions as indicators of administrative and public control. Despite the poor preservation and selectivity of the surviving issues, the use of computer-aided methods helped partially compensate for the limitations of the source and confirmed the research assumptions regarding the gradual formation of urban sanitary culture, the increasing role of the press in public health education, and the institutionalization of medico-sanitary discourse. The results demonstrate the potential of historical informatics for analyzing provincial newspapers and refine our understanding of the interaction between authorities, physicians, and urban residents in the field of public health at the beginning of the XX century.

Tiranun Rungvivatjarus, Mario Bialostozky, Amy Z. Chong et al.

Abstract Background  Clinical informatics (CI) has reshaped how medical information is shared, evaluated, and utilized in health care delivery. The widespread integration of electronic health records (EHRs) mandates proficiency among physicians and practitioners, yet medical trainees face a scarcity of opportunities for CI education. Objectives  We developed a CI rotation at a tertiary pediatric care center to teach categorical pediatric, pediatric–neurology, and medicine–pediatric residents foundational CI knowledge and applicable EHR skills. Methods  Created in 2017 and redesigned in 2020, a CI rotation aimed to provide foundational CI knowledge, promote longitudinal learning, and encourage real-world application of CI skills/tools. Led by a team of five physician informaticist faculty, the curriculum offers personalized rotation schedules and individual sessions with faculty for each trainee. Trainees were tasked with completing an informatics project, knowledge assessment, and self-efficacy perception survey before and after rotation. Paired t -test analyses were used to compare pre- and postcurriculum perception survey. Results  Thirty-one residents have completed the elective with their projects contributing to diverse areas such as medical education, division-specific initiatives, documentation improvement, regulatory compliance, and operating plan goals. The mean knowledge assessment percentage score increased from 77% (11.6) to 92% (10.6; p ≤ 0.05). Residents' perception surveys demonstrated improved understanding and confidence across various informatics concepts and tools (p ≤ 0.05). Conclusion  Medical trainees are increasingly interested in CI education and find it valuable. Our medical education curriculum was successful at increasing residents' understanding, self-efficacy, and confidence in utilizing CI concepts and EHR tools. Future data are needed to assess the impact such curricula have on graduates' proficiency and efficiency in the use of CI tools in the clinical workplace.

Qingquan Chen, Zeshun Chen, Xi Zhu et al.

Background: The sleep quality of medical staff was severely affected during COVID-19, but the factors influencing the sleep quality of frontline staff involved in medical assistance remained unclear, and screening tools for their sleep quality were lacking. Methods: From June 25 to July 14, 2022, we conducted an Internet-based cross-sectional survey. The Pittsburgh Sleep Quality Index (PSQI), a self-designed general information questionnaire, and a questionnaire regarding the factors influencing sleep quality were combined to understand the sleep quality of frontline medical staff in Fujian Province supporting Shanghai in the past month. A chi-square test was used to compare participant characteristics, and multivariate unconditional logistic regression analysis was used to determine the predictors of sleep quality. Stratified sampling was used to divide the data into a training test set ( n  = 1061, 80%) and an independent validation set ( n  = 265, 20%). Six models were developed and validated using logistic regression, artificial neural network, gradient augmented tree, random forest, naive Bayes, and model decision tree. Results: A total of 1326 frontline medical staff were included in this survey, with a mean PSQI score of 11.354 ± 4.051. The prevalence of poor sleep quality was 80.8% ( n  = 1072, PSQI >7). Six variables related to sleep quality were used as parameters in the prediction model, including type of work, professional job title, work shift, weight change, tea consumption during assistance, and basic diseases. The artificial neural network (ANN) model produced the best overall performance with area under the curve, accuracy, sensitivity, specificity, precision, F1 score, and kappa of 71.6%, 68.7%, 66.7%, 69.2%, 34.0%, 45.0%, and 26.2% respectively. Conclusions: In this study, the ANN model, which demonstrated excellent predictive efficiency, showed potential for application in monitoring the sleep quality of medical staff and provide some scientific guidance suggestions for early intervention.

Yihao Liu, Jiaming Zhang, Andres Diaz-Pinto et al.

The Segment Anything Model (SAM) has drawn significant attention from researchers who work on medical image segmentation because of its generalizability. However, researchers have found that SAM may have limited performance on medical images compared to state-of-the-art non-foundation models. Regardless, the community sees potential in extending, fine-tuning, modifying, and evaluating SAM for analysis of medical imaging. An increasing number of works have been published focusing on the mentioned four directions, where variants of SAM are proposed. To this end, a unified platform helps push the boundary of the foundation model for medical images, facilitating the use, modification, and validation of SAM and its variants in medical image segmentation. In this work, we introduce SAMM Extended (SAMME), a platform that integrates new SAM variant models, adopts faster communication protocols, accommodates new interactive modes, and allows for fine-tuning of subcomponents of the models. These features can expand the potential of foundation models like SAM, and the results can be translated to applications such as image-guided therapy, mixed reality interaction, robotic navigation, and data augmentation.

William Cagas, Chan Ko, Blake Hsiao et al.

The proliferation of machine learning models in diverse clinical applications has led to a growing need for high-fidelity, medical image training data. Such data is often scarce due to cost constraints and privacy concerns. Alleviating this burden, medical image synthesis via generative adversarial networks (GANs) emerged as a powerful method for synthetically generating photo-realistic images based on existing sets of real medical images. However, the exact image set size required to efficiently train such a GAN is unclear. In this work, we experimentally establish benchmarks that measure the relationship between a sample dataset size and the fidelity of the generated images, given the dataset's distribution of image complexities. We analyze statistical metrics based on delentropy, an image complexity measure rooted in Shannon's entropy in information theory. For our pipeline, we conduct experiments with two state-of-the-art GANs, StyleGAN 3 and SPADE-GAN, trained on multiple medical imaging datasets with variable sample sizes. Across both GANs, general performance improved with increasing training set size but suffered with increasing complexity.

Aleksander Ogonowski, Michał Żebrowski, Arkadiusz Ćwiek et al.

Most of the intrusion detection methods in computer networks are based on traffic flow characteristics. However, this approach may not fully exploit the potential of deep learning algorithms to directly extract features and patterns from raw packets. Moreover, it impedes real-time monitoring due to the necessity of waiting for the processing pipeline to complete and introduces dependencies on additional software components. In this paper, we investigate deep learning methodologies capable of detecting attacks in real-time directly from raw packet data within network traffic. We propose a novel approach where packets are stacked into windows and separately recognised, with a 2D image representation suitable for processing with computer vision models. Our investigation utilizes the CIC IDS-2017 dataset, which includes both benign traffic and prevalent real-world attacks, providing a comprehensive foundation for our research.

Christopher J Brady, R Chase Cockrell, Lindsay R Aldrich et al.

BackgroundAs trachoma is eliminated, skilled field graders become less adept at correctly identifying active disease (trachomatous inflammation—follicular [TF]). Deciding if trachoma has been eliminated from a district or if treatment strategies need to be continued or reinstated is of critical public health importance. Telemedicine solutions require both connectivity, which can be poor in the resource-limited regions of the world in which trachoma occurs, and accurate grading of the images. ObjectiveOur purpose was to develop and validate a cloud-based “virtual reading center” (VRC) model using crowdsourcing for image interpretation. MethodsThe Amazon Mechanical Turk (AMT) platform was used to recruit lay graders to interpret 2299 gradable images from a prior field trial of a smartphone-based camera system. Each image received 7 grades for US $0.05 per grade in this VRC. The resultant data set was divided into training and test sets to internally validate the VRC. In the training set, crowdsourcing scores were summed, and the optimal raw score cutoff was chosen to optimize kappa agreement and the resulting prevalence of TF. The best method was then applied to the test set, and the sensitivity, specificity, kappa, and TF prevalence were calculated. ResultsIn this trial, over 16,000 grades were rendered in just over 60 minutes for US $1098 including AMT fees. After choosing an AMT raw score cut point to optimize kappa near the World Health Organization (WHO)–endorsed level of 0.7 (with a simulated 40% prevalence TF), crowdsourcing was 95% sensitive and 87% specific for TF in the training set with a kappa of 0.797. All 196 crowdsourced-positive images received a skilled overread to mimic a tiered reading center and specificity improved to 99%, while sensitivity remained above 78%. Kappa for the entire sample improved from 0.162 to 0.685 with overreads, and the skilled grader burden was reduced by over 80%. This tiered VRC model was then applied to the test set and produced a sensitivity of 99% and a specificity of 76% with a kappa of 0.775 in the entire set. The prevalence estimated by the VRC was 2.70% (95% CI 1.84%-3.80%) compared to the ground truth prevalence of 2.87% (95% CI 1.98%-4.01%). ConclusionsA VRC model using crowdsourcing as a first pass with skilled grading of positive images was able to identify TF rapidly and accurately in a low prevalence setting. The findings from this study support further validation of a VRC and crowdsourcing for image grading and estimation of trachoma prevalence from field-acquired images, although further prospective field testing is required to determine if diagnostic characteristics are acceptable in real-world surveys with a low prevalence of the disease.

Shangqi Gao, Hangqi Zhou, Yibo Gao et al.

Due to the cross-domain distribution shift aroused from diverse medical imaging systems, many deep learning segmentation methods fail to perform well on unseen data, which limits their real-world applicability. Recent works have shown the benefits of extracting domain-invariant representations on domain generalization. However, the interpretability of domain-invariant features remains a great challenge. To address this problem, we propose an interpretable Bayesian framework (BayeSeg) through Bayesian modeling of image and label statistics to enhance model generalizability for medical image segmentation. Specifically, we first decompose an image into a spatial-correlated variable and a spatial-variant variable, assigning hierarchical Bayesian priors to explicitly force them to model the domain-stable shape and domain-specific appearance information respectively. Then, we model the segmentation as a locally smooth variable only related to the shape. Finally, we develop a variational Bayesian framework to infer the posterior distributions of these explainable variables. The framework is implemented with neural networks, and thus is referred to as deep Bayesian segmentation. Quantitative and qualitative experimental results on prostate segmentation and cardiac segmentation tasks have shown the effectiveness of our proposed method. Moreover, we investigated the interpretability of BayeSeg by explaining the posteriors and analyzed certain factors that affect the generalization ability through further ablation studies. Our code will be released via https://zmiclab.github.io/projects.html, once the manuscript is accepted for publication.

Andy Wai Kan Yeung, Anela Tosevska, Elisabeth Klager et al.

BackgroundSocial media has been extensively used for the communication of health-related information and consecutively for the potential spread of medical misinformation. Conventional systematic reviews have been published on this topic to identify original articles and to summarize their methodological approaches and themes. A bibliometric study could complement their findings, for instance, by evaluating the geographical distribution of the publications and determining if they were well cited and disseminated in high-impact journals. ObjectiveThe aim of this study was to perform a bibliometric analysis of the current literature to discover the prevalent trends and topics related to medical misinformation on social media. MethodsThe Web of Science Core Collection electronic database was accessed to identify relevant papers with the following search string: ALL=(misinformati* OR “wrong informati*” OR disinformati* OR “misleading informati*” OR “fake news*”) AND ALL=(medic* OR illness* OR disease* OR health* OR pharma* OR drug* OR therap*) AND ALL=(“social media*” OR Facebook* OR Twitter* OR Instagram* OR YouTube* OR Weibo* OR Whatsapp* OR Reddit* OR TikTok* OR WeChat*). Full records were exported to a bibliometric software, VOSviewer, to link bibliographic information with citation data. Term and keyword maps were created to illustrate recurring terms and keywords. ResultsBased on an analysis of 529 papers on medical and health-related misinformation on social media, we found that the most popularly investigated social media platforms were Twitter (n=90), YouTube (n=67), and Facebook (n=57). Articles targeting these 3 platforms had higher citations per paper (>13.7) than articles covering other social media platforms (Instagram, Weibo, WhatsApp, Reddit, and WeChat; citations per paper <8.7). Moreover, social media platform–specific papers accounted for 44.1% (233/529) of all identified publications. Investigations on these platforms had different foci. Twitter-based research explored cyberchondria and hypochondriasis, YouTube-based research explored tobacco smoking, and Facebook-based research studied vaccine hesitancy related to autism. COVID-19 was a common topic investigated across all platforms. Overall, the United States contributed to half of all identified papers, and 80% of the top 10 most productive institutions were based in this country. The identified papers were mostly published in journals of the categories public environmental and occupational health, communication, health care sciences services, medical informatics, and medicine general internal, with the top journal being the Journal of Medical Internet Research. ConclusionsThere is a significant platform-specific topic preference for social media investigations on medical misinformation. With a large population of internet users from China, it may be reasonably expected that Weibo, WeChat, and TikTok (and its Chinese version Douyin) would be more investigated in future studies. Currently, these platforms present research gaps that leave their usage and information dissemination warranting further evaluation. Future studies should also include social platforms targeting non-English users to provide a wider global perspective.