Hasil untuk "Medical legislation"

Salil Patel

Medical data range from genomic sequences and retinal photographs to structured laboratory results and unstructured clinical narratives. Although these modalities appear disparate, many encode convergent information about a single underlying physiological state. The Latent Space Hypothesis frames each observation as a projection of a unified, hierarchically organized manifold -- much like shadows cast by the same three-dimensional object. Within this learned geometric representation, an individual's health status occupies a point, disease progression traces a trajectory, and therapeutic intervention corresponds to a directed vector. Interpreting heterogeneous evidence in a shared space provides a principled way to re-examine eponymous conditions -- such as Parkinson's or Crohn's -- that often mask multiple pathophysiological entities and involve broader anatomical domains than once believed. By revealing sub-trajectories and patient-specific directions of change, the framework supplies a quantitative rationale for personalised diagnosis, longitudinal monitoring, and tailored treatment, moving clinical practice away from grouping by potentially misleading labels toward navigation of each person's unique trajectory. Challenges remain -- bias amplification, data scarcity for rare disorders, privacy, and the correlation-causation divide -- but scale-aware encoders, continual learning on longitudinal data streams, and perturbation-based validation offer plausible paths forward.

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.

Canan Uluoğlu, Ali Kağan Coşkun, Utku Aykan

Objective: The process of conducting medical research on human subjects is secured by a number of regulations from its planning phase through its publication as an original article. Ethics Committees (ECs) play an important role in the follow-up and evaluation of the study. In this study, we present an evaluation of the experiences of the “Clinical Research Ethics Committee” of a university. Methods: The characteristics of EC applications, reasons for rejection (technical, scientific, ethical), and files that did not reapply to the committee after the revision request were analyzed. Results: One thousand and fifty-seven (73.96%) of the 1429 files were accepted with minor corrections at the first examination and 15 files were rejected (1.04%). Of the applications, 357 (24.98%) were returned to the EC agenda with major correction requests. Scientific reasons were reported in 19 (90.5%) of the rejected files, ethical reasons were reported in 12 (57%) and technical reasons were reported in 8 (38.1). The rejection rate increased to 8.24% when the same files were reevaluated and did not reapply to the EC after the revision request (6.8%). Conclusion: ECs seek revisions for a significant portion of submissions and offer researchers scientific and ethical advice. The majority of the applications were approved by this consultation. In our study, most of the files that did not receive approval from the EC were actually those that did not return after the revision request. The difficulties associated with the legislation appear to be an important reason for researchers to withdraw their application files.

Alok Atreya, Sabbu Maharjan, Samata Nepal et al.

Possession state is a disorder of consciousness with substitution of the personality, which is claimed to be a spirit, a deity, a dead person or some other power. In rural Nepal these experiences are normalised and Hindu communities often attribute psychological conditions to a supernatural cause. We report the case of a 30-year-old woman who presented with acute-onset symptoms characterised by episodes of altered consciousness, vocalisations suggestive of religious chants, and deity-associated behaviour, probably influenced by local suggestions of divine possession. Additional notable features included similar presentations among a family member and seeking help from traditional healers prior to psychiatric consultation. Medical examinations and investigations were normal. Specific cultural and religious manifestations posed challenges to clinical interpretation. The patient responded well to combined pharmacotherapy and supportive psychotherapy during her brief hospital stay, with cessation of possession episodes. This case report highlights the importance of cultural competence in Nepalese forensic psychiatric evaluations, particularly in the context of possession states, while examining the application of mental health legislation in traditional cultural settings.

Liu Li, Hanchun Wang, Matthew Baugh et al.

Although existing medical image segmentation methods provide impressive pixel-wise accuracy, they often neglect topological correctness, making their segmentations unusable for many downstream tasks. One option is to retrain such models whilst including a topology-driven loss component. However, this is computationally expensive and often impractical. A better solution would be to have a versatile plug-and-play topology refinement method that is compatible with any domain-specific segmentation pipeline. Directly training a post-processing model to mitigate topological errors often fails as such models tend to be biased towards the topological errors of a target segmentation network. The diversity of these errors is confined to the information provided by a labelled training set, which is especially problematic for small datasets. Our method solves this problem by training a model-agnostic topology refinement network with synthetic segmentations that cover a wide variety of topological errors. Inspired by the Stone-Weierstrass theorem, we synthesize topology-perturbation masks with randomly sampled coefficients of orthogonal polynomial bases, which ensures a complete and unbiased representation. Practically, we verified the efficiency and effectiveness of our methods as being compatible with multiple families of polynomial bases, and show evidence that our universal plug-and-play topology refinement network outperforms both existing topology-driven learning-based and post-processing methods. We also show that combining our method with learning-based models provides an effortless add-on, which can further improve the performance of existing approaches.

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.

Atul Shendge, Abhishek Royal, Alpana Dange et al.

The article presents the LGBTQIA + inclusivity analysis of the Medical Termination of Pregnancy (Amendment) Act 2021, which aims to provide safe abortion services across India. The analysis reports the use of cisgender-heteronormative language, instead of gender-neutral terms and pronouns in the legislation. Sexual assault/rape as an eligibility criterion for permitting abortion under this act does not apply to non-binary, trans- and queer individuals due to non-inclusivity in Rape Laws (Indian Penal Code 375 and 376). The analysis highlights the necessity for critical amendments to expand its scope to ensure LGBTQIA+ inclusive abortion services.

Oksana Kovtun, Elvira Tilek kyzy, Nadira Masiumova

Abstract Introduction Removing legal barriers to HIV services is crucial for the global 2030 goal of ending the HIV and AIDS epidemic, particularly in eastern Europe, the Caucasus and central Asia. Despite state commitments to uphold human rights, gay, bisexual and other men who have sex with men (gbMSM), along with transgender people (TP) still face stigma and discrimination. This article presents an analysis of rights violations based on sexual orientation and gender identity (SOGI) and HIV reported in 2022 across six countries, highlighting features and their links to legislation and law enforcement practices. Methods We examined documented cases of rights violations among gbMSM and TP in Armenia, Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan and Ukraine in 2022 using the REAct system, a tool for documenting and responding to rights violations against key populations. Initially, we employed directed content analysis based on Yogyakarta Principles to analyse narratives of violations. A codebook was developed through contextual, manifest and latent coding, with themes, categories and codes converted into quantitative variables for statistical analysis. Descriptive statistics were used to identify the characteristics of violations. Results A total of 456 cases of rights violations related to SOGI and HIV were documented, ranging from 22 cases in Tajikistan to 217 in Ukraine. Most violations concerned gbMSM (76.5%), with one‐fifth involving TP, predominantly transgender women. Complex violations with multiple perpetrators or infringements were documented in Armenia and central Asia. Privacy rights were commonly violated, often through outing. Cases of violations of the right to the highest attainable standard of health (13.6%) and protection from medical abuses (2.6%) were also documented. Other rights violations were sporadic, with each country exhibiting distinct patterns of violated rights and types of violations. In Ukraine, the full‐scale war in 2022 influenced the nature of documented cases, reflecting the challenges faced by gbMSM and TP. Conclusions Monitoring rights violations proved effective for assessing the situation of gbMSM and TP, particularly in the insufficiently studied and diverse eastern Europe, Caucasus and central Asia regions. As rights violations are linked to both legislation and law enforcement practices, comprehensive interventions to minimize structural and interpersonal stigma are essential.

Harry Anthony, Konstantinos Kamnitsas

Implementing neural networks for clinical use in medical applications necessitates the ability for the network to detect when input data differs significantly from the training data, with the aim of preventing unreliable predictions. The community has developed several methods for out-of-distribution (OOD) detection, within which distance-based approaches - such as Mahalanobis distance - have shown potential. This paper challenges the prevailing community understanding that there is an optimal layer, or combination of layers, of a neural network for applying Mahalanobis distance for detection of any OOD pattern. Using synthetic artefacts to emulate OOD patterns, this paper shows the optimum layer to apply Mahalanobis distance changes with the type of OOD pattern, showing there is no one-fits-all solution. This paper also shows that separating this OOD detector into multiple detectors at different depths of the network can enhance the robustness for detecting different OOD patterns. These insights were validated on real-world OOD tasks, training models on CheXpert chest X-rays with no support devices, then using scans with unseen pacemakers (we manually labelled 50% of CheXpert for this research) and unseen sex as OOD cases. The results inform best-practices for the use of Mahalanobis distance for OOD detection. The manually annotated pacemaker labels and the project's code are available at: https://github.com/HarryAnthony/Mahalanobis-OOD-detection.

Yizhe Zhang, Shuo Wang, Yejia Zhang et al.

Conformal prediction (CP) generates a set of predictions for a given test sample such that the prediction set almost always contains the true label (e.g., 99.5\% of the time). CP provides comprehensive predictions on possible labels of a given test sample, and the size of the set indicates how certain the predictions are (e.g., a set larger than one is `uncertain'). Such distinct properties of CP enable effective collaborations between human experts and medical AI models, allowing efficient intervention and quality check in clinical decision-making. In this paper, we propose a new method called Reliable-Region-Based Conformal Prediction (RR-CP), which aims to impose a stronger statistical guarantee so that the user-specified error rate (e.g., 0.5\%) can be achieved in the test time, and under this constraint, the size of the prediction set is optimized (to be small). We consider a small prediction set size an important measure only when the user-specified error rate is achieved. Experiments on five public datasets show that our RR-CP performs well: with a reasonably small-sized prediction set, it achieves the user-specified error rate (e.g., 0.5\%) significantly more frequently than exiting CP methods.

Kristof Takacs, Alex Mason, Luis Eduardo Cordova-Lopez et al.

Ensuring the safety of the equipment, its environment and most importantly, the operator during robot operations is of paramount importance. Robots and complex robotic systems are appearing in more and more industrial and professional service applications. However, while mechanical components and control systems are advancing rapidly, the legislation background and standards framework for such systems and machinery are lagging behind. As part of a fundamental research work targeting industrial robots and industry 4.0 solutions for completely automated slaughtering, it was revealed that there are no particular standards addressing robotics systems applied to the agrifood domain. More specifically, within the agrifood sector, the only standards existing for the meat industry and the red meat sector are hygienic standards related to machinery. None of the identified standards or regulations consider the safety of autonomous robot operations or human robot collaborations in the abattoirs. The goal of this paper is to provide a general overview of the regulations and standards (and similar guiding documents) relevant for such applications, that could possibly be used as guidelines during the development of inherently safe robotic systems for abattoirs. Reviewing and summarizing the relevant standard and legislation landscape should also offer some instrumental help regarding the foreseen certification procedure of meat processing robots and robot cells for slaughterhouses in the near future.

Yu.М. , O.V.

The article studies the problems of formation of the nomenclature and creation of stocks of material values of the state material reserve, consisting of stocks of raw materials, fuel and energy, material and technical resources, industrial and food products, medicines and medical devices intended for use in peacetime, as well as in a special period in the conditions of reforming the system of the state material reserve and the negative impact of large-scale aggression of the Russian Federation and its consequences. According to the results of the study, selection criteria for the formation of the nomenclature of material values of the state material reserve, norms of their accumulation, including the level of irreducible stock, are proposed. At the same time, calculations of norms of accumulation and sizes of the irreducible stock of material values of the state reserve are proposed to be carried out by applying the product of indicators of daily consumption of material values, duration of provision and population group size. It has been determined that the creation and maintenance in normative levels of the irreducible stock and volumes of accumulation of stocks of material values of the state reserve, its ability to ensure the fulfillment of the tasks stipulated by the legislation should be implemented gradually, taking into account the priority of accumulation of stocks in the areas of material values and the ability of full and timely financing of the state material reserve in an amount sufficient for their proper provision.

Alexander I. Zemlin, Maria A. Matveeva, Evgeniia V. Gots

The purpose of studying the issues of ensuring the safe and efficient use of unmanned vehicles in a megalopolis in the context of the spread of infectious diseases using public regulation tools is to develop, based on the application of methods of systemic legal analysis, evidence-based proposals for improving legislation in the interests of using unmanned vehicles in a megalopolis in order to countering the spread of epidemics and pandemics. The lack of results of scientific understanding of the experience of legal support for the functioning of the transport system of the metropolis in the context of epidemics and pandemics, and, in particular, during the period of countering the spread of the coronavirus infection COVID-19, both in foreign countries and in Russia, seems to be an important negative factor that minimizes the potential of using self-driving cars to reduce cross-contamination opportunities in high-density areas, which undoubtedly include metropolitan areas. Background. The ever-increasing threat of the spread of epidemics and pandemics, as well as their consequences, demonstrated, among other things, by the results of the coronavirus infection COVID-19, entails the well-founded attention of practitioners and the interest of scientists in determining the means to counter the threats of the spread of infectious diseases. Of particular importance is the search for such funds to ensure the safety of the population living in certain areas, characterized by the presence of additional factors that lead to a significant increase in the risk of the disease. In particular, we are talking about the situation in megacities characterized by an extremely high population density, which, combined with the presence of developed transport communications, increased passenger traffic, contributes to “cross” infection, the rapid spread of mass infectious diseases. At the same time, as the results of the study show, the use of unmanned vehicles in a metropolis in the interests of monitoring the epidemiological situation in areas with a high level of infection, delivering medicines to patients, etc. ensures minimization of the risk of infection of medical personnel, significantly reduces material costs and time spent on the implementation of such measures. Materials and research methods. The use of methods of comparative legal analysis allowed the authors, based on the results of studying foreign legal sources, analytical data and scientific literature, allowed the authors to assess the applicability of the experience of legal regulation and the organization of the safe use of unmanned vehicles in a metropolis in the context of the emergence and counteraction to the spread of epidemics and pandemics. A formal dogmatic analysis of Russian regulatory legal and individual acts on the issues of ensuring the safe use of unmanned vehicles in an urban agglomeration made it possible to identify gaps and contradictions that minimize the potential for legal impact on the participants in the relations under study. Results. A comprehensive systematic and legal study of scientific sources, norms of international, foreign and Russian legislation, law enforcement practice allowed the authors to formulate proposals for improving the system of legal regulation in order to safely use unmanned vehicles in a metropolis in the event of risks of occurrence and counteracting the spread of epidemics and pandemics. Conclusion. Legalization in Russian public law of the special requirements proposed by the authors for ensuring the use of unmanned vehicles in a metropolis, their network interaction, the legal grounds for admission to operation and the conditions for their safe use to monitor the epidemiological situation, supply the population with medicines and food in the context of the introduction of quarantine measures in urban agglomeration, can contribute to the elimination of identified gaps in administrative regulation.

A. L. Santashov, M. Yu. Kashinskiy, L. N. Tarabuev

The subject of the research is the problems of legislative regulation of compulsory psychiatric measures in the criminal legislation of the Republic of Belarus and the Russian Federation.The purpose of the article is to confirm or refute the hypothesis that there are defects in legislative regulation that prevent the effective use of compulsory psychiatric treatment. They are not eliminated in the Criminal Codes of the Russian Federation and the Republic of Belarus. Research methodology. On the basis of a system-integrated approach on an interdisciplinary basis, a scientific analysis of special legal and forensic psychiatric scientific literature and interpretation of the criminal legislation of the Republic of Belarus and the Russian Federation were carried out.Main results. An analysis of the Criminal Codes of the Republic of Belarus and the Russian Federation showed that compulsory psychiatric treatment is an independent institution of criminal law, which is regulated in sufficient detail in the national criminal legislation. The article includes a comparative legal analysis of the norms of the criminal legislation of both states (Chapter 14 of the Criminal Code of the Republic of Belarus and Chapter 15 of the Criminal Code of the Russian Federation) that regulate psychiatric coercive measures applied to persons with mental disorders who have committed illegal acts. Based on the results of the study, a number of problems were identified in the legislative regulation of psychiatric compulsory measures in the criminal codes of both states, the authors propose directions for further improvement of the current criminal legislation.Conclusions. The Criminal Codes of the Republic of Belarus and the Russian Federation contain only general criteria for choosing the type of psychiatric measures of a coercive nature, in the most general form, the procedure for their change and termination is provided, they need editorial clarification of their purpose. There is no legislative definition of the concepts of “coercive security measures and treatment” (Chapter 14 of the Criminal Code of the Republic of Belarus) and “compulsory measures of a medical nature” (Chapter 15 of the Criminal Code of the Russian Federation), etc., which causes serious difficulties in law enforcement practice and indicates the need to continue work to improve the current criminal legislation.The terminology used “coercive and security measures and treatment” (Chapter 14 of the Criminal Code of the Republic of Belarus) and “coercive measures of a medical nature” (Chapter 15 of the Criminal Code of the Russian Federation) does not reflect the specifics of these measures, which in their essence and content are exclusively psychiatric measures. The identified problems of legislative regulation of psychiatric compulsory measures in the criminal legislation of both states require their further resolution, and based on the interdisciplinary medico-legal nature of the problems involved, with the obligatory involvement of forensic psychiatrists in their solution.

Bobak Toussi Kiani, Agnes Villanyi, Seth Lloyd

A central task in medical imaging is the reconstruction of an image or function from data collected by medical devices (e.g., CT, MRI, and PET scanners). We provide quantum algorithms for image reconstruction with exponential speedup over classical counterparts when data is input as a quantum state. Since outputs of our algorithms are stored in quantum states, individual pixels of reconstructed images may not be efficiently accessed classically; instead, we discuss various methods to extract information from outputs using a variety of quantum post-processing algorithms.

Xudong Wang, Shizhong Han, Yunqiang Chen et al.

A volumetric attention(VA) module for 3D medical image segmentation and detection is proposed. VA attention is inspired by recent advances in video processing, enables 2.5D networks to leverage context information along the z direction, and allows the use of pretrained 2D detection models when training data is limited, as is often the case for medical applications. Its integration in the Mask R-CNN is shown to enable state-of-the-art performance on the Liver Tumor Segmentation (LiTS) Challenge, outperforming the previous challenge winner by 3.9 points and achieving top performance on the LiTS leader board at the time of paper submission. Detection experiments on the DeepLesion dataset also show that the addition of VA to existing object detectors enables a 69.1 sensitivity at 0.5 false positive per image, outperforming the best published results by 6.6 points.

Hongxu Yang, Caifeng Shan, Alexander F. Kolen et al.

Medical instrument detection is essential for computer-assisted interventions since it would facilitate the surgeons to find the instrument efficiently with a better interpretation, which leads to a better outcome. This article reviews medical instrument detection methods in the ultrasound-guided intervention. First, we present a comprehensive review of instrument detection methodologies, which include traditional non-data-driven methods and data-driven methods. The non-data-driven methods were extensively studied prior to the era of machine learning, i.e. data-driven approaches. We discuss the main clinical applications of medical instrument detection in ultrasound, including anesthesia, biopsy, prostate brachytherapy, and cardiac catheterization, which were validated on clinical datasets. Finally, we selected several principal publications to summarize the key issues and potential research directions for the computer-assisted intervention community.

Fabrícia Helena Linhares Coelho da Silva Pereira

Este trabalho objetiva identificar o panorama de contratação do setor privado sem fins lucrativos pelo Sistema Único de Saúde para realização de serviços de saúde de média e alta complexidade em atenção hospitalar, e a relação desse panorama com a diretriz de descentralização do sistema, que deve garantir atendimentos dos grandes centros urbanos às zonas onde os serviços públicos são mais escassos. Questiona-se se a contratação expandida de serviços hospitalares privados representa um afastamento das direções nacional e estadual da execução dos serviços de saúde a partir da municipalização generalizada. A metodologia de pesquisa é exploratória e descritiva, com base bibliográfica na literatura dos direitos Sanitário e Administrativo. A partir da análise dos gastos públicos com serviços hospitalares,nos últimos cinco anos, identifica-se que as entidades sem fins lucrativos tiveram mais investimento em produção hospitalar do que as esferas da administração pública direta e indireta de todos os entes. A título de contribuição para a busca de soluções para o problema, conclui-se que a menor participação da gestão nacional do  SUS na prestação de serviços de saúde acarreta maior compra de serviços da iniciativa privada pelos entes, descumprindo a determinação constitucional de complementariedade da participação do setor privado no SUS. Ademais, a prestação de serviços hospitalares nesses moldes promove a falta de equilíbrio na alocação de leitos hospitalares, comprometendo a igualdade de condições no acesso às ações e aos serviços de saúde, objetivo da descentralização, e o adequado funcionamento do sistema.

S. N. Tishkina, V. E. Matskevich, Yu. A. Ledovskikh et al.

Aim. To conduct an analytical review of the regulatory legal framework on the resources provision required in medical organizations for the provision of oncological medical care based on clinical guidelines.Materials and methods. Using Russian normative legal and regulative documents on the oncological medical care, the authors studied the normative regulation of the issues of both resources’ provision for medical organizations and resources’ demand assessment, including the need for financial support from various sources of the budgetary system of the Russian Federation. The analysis was carried out in two regulatory areas: financing and organization of the provision of medical care. The authors examined the main legislation acts relating to oncological medical care, including documents of the compulsory medical insurance, the annual decrees of the Government of the Russian Federation on the program of state guarantees of free provision of medical care to citizens for the coming year and the planned period, the decree of the Government of the Russian Federation regulating the provision of medicines and medical products, provisions on the organization of medical care by type of medical care, procedures for the provision of oncological medical care, regulatory documents on clinical guidelines and standards of medical care.Results. The content analysis of the studied materials showed that certain components of the resources’ provision, including the financial provision of oncological medical care, are regulated by various regulatory documents, and there is no methodology for assessing the resources’ demand based on clinical guidelines.Conclusion. New approaches to assessing the resources’ demand for the provision of medical care and the required financial support based on clinical guidelines need to be developed.

Dominik Müller, Frank Kramer

The increased availability and usage of modern medical imaging induced a strong need for automatic medical image segmentation. Still, current image segmentation platforms do not provide the required functionalities for plain setup of medical image segmentation pipelines. Already implemented pipelines are commonly standalone software, optimized on a specific public data set. Therefore, this paper introduces the open-source Python library MIScnn. The aim of MIScnn is to provide an intuitive API allowing fast building of medical image segmentation pipelines including data I/O, preprocessing, data augmentation, patch-wise analysis, metrics, a library with state-of-the-art deep learning models and model utilization like training, prediction, as well as fully automatic evaluation (e.g. cross-validation). Similarly, high configurability and multiple open interfaces allow full pipeline customization. Running a cross-validation with MIScnn on the Kidney Tumor Segmentation Challenge 2019 data set (multi-class semantic segmentation with 300 CT scans) resulted into a powerful predictor based on the standard 3D U-Net model. With this experiment, we could show that the MIScnn framework enables researchers to rapidly set up a complete medical image segmentation pipeline by using just a few lines of code. The source code for MIScnn is available in the Git repository: https://github.com/frankkramer-lab/MIScnn.