Hasil untuk "History of medicine. Medical expeditions"

Junho JUNG

HIV/AIDS control in Korea characterized with “AIDS Prevention Law”, enacted in 1987. It was one of the first separate legal enforcement around the world that governs control of the HIV/AIDS epidemic. Yet with significant limitations regarding human rights, as it criminalized HIV infection, and dictates penal action against ‘transmitters’. This papers looks into how HIV/AIDS epidemic started in Korea in 1980s, with specific focus on disease narrative that was constructed by the government. It was known to United States Forces Korea, that HIV was already spreading steady into Korean female sex workers around U.S. military bases in 1985. This information was concealed by Korean Ministry of health, in the face of upcoming international events such as 1988 Seoul Olympics. Instead, the Korean government turned public attention to ‘imported’ cases, constructing narrative that HIV/AIDS as a foreign disease. With direction of president, HIV/AIDS control focus on compulsory testing and isolation of identified risk group of sexual minorities and sex workers around U.S. military bases. This narrative of foreign disease had lasting impact even after democratization of Korea in 1987, as civil society, unaware that HIV/AIDS had already became endemic in Korea, argued to enforced compulsory testing against foreign nationals upon entry. This paper argues that disease narratives were carefully constructed by the government during early phase of HIV/AIDS epidemic in Korea, and used legal structure as ways to conceal the actual prevalence from both domestic and international attention.

Alexandre White, Gabriel Salgado Ribeiro de Sá

This interview explores Alexandre White’s contributions to the history of medicine, focusing on his latest work, Epidemic Orientalism: race, capital, and the governance of infectious disease, published by Stanford University Press in 2023. White’s extensive research on infectious disease regulation is examined, covering his motivations, ongoing projects, and the pivotal role of comparative methods in the field. His theoretical frameworks are also highlighted for the valuable insights they provide for understanding the complex dynamics of global health, particularly amidst emerging international tensions surrounding infectious diseases.

Ahmed Kabil, Ghada Khoriba, Mina Yousef et al.

Medical Image Segmentation (MIS) stands as a cornerstone in medical image analysis, playing a pivotal role in precise diagnostics, treatment planning, and monitoring of various medical conditions. This paper presents a comprehensive and systematic survey of MIS methodologies, bridging the gap between traditional image processing techniques and modern deep learning approaches. The survey encompasses thresholding, edge detection, region-based segmentation, clustering algorithms, and model-based techniques while also delving into state-of-the-art deep learning architectures such as Convolutional Neural Networks (CNNs), Fully Convolutional Networks (FCNs), and the widely adopted U-Net and its variants. Moreover, integrating attention mechanisms, semi-supervised learning, generative adversarial networks (GANs), and Transformer-based models is thoroughly explored. In addition to covering established methods, this survey highlights emerging trends, including hybrid architectures, cross-modality learning, federated and distributed learning frameworks, and active learning strategies, which aim to address challenges such as limited labeled datasets, computational complexity, and model generalizability across diverse imaging modalities. Furthermore, a specialized case study on lumbar spine segmentation is presented, offering insights into the challenges and advancements in this relatively underexplored anatomical region. Despite significant progress in the field, critical challenges persist, including dataset bias, domain adaptation, interpretability of deep learning models, and integration into real-world clinical workflows.

Gabriele Lozupone, Alessandro Bria, Francesco Fontanella et al.

This study presents Latent Diffusion Autoencoder (LDAE), a novel encoder-decoder diffusion-based framework for efficient and meaningful unsupervised learning in medical imaging, focusing on Alzheimer disease (AD) using brain MR from the ADNI database as a case study. Unlike conventional diffusion autoencoders operating in image space, LDAE applies the diffusion process in a compressed latent representation, improving computational efficiency and making 3D medical imaging representation learning tractable. To validate the proposed approach, we explore two key hypotheses: (i) LDAE effectively captures meaningful semantic representations on 3D brain MR associated with AD and ageing, and (ii) LDAE achieves high-quality image generation and reconstruction while being computationally efficient. Experimental results support both hypotheses: (i) linear-probe evaluations demonstrate promising diagnostic performance for AD (ROC-AUC: 90%, ACC: 84%) and age prediction (MAE: 4.1 years, RMSE: 5.2 years); (ii) the learned semantic representations enable attribute manipulation, yielding anatomically plausible modifications; (iii) semantic interpolation experiments show strong reconstruction of missing scans, with SSIM of 0.969 (MSE: 0.0019) for a 6-month gap. Even for longer gaps (24 months), the model maintains robust performance (SSIM > 0.93, MSE < 0.004), indicating an ability to capture temporal progression trends; (iv) compared to conventional diffusion autoencoders, LDAE significantly increases inference throughput (20x faster) while also enhancing reconstruction quality. These findings position LDAE as a promising framework for scalable medical imaging applications, with the potential to serve as a foundation model for medical image analysis. Code available at https://github.com/GabrieleLozupone/LDAE

Muhammad Zubair, Muzammil Hussai, Mousa Ahmad Al-Bashrawi et al.

Multi-modal medical image fusion (MMIF) is increasingly recognized as an essential technique for enhancing diagnostic precision and facilitating effective clinical decision-making within computer-aided diagnosis systems. MMIF combines data from X-ray, MRI, CT, PET, SPECT, and ultrasound to create detailed, clinically useful images of patient anatomy and pathology. These integrated representations significantly advance diagnostic accuracy, lesion detection, and segmentation. This comprehensive review meticulously surveys the evolution, methodologies, algorithms, current advancements, and clinical applications of MMIF. We present a critical comparative analysis of traditional fusion approaches, including pixel-, feature-, and decision-level methods, and delves into recent advancements driven by deep learning, generative models, and transformer-based architectures. A critical comparative analysis is presented between these conventional methods and contemporary techniques, highlighting differences in robustness, computational efficiency, and interpretability. The article addresses extensive clinical applications across oncology, neurology, and cardiology, demonstrating MMIF's vital role in precision medicine through improved patient-specific therapeutic outcomes. Moreover, the review thoroughly investigates the persistent challenges affecting MMIF's broad adoption, including issues related to data privacy, heterogeneity, computational complexity, interpretability of AI-driven algorithms, and integration within clinical workflows. It also identifies significant future research avenues, such as the integration of explainable AI, adoption of privacy-preserving federated learning frameworks, development of real-time fusion systems, and standardization efforts for regulatory compliance.

Siyi Xun, Yue Sun, Jingkun Chen et al.

Rapid advances in medical imaging technology underscore the critical need for precise and automated image quality assessment (IQA) to ensure diagnostic accuracy. Existing medical IQA methods, however, struggle to generalize across diverse modalities and clinical scenarios. In response, we introduce MedIQA, the first comprehensive foundation model for medical IQA, designed to handle variability in image dimensions, modalities, anatomical regions, and types. We developed a large-scale multi-modality dataset with plentiful manually annotated quality scores to support this. Our model integrates a salient slice assessment module to focus on diagnostically relevant regions feature retrieval and employs an automatic prompt strategy that aligns upstream physical parameter pre-training with downstream expert annotation fine-tuning. Extensive experiments demonstrate that MedIQA significantly outperforms baselines in multiple downstream tasks, establishing a scalable framework for medical IQA and advancing diagnostic workflows and clinical decision-making.

Johannes Kiechle, Stefan M. Fischer, Daniel M. Lang et al.

The sharp rise in medical tomography examinations has created a demand for automated systems that can reliably extract informative features for downstream tasks such as tumor characterization. Although 3D volumes contain richer information than individual slices, effective 3D classification remains difficult: volumetric data encode complex spatial dependencies, and the scarcity of large-scale 3D datasets has constrained progress toward 3D foundation models. As a result, many recent approaches rely on 2D vision foundation models trained on natural images, repurposing them as feature extractors for medical scans with surprisingly strong performance. Despite their practical success, current methods that apply 2D foundation models to 3D scans via slice-based decomposition remain fundamentally limited. Standard slicing along axial, sagittal, and coronal planes often fails to capture the true spatial extent of a structure when its orientation does not align with these canonical views. More critically, most approaches aggregate slice features independently, ignoring the underlying 3D geometry and losing spatial coherence across slices. To overcome these limitations, we propose TomoGraphView, a novel framework that integrates omnidirectional volume slicing with spherical graph-based feature aggregation. Instead of restricting the model to axial, sagittal, or coronal planes, our method samples both canonical and non-canonical cross-sections generated from uniformly distributed points on a sphere enclosing the volume. We publicly share our accessible code base at http://github.com/compai-lab/2025-MedIA-kiechle and provide a user-friendly library for omnidirectional volume slicing at https://pypi.org/project/OmniSlicer.

Feng Zhao, Yizhou Wu, Mingzhe Hu et al.

Medical imaging has played a pivotal role in advancing and refining digital twin technology, allowing for the development of highly personalized virtual models that represent human anatomy and physiological functions. A key component in constructing these digital twins is the integration of high-resolution imaging data, such as MRI, CT, PET, and ultrasound, with sophisticated computational models. Advances in medical imaging significantly enhance real-time simulation, predictive modeling, and early disease diagnosis, individualized treatment planning, ultimately boosting precision and personalized care. Although challenges persist, such as the complexity of anatomical modeling, integrating various imaging modalities, and high computational demands, recent progress in imaging and machine learning has greatly improved the precision and clinical applicability of digital twins. This review investigates the role of medical imaging in developing digital twins across organ systems. Key findings demonstrate that improvements in medical imaging have enhanced the diagnostic and therapeutic potential of digital twins beyond traditional methods, particularly in imaging accuracy, treatment effectiveness, and patient outcomes. The review also examines the technical barriers that currently limit further development of digital twin technology, despite advances in medical imaging, and outlines future research avenues aimed at overcoming these challenges to unlock the full potential of this technology in precision medicine.

Sicheng Wang, Che Liu, Rossella Arcucci

Recent advancements in medical vision-language pre-training (MedVLP) have significantly enhanced zero-shot medical vision tasks such as image classification by leveraging large-scale medical image-text pair pre-training. However, the performance of these tasks can be heavily influenced by the variability in textual prompts describing the categories, necessitating robustness in MedVLP models to diverse prompt styles. Yet, this sensitivity remains underexplored. In this work, we are the first to systematically assess the sensitivity of three widely-used MedVLP methods to a variety of prompts across 15 different diseases. To achieve this, we designed six unique prompt styles to mirror real clinical scenarios, which were subsequently ranked by interpretability. Our findings indicate that all MedVLP models evaluated show unstable performance across different prompt styles, suggesting a lack of robustness. Additionally, the models' performance varied with increasing prompt interpretability, revealing difficulties in comprehending complex medical concepts. This study underscores the need for further development in MedVLP methodologies to enhance their robustness to diverse zero-shot prompts.

Miguel Rios

Large Language Models (LLMs) have shown promising results on machine translation for high resource language pairs and domains. However, in specialised domains (e.g. medical) LLMs have shown lower performance compared to standard neural machine translation models. The consistency in the machine translation of terminology is crucial for users, researchers, and translators in specialised domains. In this study, we compare the performance between baseline LLMs and instruction-tuned LLMs in the medical domain. In addition, we introduce terminology from specialised medical dictionaries into the instruction formatted datasets for fine-tuning LLMs. The instruction-tuned LLMs significantly outperform the baseline models with automatic metrics.

Qiang Qiao, Wenyu Wang, Meixia Qu et al.

The field of medical image segmentation is challenged by domain generalization (DG) due to domain shifts in clinical datasets. The DG challenge is exacerbated by the scarcity of medical data and privacy concerns. Traditional single-source domain generalization (SSDG) methods primarily rely on stacking data augmentation techniques to minimize domain discrepancies. In this paper, we propose Random Amplitude Spectrum Synthesis (RASS) as a training augmentation for medical images. RASS enhances model generalization by simulating distribution changes from a frequency perspective. This strategy introduces variability by applying amplitude-dependent perturbations to ensure broad coverage of potential domain variations. Furthermore, we propose random mask shuffle and reconstruction components, which can enhance the ability of the backbone to process structural information and increase resilience intra- and cross-domain changes. The proposed Random Amplitude Spectrum Synthesis for Single-Source Domain Generalization (RAS^4DG) is validated on 3D fetal brain images and 2D fundus photography, and achieves an improved DG segmentation performance compared to other SSDG models.

Daniele Cristina Carqueijeiro de Medeiros, Marcelo Moraes e Silva, Evelise Amgarten Quitzau

Resumo O artigo analisa concepções veiculadas pelos clubes de remo e imprensa esportiva sobre o rio Tietê nas primeiras quatro décadas do século XX, em São Paulo. As fontes históricas utilizadas foram jornais paulistanos e revistas produzidas pelos clubes. Entre 1900 e 1920, tais instituições deram início a práticas esportivas aquáticas, e as fontes apontam um discurso positivo veiculado à promoção da saúde pelos esportes. Entretanto, essa relação se alterou nas décadas de 1930 e 1940. De espaço indissociável das práticas esportivas, da saúde e dos divertimentos, o Tietê passou a ser considerado inadequado, dada a poluição do rio e a impossibilidade de realização de provas esportivas.

Наталія Руснак, Іванна Струк, Юлія Руснак

Вивчення народного (діалектного) мовлення є важливим у шкільному курсі української мови, особливо в сільській місцевості, оскільки дає змогу зрозуміти специфіку української мови в її територіальному прояві. Усвідомлення специфіки територіального різновиду української мови сприятиме вияву патріотичних почуттів учнів, прагненню зберегти етнічну (територіальну) своєрідність. Актуальність нашого дослідження зумовлена необхідністю вивчення народного мовлення у шкільному курсі української мови. Мета статті – розробити тематику однієї з форм позакласної роботи (колоквіуму) з української мови для вивчення розмовної мови в загальноосвітній школі. Дослідження проведено на матеріалі говірок сіл Улашківці та Заболотівка Чортківського району Тернопільської області. Методи дослідження. Дослідження проводилось за основними загальнонауковими методами аналізу та синтезу, а також за лінгвістичними методами – описовим, структурним. За допомогою описового методу було узагальнено фактичний матеріал. Інвентаризації та систематизації мовних явищ сприяв структурний метод. Висновки. Внаслідок недостатнього охоплення відомостями з «Діалектології української мови» у шкільному курсі української мови підвищується роль позакласної роботи як одного з видів виховної роботи, метою якої є поглиблення інформацію з цього курсу, а також пояснити школярам відмінності народних (розмовних) слів від буквальних назв. Тому учні повинні розуміти специфіку вияву територіального різновиду національної української мови. Для цього найоптимальнішою формою позааудиторної роботи є колоквіум, під час якого вчитель надасть відомості про типи діалектних явищ, розповість про походження діалектизмів мовлення, запропонує відомості про лексику календарного циклу (серед. класи), пояснити фонетико-граматичні закономірності діалектного мовлення (старші класи).

Haochun Wang, Chi Liu, Nuwa Xi et al.

Large Language Models (LLMs), such as the LLaMA model, have demonstrated their effectiveness in various general-domain natural language processing (NLP) tasks. Nevertheless, LLMs have not yet performed optimally in biomedical domain tasks due to the need for medical expertise in the responses. In response to this challenge, we propose HuaTuo, a LLaMA-based model that has been supervised-fine-tuned with generated QA (Question-Answer) instances. The experimental results demonstrate that HuaTuo generates responses that possess more reliable medical knowledge. Our proposed HuaTuo model is accessible at https://github.com/SCIR-HI/Huatuo-Llama-Med-Chinese.

Nicolás Gaggion, Rodrigo Echeveste, Lucas Mansilla et al.

It has recently been shown that deep learning models for anatomical segmentation in medical images can exhibit biases against certain sub-populations defined in terms of protected attributes like sex or ethnicity. In this context, auditing fairness of deep segmentation models becomes crucial. However, such audit process generally requires access to ground-truth segmentation masks for the target population, which may not always be available, especially when going from development to deployment. Here we propose a new method to anticipate model biases in biomedical image segmentation in the absence of ground-truth annotations. Our unsupervised bias discovery method leverages the reverse classification accuracy framework to estimate segmentation quality. Through numerical experiments in synthetic and realistic scenarios we show how our method is able to successfully anticipate fairness issues in the absence of ground-truth labels, constituting a novel and valuable tool in this field.

G. Nandakumar, N. Ryde, M. Montelius et al.

Phosphorus (P) is considered to be one of the key elements for life, making it an important element to look for in the abundance analysis of spectra of stellar systems. Yet, there exists only a handful of spectroscopic studies to estimate the P abundances and investigate its trend across a range of metallicities. We have observed full HK band spectra at a spectral resolving power of R=45,000 with IGRINS instrument. Abundances are determined using SME in combination with 1D MARCS stellar atmosphere models. The investigated sample of stars have reliable stellar parameters estimated using optical FIES spectra (GILD; Jönsson et al. in prep.). In order to determine the P abundances from the 16482.92 Angstrom P line, we take special care of the CO($ν=7-4$) blend. We determine the C, N, O abundances from atomic carbon and a range of non-blended molecular lines (CO, CN, OH) which are aplenty in the H band region of K giant stars, assuring an appropriate modelling of the blending CO($ν=7-4$) line. We present [P/Fe] vs [Fe/H] trend for 38 K giant stars in the metallicity range of -1.2 dex $<$ [Fe/H] $<$ 0.4 dex. We find that our trend matches well with the compiled literature sample of prominently dwarf stars and limited number of giant stars. Our trend is found to be higher by $\sim$ 0.05 - 0.1 dex compared to the theoretical chemical evolution trend in Cescutti et al. 2012 resulting from core collapse supernova (type II) of massive stars with the P yields from Kobayashi et al. (2006) arbitrarily increased by a factor of 2.75. Thus the enhancement factor might need to be $\sim$ 0.05 - 0.1 dex higher to match our trend. We also find an empirically determined primary behaviour for phosphorus. Furthermore, the phosphorus abundance is found to be elevated by $\sim$ 0.6 - 0.9 dex in two metal poor s-enriched stars compared to the theoretical chemical evolution trend.

Natalia RUSNAK

The relevance of scientific work is determined by the need to research the vocabulary of dialects that keep the original worldview of the nation, relics of spiritual culture, understanding of ethical norms of the people, relations with other peoples nations. The purpose of scientific research is to find out the peculiarities of the lexical system of Bukovina dialects. The novelty of article is that, for the first time, the linguistic material of the Yuzhynets dialect of Kitsman district of Chernivtsi region is attracted for scientific circulation. In scientific work the structural, comparative-historical methods and the approach of component analysis are used Conclusions. Dialecticisms are the basis of Yuzhynets dialect vocabulary. Dialecticisms are specific Ukrainian words which testify the single linguistic world of the Ukrainian nation. The source of dialecticisms is the Slavic language: вирЕня, вирІтка відЕй, витАти гальманИ, гАчі, глошИти, грЄтка, ґУндзлик дрАби. Among the borrowed vocabulary there are words which came from Romanian: бриндУшка букАта бумбОна, врИтний, гор- дЯв, ґрЕжда, дзер, клАка; Polish: бештифрАнт, вАріят, гаму- вАти, ґвавт, цИрка, збан, зґАрда; German: алЯрм бАвна, бан- тИна, братрУра, фасувАти, гИмбель, ґріс; Hungarian: банувА- ти, батЯр, бУнда, ганч, рУнтати, пУгарь; Turkish: бАйда, кАлфа, катрАн, кирИня, талабувАти, фис; Greek: анкІрь, кАн- діти, катавАсія; Latin: кАпа, бесАги, пОрція, Russian: болвАн, капЕц, тужУрка; Czech: крижЄвка. New words are included to the lexical system of Yuzhynets dialect. Among lexical innovations there are jargonisms – the wor- ds which are used by young generation.

Boleslav L. Lichterman

The aim of this paper is to compare “Zapiski Vracha” (“Confessions of a Physician”, first published in 1901) by Vikenty Veresaev to “Aerztliche Ethik” (“Doctors’ Ethics”, first published in 1902; two Russian editions were published in 1903 and 1904) by Albert Moll. It starts with an overview of medical ethics in Russia at the turn of the 20th century in relation to zemstvo medicine, followed by reception of Veresaev’s “Confessions of a Physician” by Russian and German physicians, and of Moll’s “Doctors’ Ethics” in Russia. Comparison of these two books may serve as a good example of a search for common philosophical foundations of medical ethics as well as the impact of national cultural traditions.

Jörg Sander, Bob D. de Vos, Ivana Išgum

Although high resolution isotropic 3D medical images are desired in clinical practice, their acquisition is not always feasible. Instead, lower resolution images are upsampled to higher resolution using conventional interpolation methods. Sophisticated learning-based super-resolution approaches are frequently unavailable in clinical setting, because such methods require training with high-resolution isotropic examples. To address this issue, we propose a learning-based super-resolution approach that can be trained using solely anisotropic images, i.e. without high-resolution ground truth data. The method exploits the latent space, generated by autoencoders trained on anisotropic images, to increase spatial resolution in low-resolution images. The method was trained and evaluated using 100 publicly available cardiac cine MR scans from the Automated Cardiac Diagnosis Challenge (ACDC). The quantitative results show that the proposed method performs better than conventional interpolation methods. Furthermore, the qualitative results indicate that especially finer cardiac structures are synthesized with high quality. The method has the potential to be applied to other anatomies and modalities and can be easily applied to any 3D anisotropic medical image dataset.

Diana Garcia-Hernandez, Xochitl Lopez-Rendon, Mariana Hernandez-Bojorquez et al.

The clinical practice of Medical Physics in Mexico has not been subject of comprehensive occupational analyses. The absence of such studies not only arises radiation safety concerns, but also imposes challenges to work-policy making. This work presents an initial effort to overview the current occupational status of clinical Medical Physics in Mexico. Our motivation and final goal is to support, based on data, the legal recognition of Medical Physics high-end training, and to provide information that will potentially improve the Mexican health-care system. For the ease of analysis, the concept of "person(s) developing Medical Physics tasks" (PDMPT) is introduced to refer to professionals playing clinical medical physicist's (cMP) roles, disregarding academic profile or training. A database of PDMPT in Mexico was built from official sources and personal communication with peers. Our database included: employer(s), specialty and academic profile. It was found that 133 hospitals in Mexico employ PDMPT, 49% of which are public institutions. A total of 360 positions involving cMP roles were identified in the National Health-Care System, 77% of which corresponded to radiation oncology. Public health services hold 65% of the reported positions. Cases of double- and triple-shift workers where identified in this study, as 283 PDMPT occupied the 360 reported positions. Of all PDMPT, 32% were women. Only 40% of PDMPT hold a graduate degree in Medical Physics, 46% of which were located in the most densely populated region of Mexico. Our data suggests that Mexico is far from fulfilling the international recommendations regarding cMP academic profile; however, this problem could be solved in the near future for the specific cases of radiation oncology and nuclear medicine services in the public health-care sector.