Hasil untuk "Human anatomy"

D. Worthen

A. Karni, D. Sagi

A. Bicchi

P. McMenamin, Michelle R. Quayle, C. McHenry et al.

G. Longhurst, Danya M. Stone, K. Dulohery et al.

The Covid‐19 pandemic has driven the fastest changes to higher education across the globe, necessitated by social distancing measures preventing face‐to‐face teaching. This has led to an almost immediate switch to distance learning by higher education institutions. Anatomy faces some unique challenges. Intrinsically, anatomy is a three‐dimensional subject that requires a sound understanding of the relationships between structures, often achieved by the study of human cadaveric material, models, and virtual resources. This study sought to identify the approaches taken in the United Kingdom and Republic of Ireland to deliver anatomical education through online means. Data were collected from 14 different universities in the United Kingdom and Republic of Ireland and compared adopting a thematic analysis approach. Once themes were generated, they were collectively brought together using a strength, weakness, opportunity, threat (SWOT) analysis. Key themes included the opportunity to develop new online resources and the chance to engage in new academic collaborations. Academics frequently mentioned the challenge that time constrains could place on the quality and effectiveness of these resources; especially as in many cases the aim of these resources was to compensate for a lack of exposure to cadaveric exposure. Comparisons of the actions taken by multiple higher education institutions reveal the ways that academics have tried to balance this demand. Discussions will facilitate decisions being made by higher education institutions regarding adapting the curriculum and assessment methods in anatomy.

G. Grimbizis, S. Gordts, A. Di Spiezio Sardo et al.

STUDY QUESTION What classification system is more suitable for the accurate, clear, simple and related to the clinical management categorization of female genital anomalies? SUMMARY ANSWER The new ESHRE/ESGE classification system of female genital anomalies is presented. WHAT IS KNOWN ALREADY Congenital malformations of the female genital tract are common miscellaneous deviations from normal anatomy with health and reproductive consequences. Until now, three systems have been proposed for their categorization but all of them are associated with serious limitations. STUDY DESIGN, SIZE AND DURATION The European Society of Human Reproduction and Embryology (ESHRE) and the European Society for Gynaecological Endoscopy (ESGE) have established a common Working Group, under the name CONUTA (CONgenital UTerine Anomalies), with the goal of developing a new updated classification system. A scientific committee (SC) has been appointed to run the project, looking also for consensus within the scientists working in the field. PARTICIPANTS/MATERIALS, SETTING, METHODS The new system is designed and developed based on (i) scientific research through critical review of current proposals and preparation of an initial proposal for discussion between the experts, (ii) consensus measurement among the experts through the use of the DELPHI procedure and (iii) consensus development by the SC, taking into account the results of the DELPHI procedure and the comments of the experts. Almost 90 participants took part in the process of development of the ESHRE/ESGE classification system, contributing with their structured answers and comments. MAIN RESULTS AND THE ROLE OF CHANCE The ESHRE/ESGE classification system is based on anatomy. Anomalies are classified into the following main classes, expressing uterine anatomical deviations deriving from the same embryological origin: U0, normal uterus; U1, dysmorphic uterus; U2, septate uterus; U3, bicorporeal uterus; U4, hemi-uterus; U5, aplastic uterus; U6, for still unclassified cases. Main classes have been divided into sub-classes expressing anatomical varieties with clinical significance. Cervical and vaginal anomalies are classified independently into sub-classes having clinical significance. LIMITATIONS, REASONS FOR CAUTION The ESHRE/ESGE classification of female genital anomalies seems to fulfill the expectations and the needs of the experts in the field, but its clinical value needs to be proved in everyday practice. WIDER IMPLICATIONS OF THE FINDINGS The ESHRE/ESGE classification system of female genital anomalies could be used as a starting point for the development of guidelines for their diagnosis and treatment. STUDY FUNDING/COMPETING INTEREST(S) None.

J. Geng

N. Robinson, K. Krieger, Faiza M. Khan et al.

Animal models have provided invaluable information in the pursuit of medical knowledge and alleviation of human suffering. The foundations of our basic understanding of disease pathophysiology and human anatomy can largely be attributed to preclinical investigations using various animal models. Recently, however, the scientific community, citing concerns about animal welfare as well as the validity and applicability of outcomes, has called the use of animals in research into question. In this review, we seek to summarize the current state of the use of animal models in research.

Stanley H. Ambrose

C. Lopez, O. Blanke

Zhuchenyang Liu, Yao Zhang, Yalan He et al.

Flex sensors are widely used in e-textiles for detecting joint motions and, subsequently, full-body movements. A critical initial step in utilizing these sensors is determining the optimal placement on the body to accurately capture human motions. This task requires a combination of expertise in fields such as anatomy, biomechanics, and textile design, which is seldom found in a single practitioner. Generative AI, such as Large Language Models (LLMs), has recently shown promise in facilitating design. However, to our knowledge, the extent to which LLMs can aid in the e-textile design process remains largely unexplored in the literature. To address this open question, we conducted a case study focusing on shoulder motion detection using flex sensors. We enlisted three human designers to participate in an experiment involving human-AI collaborative design. We examined design efficiency across three scenarios: designs produced by LLMs alone, by humans alone, and through collaboration between LLMs and human designers. Our quantitative and qualitative analyses revealed an intriguing relationship between expertise and outcomes: the least experienced human designer achieved continuous improvement through collaboration, ultimately matching the best performance achieved by humans alone, whereas the most experienced human designer experienced a decline in performance. Additionally, the effectiveness of human-AI collaboration is affected by the granularity of feedback - incremental adjustments outperformed sweeping redesigns - and the level of abstraction, with observation-oriented feedback producing better outcomes than prescriptive anatomical directives. These findings offer valuable insights into the opportunities and challenges associated with human-AI collaborative e-textile design.

Naif Almughamisi, Gibrael Abosamra, Adnan Albar et al.

<b>Background/Objectives</b>: The clinical adoption of AI in radiology requires models that balance high accuracy with interpretable, anatomically plausible reasoning. This study presents an integrated diagnostic framework that addresses this need by unifying a hybrid deep-learning architecture with explicit anatomical guidance and neuro-symbolic inference. <b>Methods</b>: The proposed system employs a dual-path model: an enhanced EfficientNetV2 backbone extracts hierarchical local features, whereas a refined Vision Transformer captures global contextual dependencies across the thoracic cavity. These representations are fused and critically disciplined through auxiliary segmentation supervision using CheXmask. This anchors the learned features to lung and cardiac anatomy, reducing reliance on spurious artifacts. This anatomical basis is fundamental to the interpretability pipeline. It confines Gradient-weighted Class Activation Mapping (Grad-CAM) visual explanations to clinically valid regions. Then, a novel neuro-symbolic reasoning layer is introduced. Using a fuzzy logic engine and radiological ontology, this module translates anatomically aligned neural activations into structured, human-readable diagnostic statements that explicitly articulate the model’s clinical rationale. <b>Results</b>: Evaluated on the NIH ChestX-ray14 dataset, the framework achieved a macro-AUROC of 0.9056 and a macro-accuracy of 93.9% across 14 pathologies, with outstanding performance on emphysema (0.9694), hernia (0.9711), and cardiomegaly (0.9589). The model’s generalizability was confirmed through external validation on the CheXpert dataset, yielding a macro-AUROC of 0.85. <b>Conclusions</b>: This study demonstrates a cohesive path toward clinically transparent and trustworthy AI by seamlessly integrating data-driven learning with anatomical knowledge and symbolic reasoning.

Dayo Rotimi Omotoso, Joy Oyiza Peter

Introduction: The application of modern and innovative technology into anatomical science education has been on a rapid increase globally in recent years with the anatomy tutors and students playing important and complementary roles in the process of the integration. Methods: This narrative perspective presents the current state of technological applications adopted in anatomy education in medical colleges in Africa and the roles of the anatomy tutors and students in the process. Results: The role of the anatomy tutors in African medical colleges include facilitation of technological integration into anatomy education and curriculum, promotion of active learning in anatomy, design of innovative assessment strategies, provision of relevant technological support, and advocate for provision of resources and infrastructure. Similarly, the trainees played important roles in the process of technological integration into anatomy education in Africa which include increased acceptability of technology for anatomy learning, utilisation and validation of innovative assessment methods, peer-advocacy for technology-driven learning, and bridging the technological gap. Conclusion: The active participation of both the tutors and students in the adoption of technological solutions and tools will continue to enhance the quality of anatomical science pedagogy across African medical colleges.

Zefeng Qian, Xincheng Yao, Yifei Huang et al.

Few-shot action recognition (FSAR) aims to classify human actions in videos with only a small number of labeled samples per category. The scarcity of training data has driven recent efforts to incorporate additional modalities, particularly text. However, the subtle variations in human posture, motion dynamics, and the object interactions that occur during different phases, are critical inherent knowledge of actions that cannot be fully exploited by action labels alone. In this work, we propose Language-Guided Action Anatomy (LGA), a novel framework that goes beyond label semantics by leveraging Large Language Models (LLMs) to dissect the essential representational characteristics hidden beneath action labels. Guided by the prior knowledge encoded in LLM, LGA effectively captures rich spatiotemporal cues in few-shot scenarios. Specifically, for text, we prompt an off-the-shelf LLM to anatomize labels into sequences of atomic action descriptions, focusing on the three core elements of action (subject, motion, object). For videos, a Visual Anatomy Module segments actions into atomic video phases to capture the sequential structure of actions. A fine-grained fusion strategy then integrates textual and visual features at the atomic level, resulting in more generalizable prototypes. Finally, we introduce a Multimodal Matching mechanism, comprising both video-video and video-text matching, to ensure robust few-shot classification. Experimental results demonstrate that LGA achieves state-of-the-art performance across multipe FSAR benchmarks.

Ankit Vadehra, Bill Johnson, Gene Saunders et al.

Text editing can involve several iterations of revision. Incorporating an efficient Grammar Error Correction (GEC) tool in the initial correction round can significantly impact further human editing effort and final text quality. This raises an interesting question to quantify GEC Tool usability: How much effort can the GEC Tool save users? We present the first large-scale dataset of post-editing (PE) time annotations and corrections for two English GEC test datasets (BEA19 and CoNLL14). We introduce Post-Editing Effort in Time (PEET) for GEC Tools as a human-focused evaluation scorer to rank any GEC Tool by estimating PE time-to-correct. Using our dataset, we quantify the amount of time saved by GEC Tools in text editing. Analyzing the edit type indicated that determining whether a sentence needs correction and edits like paraphrasing and punctuation changes had the greatest impact on PE time. Finally, comparison with human rankings shows that PEET correlates well with technical effort judgment, providing a new human-centric direction for evaluating GEC tool usability. We release our dataset and code at: https://github.com/ankitvad/PEET_Scorer.

Simone Baldovino, Savino Sciascia, Claudio Carta et al.

BackgroundUndiagnosed rare diseases (URDs) are a complex and multifaceted challenge, especially in low-and medium-income countries. They affect individuals with unique clinical features and lack a clear diagnostic label. Although the Undiagnosed Diseases Network International (UDNI) definition of URDs is not universally accepted, it is widely recognized.MethodsWe surveyed UDNI members and participants from other countries to explore the challenges posed by URDs and identify possible solutions. Participation in the survey was completely voluntary.ResultsThe survey revealed a need for more consensus on a universally accepted definition for URDs. Still, the UDNI definition gained widespread recognition and serves as a valuable framework for understanding and addressing the challenges of URDs. In addition to national or international networks, fostering a more substantial engagement and resource-sharing ethos among member countries is critical. Despite advances in genomics and diagnostic tools, the diagnostic journey for people living with URDs (PLURDs) remains arduous and often inconclusive. The availability of specialized centers and the utilization of whole exome sequencing (WES) and whole genome sequencing (WGS) vary across countries, with disparities due to healthcare systems, economic status, and government policies. Advocacy groups play a crucial role in supporting PLURDs.ConclusionA unified commitment to prioritizing URDs on the global health agenda, paired with targeted funding, stipulated national strategies, and aligned international cooperation, is imperative to leveling the playing field for the diagnosis and management of URDs and capitalizing on the potential of Advocacy Groups as allies in this endeavor.

Milena Edite Casé de Oliveira, José Marcos Nascimento de Sousa, Gerlane Da Silva Vieira Torres et al.

The assessment of visual attention is important in visual and cognitive neuroscience, providing objective measures for researchers and clinicians. This study investigated the effects of chemotherapy on eye movements in women with breast cancer. Twelve women with breast cancer and twelve healthy controls aged between 33 and 59 years completed a visual search task, identifying an Arabic number among 79 alphabetic letters. Test duration, fixation duration, total fixation duration, and total visit duration were recorded. Compared to healthy controls, women with breast cancer exhibited significantly longer mean fixation duration [t = 4.54, <i>p</i> < 0.00]; mean total fixation duration [t = 2.41, <i>p</i> < 0.02]; mean total visitation duration [t = 2.05, <i>p</i> < 0.05]; and total test time [t = 2.32, <i>p</i> < 0.03]. Additionally, positive correlations were observed between the number of chemotherapy cycles and the eye tracking parameters. These results suggest the possibility of slower information processing in women experiencing acute effects of chemotherapy. However, further studies are needed to clarify this relationship.

Aaron W. Beger, Sarah Hannan, Riya Patel et al.

Virtual escape rooms (ERs) require learners to solve puzzles and answer riddles while trying to “escape” a digital room. Although the educational merit of such gamified learning activities continues to be realized, guides on the development of ERs are lacking, as well as student perceptions on how, if, and where they should be integrated into medical curricula. Therefore, the aim of this study was to describe the experiences of building anatomy-themed virtual ERs of differing formats at two separate institutions, Queen’s University Belfast (QUB) and Edward Via College of Osteopathic Medicine (VCOM), focusing on abdominal and upper limb anatomy, respectively. Google Workspace applications served as the primary platform. Three-dimensional (3-D) models were built with photogrammetry techniques or Virtual Human Dissector software (www.toltech.net) and integrated into the ER. Of 69 students and staff invited at QUB, 9 (13%) participated in the in-person virtual ER in teams of two or three (7 medical students, 2 anatomy instructors). Of 27 VCOM medical students invited, 8 (30%) agreed to participate and individually completed VCOM’s virtual ER remotely. Anonymous surveys and a focus group revealed the ERs to be enjoyable and engaging and that they encouraged participants to think about material in a new way while helping them to identify knowledge gaps. Strengths and weaknesses of different designs (linear vs. nonlinear), delivery methods (in person vs. remote), and grouping of participants (team based vs. individual) were realized and discussed, revealing opportunities for optimizing the experience. Future studies would benefit from increasing sample sizes to assess the learning gain of such activities.NEW & NOTEWORTHY Virtual escape rooms (ERs) offer an innovative way to expose students to educational material in a creative, engaging way, particularly when they incorporate three-dimensional (3-D) models. Activities can be readily built with Google Workspace. Offering this activity to teams in a physical setting may promote collaboration and maximize the educational utility, whereas having learners complete it remotely on an individual basis may be more convenient, allowing them to fit it in their study schedule at their own convenience.

T. Rohlfing, N. Zahr, E. Sullivan et al.

Kyle Lucke, Aleksandar Vakanski, Min Xian

In recent years, convolutional neural networks for semantic segmentation of breast ultrasound (BUS) images have shown great success; however, two major challenges still exist. 1) Most current approaches inherently lack the ability to utilize tissue anatomy, resulting in misclassified image regions. 2) They struggle to produce accurate boundaries due to the repeated down-sampling operations. To address these issues, we propose a novel breast anatomy-aware network for capturing fine image details and a new smoothness term that encodes breast anatomy. It incorporates context information across multiple spatial scales to generate more accurate semantic boundaries. Extensive experiments are conducted to compare the proposed method and eight state-of-the-art approaches using a BUS dataset with 325 images. The results demonstrate the proposed method significantly improves the segmentation of the muscle, mammary, and tumor classes and produces more accurate fine details of tissue boundaries.