Hasil untuk "Human anatomy"

Goran Muric, Steven Minton

Automated decision systems increasingly rely on human oversight to ensure accuracy in uncertain cases. This paper presents a practical framework for optimizing such human-in-the-loop classification systems using a double-threshold policy. Conventional classifiers usually produce a confidence score and apply a single cutoff, but our approach uses two thresholds (a lower and an upper) to automatically accept or reject high-confidence cases while routing ambiguous instances to human reviewers. We formulate this problem as an optimization task that balances system accuracy against the cost of human review. Through analytical derivations and Monte Carlo simulations, we show how different confidence score distributions impact the efficiency of human intervention and reveal regions of diminishing returns, where additional review yields minimal benefit. The framework provides a general, reproducible method for improving reliability in any decision pipeline requiring selective human validation, including applications in entity resolution, fraud detection, medical triage, and content moderation.

Lucia Borrego, Vajira Thambawita, Marco Ciuffreda et al.

The scarcity of high-quality segmentation masks remains a major bottleneck for medical image analysis, particularly in non-contrast CT (NCCT) neuroimaging, where manual annotation is costly and variable. To address this limitation, we propose an anatomy-preserving generative framework for the unconditional synthesis of multi-class brain segmentation masks, including ischemic infarcts. The proposed approach combines a variational autoencoder trained exclusively on segmentation masks to learn an anatomical latent representation, with a diffusion model operating in this latent space to generate new samples from pure noise. At inference, synthetic masks are obtained by decoding denoised latent vectors through the frozen VAE decoder, with optional coarse control over lesion presence via a binary prompt. Qualitative results show that the generated masks preserve global brain anatomy, discrete tissue semantics, and realistic variability, while avoiding the structural artifacts commonly observed in pixel-space generative models. Overall, the proposed framework offers a simple and scalable solution for anatomy-aware mask generation in data-scarce medical imaging scenarios.

Weixuan Chen, Marcin Kuniewicz, Marcin Kuniewicz et al.

IntroductionHigh-resolution digitized cardiac anatomical data sets are in huge demand in clinical, basic research and computational settings. They can be leveraged to evaluate intricate anatomical and structural changes in disease pathology, such as myocardial infarction (MI), which is one of the most common causes of heart failure and death. Advancements in high-resolution imaging and anatomical techniques in this field and our laboratory have led to vast improvements in understanding cardiovascular anatomy, especially the cardiac conduction system (CCS) responsible for the electricity of the heart, in healthy/aged/obese post-mortem human hearts. However, the digitized anatomy of the electrical system of the heart within MI hearts remains unexplored.MethodsFive post-mortem non-MI and MI human hearts were obtained by the Visible Heart® Laboratories via LifeSource, Minneapolis, MN, United States (with appropriate ethics and consent): specimens were then transported to Manchester University with an material transfer agreement in place and stored under the HTA 2004, UK. After performing contrast-enhanced micro-CT, a visualization tool (namely Amira) was used for 3D high-resolution anatomical visualizations and reconstruction. Various cardiovascular structures were segmented based on the attenuation difference of micro-CT scans and tissue traceability. The relationship between the CCS and surrounding tissues in MI and non-MI human hearts was obtained. 3D anatomical models were further explored for their use in computational simulations, 3D printing and mix/virtual reality visualization.Results3D segmented cardiovascular structures in the MI hearts elicited diverse macro-/micro- anatomical changes. The key findings are thickened valve leaflets, formation of new coronary arteries, increased or reduced thicknesses of pectinate and papillary muscles and Purkinje fibers, thinner left bundle branches, sinoatrial nodal atrophy, atrioventricular conduction axis fragmentation, and increased epicardial fat in some hearts. The propagation of the excitation impulses can be simulated, and 3D printing can be utilized from the reconstructed and segmented structures.DiscussionHigh-resolution digitized cardiac anatomical datasets offer exciting new tools for medical education, clinical applications, and computational simulation.

Sean Koon

Amidst the race to create more intelligent machines there is a risk that we will rely on AI in ways that reduce our own agency as humans. To reduce this risk, we could aim to create tools that prioritize and enhance the human role in human-AI interactions. This paper outlines a human-centered augmented reasoning paradigm by 1. Articulating fundamental principles for augmented reasoning tools, emphasizing their ergonomic, pre-conclusive, directable, exploratory, enhancing, and integrated nature; 2. Proposing a 'many tasks, many tools' approach to ensuring human influence and control, and 3. Offering examples of interaction modes that can serve as bridges between human reasoning and AI algorithms.

Sean Koon

The idea of augmented or hybrid intelligence offers a compelling vision for combining human and AI capabilities, especially in tasks where human wisdom, expertise, or common sense are essential. Unfortunately, human reasoning can be flawed and shortsighted, resulting in adverse individual impacts or even long-term societal consequences. While strong efforts are being made to develop and optimize the AI aspect of hybrid reasoning, the real urgency lies in fostering wiser and more intelligent human participation. Tools that enhance critical thinking, ingenuity, expertise, and even wisdom could be essential in addressing the challenges of our emerging future. This paper proposes the development of generative AI-based tools that enhance both the human ability to reflect upon a problem as well as the ability to explore the technical aspects of it. A high-level model is also described for integrating AI and human capabilities in a way that centralizes human participation and control.

Riccardo Loconte, Merylin Monaro, Pietro Pietrini et al.

Automated verbal deception detection using methods from Artificial Intelligence (AI) has been shown to outperform humans in disentangling lies from truths. Research suggests that transparency and interpretability of computational methods tend to increase human acceptance of using AI to support decisions. However, the extent to which humans accept AI judgments for deception detection remains unclear. We experimentally examined how an AI model's accuracy (i.e., its overall performance in deception detection) and confidence (i.e., the model's uncertainty in single-statements predictions) influence human adoption of the model's judgments. Participants (n=373) were presented with veracity judgments of an AI model with high or low overall accuracy and various degrees of prediction confidence. The results showed that humans followed predictions from a highly accurate model more than from a less accurate one. Interestingly, the more confident the model, the more people deviated from it, especially if the model predicted deception. We also found that human interaction with algorithmic predictions either worsened the machine's performance or was ineffective. While this human aversion to accept highly confident algorithmic predictions was partly explained by participants' tendency to overestimate humans' deception detection abilities, we also discuss how truth-default theory and the social costs of accusing someone of lying help explain the findings.

Zora Zhiruo Wang, Yijia Shao, Omar Shaikh et al.

AI agents are continually optimized for tasks related to human work, such as software engineering and professional writing, signaling a pressing trend with significant impacts on the human workforce. However, these agent developments have often not been grounded in a clear understanding of how humans execute work, to reveal what expertise agents possess and the roles they can play in diverse workflows. In this work, we study how agents do human work by presenting the first direct comparison of human and agent workers across multiple essential work-related skills: data analysis, engineering, computation, writing, and design. To better understand and compare heterogeneous computer-use activities of workers, we introduce a scalable toolkit to induce interpretable, structured workflows from either human or agent computer-use activities. Using such induced workflows, we compare how humans and agents perform the same tasks and find that: (1) While agents exhibit promise in their alignment to human workflows, they take an overwhelmingly programmatic approach across all work domains, even for open-ended, visually dependent tasks like design, creating a contrast with the UI-centric methods typically used by humans. (2) Agents produce work of inferior quality, yet often mask their deficiencies via data fabrication and misuse of advanced tools. (3) Nonetheless, agents deliver results 88.3% faster and cost 90.4-96.2% less than humans, highlighting the potential for enabling efficient collaboration by delegating easily programmable tasks to agents.

Riccardo Tenderini, Luca Pegolotti, Fanwei Kong et al.

This work introduces AD-SVFD, a deep learning model for the deformable registration of vascular shapes to a pre-defined reference and for the generation of synthetic anatomies. AD-SVFD operates by representing each geometry as a weighted point cloud and models ambient space deformations as solutions at unit time of ODEs, whose time-independent right-hand sides are expressed through artificial neural networks. The model parameters are optimized by minimizing the Chamfer Distance between the deformed and reference point clouds, while backward integration of the ODE defines the inverse transformation. A distinctive feature of AD-SVFD is its auto-decoder structure, that enables generalization across shape cohorts and favors efficient weight sharing. In particular, each anatomy is associated with a low-dimensional code that acts as a self-conditioning field and that is jointly optimized with the network parameters during training. At inference, only the latent codes are fine-tuned, substantially reducing computational overheads. Furthermore, the use of implicit shape representations enables generative applications: new anatomies can be synthesized by suitably sampling from the latent space and applying the corresponding inverse transformations to the reference geometry. Numerical experiments, conducted on healthy aortic anatomies, showcase the high-quality results of AD-SVFD, which yields extremely accurate approximations at competitive computational costs.

Felix Glawe, Tim Schmeckel, Philipp Brauner et al.

Human autonomy and sense of agency are increasingly recognised as critical for user well-being, motivation, and the ethical deployment of robots in human-robot interaction (HRI). Given the rapid development of artificial intelligence, robot capabilities and their potential to function as colleagues and companions are growing. This systematic literature review synthesises 22 empirical studies selected from an initial pool of 728 articles published between 2011 and 2024. Articles were retrieved from major scientific databases and identified based on empirical focus and conceptual relevance, namely, how to preserve and promote human autonomy and sense of agency in HRI. Derived through thematic synthesis, five clusters of potentially influential factors are revealed: robot adaptiveness, communication style, anthropomorphism, presence of a robot and individual differences. Measured through psychometric scales or the intentional binding paradigm, perceptions of autonomy and agency varied across industrial, educational, healthcare, care, and hospitality settings. The review underscores the theoretical differences between both concepts, but their yet entangled use in HRI. Despite increasing interest, the current body of empirical evidence remains limited and fragmented, underscoring the necessity for standardised definitions, more robust operationalisations, and further exploratory and qualitative research. By identifying existing gaps and highlighting emerging trends, this review contributes to the development of human-centered, autonomy-supportive robot design strategies that uphold ethical and psychological principles, ultimately supporting well-being in human-robot interaction.

Abigail Salas, Mingqian Tan, Sofia Andrienko et al.

Bloom’s Taxonomy is an andragogical tool that classifies educational objectives, learning activities, and assessments into distinct levels of cognitive thinking. Preclinical medical sciences educators aim to promote higher-order thinking in their curricula to help students develop clinical decision-making skills and foster deep learning. However, many courses and curricula remain focused on lower levels of Bloom’s Taxonomy because they are easier to implement and assess. Meanwhile, many service-learning opportunities for medical students focus on developing affective faculties over higher-level cognitive processing of curricular material. We describe a model program in which undergraduate pre-medical education (UPE) and undergraduate medical education (UPE/UME) students at Boston University Charles River Campus (BU CRC) and Boston University Aram V. Chobanian & Edward Avedisian School of Medicine (BU Chobanian & Avedisian SOM) simultaneously engaged in service-learning and the Create level of Bloom’s Taxonomy by developing a supplementary high school (HS) medical science curriculum based on content and instructional models from their preclinical courses. Activities such as mock patient cases, simulated patient interviews, and physical examination training contributed toward a HS curriculum that promotes healthy habits; increases community public health self-efficacy; sparks interest in Science, Technology, Engineering, Math and Medicine (STEMM) concepts; and educates about medical careers through engaging lessons and activities focused on human anatomy and physiology.

Uzma Shahid , Maryam Ahmer, Kaukab Anjum et al.

Objective: To compare the effect of flipped classroom versus traditional classroom on students' academic performance in teaching human anatomy. To assess the perceptions of medical students about flipped classroom and traditional classroom strategies. Study Design: The present study followed quasi-experimental design, including pretest, posttest, and a questionnaire. Place and Duration of Study: The study was carried out in the Department of Anatomy, at Wah Medical College, Pakistan from April 10 , 2023, to June 9 , 2023. Materials and Methods: A total of 143 second year MBBS students were randomly divided into two groups;Group I (n=72) and Group II (n=71). Group I (Experimental group) was exposed to the flipped classroom while Group II (Control group) was taught through the traditional classroom. A Pretest and a posttest were taken at the start and end of the experiment. Perceptions of students regarding flipped classroom and traditional classroom strategies were recorded through a 5-point Likert scale questionnaire. The data was analyzed by SPSS version 23. The p value 0.05 was significant. Results: The mean pretest score was not statistically significant between groups I and II (p>0.05). By the end of the study, the mean posttest score of each group significantly raised as compared to its pretest score (p<0.001). However, Group I achieved a significantly higher posttest score than Group II (p<0.05). Students perceived flipped classroom as more beneficial than traditional classroom (p=0.001) as it enhanced their understanding, memorization, integration, and application of subject knowledge. Moreover, flipped classrooms proved to be more valuable in engaging students and improving their ability to participate in problem-solving activities. Conclusion: Flipped Classroom has proven to be a more effective strategy in teaching human anatomy to medical students compared to traditional classroom method.

Jinkyung Katie Park, Rahul Dev Ellezhuthil, Pamela Wisniewski et al.

Collaborative human-AI annotation is a promising approach for various tasks with large-scale and complex data. Tools and methods to support effective human-AI collaboration for data annotation are an important direction for research. In this paper, we present CHAIRA: a Collaborative Human-AI Risk Annotation tool that enables human and AI agents to collaboratively annotate online incivility. We leveraged Large Language Models (LLMs) to facilitate the interaction between human and AI annotators and examine four different prompting strategies. The developed CHAIRA system combines multiple prompting approaches with human-AI collaboration for online incivility data annotation. We evaluated CHAIRA on 457 user comments with ground truth labels based on the inter-rater agreement between human and AI coders. We found that the most collaborative prompt supported a high level of agreement between a human agent and AI, comparable to that of two human coders. While the AI missed some implicit incivility that human coders easily identified, it also spotted politically nuanced incivility that human coders overlooked. Our study reveals the benefits and challenges of using AI agents for incivility annotation and provides design implications and best practices for human-AI collaboration in subjective data annotation.

Jicong Ao, Yansong Wu, Fan Wu et al.

In this work, we propose an LLM-based BT generation framework to leverage the strengths of both for sequential manipulation planning. To enable human-robot collaborative task planning and enhance intuitive robot programming by nonexperts, the framework takes human instructions to initiate the generation of action sequences and human feedback to refine BT generation in runtime. All presented methods within the framework are tested on a real robotic assembly example, which uses a gear set model from the Siemens Robot Assembly Challenge. We use a single manipulator with a tool-changing mechanism, a common practice in flexible manufacturing, to facilitate robust grasping of a large variety of objects. Experimental results are evaluated regarding success rate, logical coherence, executability, time consumption, and token consumption. To our knowledge, this is the first human-guided LLM-based BT generation framework that unifies various plausible ways of using LLMs to fully generate BTs that are executable on the real testbed and take into account granular knowledge of tool use.

Lingyu Zhang, Zhengran Ji, Boyuan Chen

With the increasing deployment of artificial intelligence (AI) technologies, the potential of humans working with AI agents has been growing at a great speed. Human-AI teaming is an important paradigm for studying various aspects when humans and AI agents work together. The unique aspect of Human-AI teaming research is the need to jointly study humans and AI agents, demanding multidisciplinary research efforts from machine learning to human-computer interaction, robotics, cognitive science, neuroscience, psychology, social science, and complex systems. However, existing platforms for Human-AI teaming research are limited, often supporting oversimplified scenarios and a single task, or specifically focusing on either human-teaming research or multi-agent AI algorithms. We introduce CREW, a platform to facilitate Human-AI teaming research in real-time decision-making scenarios and engage collaborations from multiple scientific disciplines, with a strong emphasis on human involvement. It includes pre-built tasks for cognitive studies and Human-AI teaming with expandable potentials from our modular design. Following conventional cognitive neuroscience research, CREW also supports multimodal human physiological signal recording for behavior analysis. Moreover, CREW benchmarks real-time human-guided reinforcement learning agents using state-of-the-art algorithms and well-tuned baselines. With CREW, we were able to conduct 50 human subject studies within a week to verify the effectiveness of our benchmark.

Kartik Hosanagar, Daehwan Ahn

We examined the effectiveness of various human-AI collaboration designs on creative work. Through a human subjects experiment set in the context of creative writing, we found that while AI assistance improved productivity across all models, collaboration design significantly influenced output quality, user satisfaction, and content characteristics. Models incorporating human creative input delivered higher content interestingness and overall quality as well as greater task performer satisfaction compared to conditions where humans were limited to confirming AI's output. Increased AI involvement encouraged creators to explore beyond personal experience but also led to lower aggregate diversity in stories and genres among participants. However, this effect was mitigated through human participation in early creative tasks. These findings underscore the importance of preserving the human creative role to ensure quality, satisfaction, and creative diversity in human-AI collaboration.

Hao Guo, Bangan Wu, Qi Li et al.

Human trust plays a crucial role in the effectiveness of human-robot collaboration. Despite its significance, the development and maintenance of an optimal trust level are obstructed by the complex nature of influencing factors and their mechanisms. This study investigates the effects of cognitive load on human trust within the context of a hybrid human-robot collaboration task. An experiment is conducted where the humans and the robot, acting as team members, collaboratively construct pyramids with differentiated levels of task complexity. Our findings reveal that cognitive load exerts diverse impacts on human trust in the robot. Notably, there is an increase in human trust under conditions of high cognitive load. Furthermore, the rewards for performance are substantially higher in tasks with high cognitive load compared to those with low cognitive load, and a significant correlation exists between human trust and the failure risk of performance in tasks with low and medium cognitive load. By integrating interdependent task steps, this research emphasizes the unique dynamics of hybrid human-robot collaboration scenarios. The insights gained not only contribute to understanding how cognitive load influences trust but also assist developers in optimizing collaborative target selection and designing more effective human-robot interfaces in such environments.

Dipanjana Chakraborty, Aribam Jaishree Devi

Background: The present study attempted to find out the histological changes of parotid gland in the developing human fetuses. Materials and Methods: Parotid glands from 60 fresh fetuses of gestational weeks ranging from 12weeks to term were studied after staining with Hematoxylene & Eosin, Masson's Trichome stain, Van Gieson's stain and Verhoeff's stains. Results: The gland of the fetus at 12 weeks composed of solid epithelial cords with occasional canalization, surrounded by loose mesenchyme. Canalization of cords was completed at 25 weeks and adult picture attained at 36 weeks. Division of the glandular parenchyma into lobes and lobules by connective tissue septa started at 17 weeks. A well formed capsule was seen around the gland at 20 weeks. A gradual decrease in intra-glandular connective tissue occurred in late gestational age. Variety of cells such as fibroblasts, mesenchymal cells, fibrocytes and lymphocytes were seen. Numerous adipocytes were found within the glandular parenchyma and around the gland. Ductal tree showed gradual differentiation, presence of cilia-like surface projections from epithelia of larger ducts were seen in 14-32 week fetuses. Conclusion: When compared with various authors, some findings of the present study followed the foot-steps of previous workers whereas some were in contrary, the most important difference being the age of full maturation of the glandular architecture. One of the unique finding being the presence of cilia-like surface projections seen in the larger ducts in 14-32 week aged fetuses.

P. Thompson, A. Toga

Jung A Kim

Junyu Yan, Yalan Yang, Xinhao Fan et al.

Abstract Background Circular RNAs (circRNAs) represent a novel class of non‐coding RNAs formed by a covalently closed loop and play crucial roles in many biological processes. Several circRNAs associated with myogenesis have been reported. However, the dynamic expression, function, and mechanism of circRNAs during myogenesis and skeletal muscle development are largely unknown. Methods Strand‐specific RNA‐sequencing (RNA‐seq) and microarray datasets were used to profile the dynamic circRNAome landscape during skeletal muscle development and myogenic differentiation. Bioinformatics analyses were used to characterize the circRNAome and identify candidate circRNAs associated with myogenesis. Bulk and single‐cell RNA‐seq were performed to identify the downstream genes and pathways of circFgfr2. The primary myoblast cells, C2C12 cells, and animal model were used to assess the function and mechanism of circFgfr2 in myogenesis and muscle regeneration in vitro or in vivo by RT‐qPCR, western blotting, dual‐luciferase activity assay, RNA immunoprecipitation, RNA fluorescence in situ hybridization, and chromatin immunoprecipitation. Results We profiled the dynamic circRNAome in pig skeletal muscle across 27 developmental stages and detected 52 918 high‐confidence circRNAs. A total of 2916 of these circRNAs are conserved across human, mouse, and pig, including four circRNAs (circFgfr2, circQrich1, circMettl9, and circCamta1) that were differentially expressed (|log2 fold change| > 1 and adjusted P value < 0.05) in various myogenesis systems. We further focused on a conserved circRNA produced from the fibroblast growth factor receptor 2 (Fgfr2) gene, termed circFgfr2, which was found to inhibit myoblast proliferation and promote differentiation and skeletal muscle regeneration. Mechanistically, circFgfr2 acted as a sponge for miR‐133 to regulate the mitogen‐activated protein kinase kinase kinase 20 (Map3k20) gene and JNK/MAPK pathway. Importantly, transcription factor Kruppel like factor 4 (Klf4), the downstream target of the JNK/MAPK pathway, directly bound to the promoter of circFgfr2 and affected its expression via an miR‐133/Map3k20/JNK/Klf4 auto‐regulatory feedback loop. RNA binding protein G3BP stress granule assembly factor 1 (G3bp1) inhibited the biogenesis of circFgfr2. Conclusions The present study provides a comprehensive circRNA resource for skeletal muscle study. The functional and mechanistic analysis of circFgfr2 uncovered a circRNA‐mediated auto‐regulatory feedback loop regulating myogenesis and muscle regeneration, which provides new insight to further understand the regulatory mechanism of circRNAs.