Hasil untuk "Sports medicine"

Yuta Shimane, Ko Yamamoto

This study proposes a reinforcement learning-based adaptive running motion simulation for a unilateral transtibial amputee with the flexibility of a leaf-spring-type sports prosthesis using hybrid-link system. The design and selection of sports prostheses often rely on trial and error. A comprehensive whole-body dynamics analysis that considers the interaction between human motion and prosthetic deformation could provide valuable insights for user-specific design and selection. The hybrid-link system facilitates whole-body dynamics analysis by incorporating the Piece-wise Constant Strain model to represent the flexible deformation of the prosthesis. Based on this system, the simulation methodology generates whole-body dynamic motions of a unilateral transtibial amputee through a reinforcement learning-based approach, which combines imitation learning from motion capture data with accurate prosthetic dynamics computation. We simulated running motions under different virtual prosthetic stiffness conditions and analyzed the metabolic cost of transport obtained from the simulations, suggesting that variations in stiffness influence running performance. Our findings demonstrate the potential of this approach for simulation and analysis under virtual conditions that differ from real conditions.

Barbara Jelonek, Wiktoria Tłoczek, Daria Twardowska et al.

Background: Atrial fibrillation (AF) is the most common sustained arrhythmia worldwide and a major cause of ischaemic stroke, heart failure, and cardiovascular mortality. Due to its frequently asymptomatic and paroxysmal nature, many cases remain undiagnosed until serious complications occur. This diagnostic challenge is particularly relevant in physically active individuals, as arrhythmias may be triggered or exacerbated during sports and exercise. Traditional monitoring methods have important limitations in sensitivity, duration, and long-term patient acceptability. Aim: This narrative review evaluates the current evidence on fitness trackers and other wearable devices in the prevention, screening, and diagnosis of atrial fibrillation, focusing on clinical performance, patient-centred aspects, limitations, and applications in sports and exercise. Material and Methods A comprehensive narrative review was performed, incorporating the 2024 ESC Guidelines, major clinical trials, meta-analyses, and recent studies on photoplethysmography (PPG) and single-lead ECG technologies across various wearable form factors. Results: Wearable devices, including smartwatches, adhesive patches, and smart textiles, achieve high diagnostic accuracy for AF detection (sensitivity and specificity frequently 90–97%). They improve identification of subclinical and silent AF in high-risk and physically active populations and provide markedly better comfort and adherence during sports and exercise. Key challenges include motion artefacts, false-positive alerts, and the scarcity of large randomised trials with hard clinical outcomes. Conclusions: Wearable technologies have the potential to transform AF care from reactive treatment of complications to proactive, personalised prevention. While already clinically valuable, widespread adoption requires overcoming technical barriers and confirming long-term benefit in robust outcome trials.

Sophia Wesely, Ella Hofer, Robin Curth et al.

Over the past four decades, cheerleading has evolved from a sideline activity at major sporting events into a professional, competitive sport with growing global popularity. Evaluating tumbling elements in cheerleading relies on both objective measures and subjective judgments, such as difficulty and execution quality. However, the complexity of tumbling - encompassing team synchronicity, ground interactions, choreography, and artistic expression - makes objective assessment challenging. Artificial intelligence (AI) has revolutionized various scientific fields and industries through precise data-driven analyses, yet their application in acrobatic sports remains limited despite significant potential for enhancing performance evaluation and coaching. This study investigates the feasibility of using an AI-based approach with data from a single inertial measurement unit to accurately identify and objectively assess tumbling elements in standard cheerleading routines. A sample of 16 participants (13 females, 3 males) from a Division I collegiate cheerleading team wore a single inertial measurement unit at the dorsal pelvis. Over a 4-week seasonal preparation period, 1102 tumbling elements were recorded during regular practice sessions. Using triaxial accelerations and rotational speeds, various ML algorithms were employed to classify and evaluate the execution of tumbling manoeuvres. Results indicate that certain machine learning models can effectively identify different tumbling elements despite inter-individual variability and data noise, achieving high accuracy. These findings demonstrate the significant potential for integrating AI-driven assessments into cheerleading and other acrobatic sports, providing objective metrics that complement traditional judging methods.

Jordan Chipka, Chris Moyer, Clay Troyer et al.

The rapid growth of big data and advancements in computational techniques have significantly transformed sports analytics. However, the diverse range of data sources -- including structured statistics, semi-structured formats like sensor data, and unstructured media such as written articles, audio, and video -- creates substantial challenges in extracting actionable insights. These various formats, often referred to as multimodal data, require integration to fully leverage their potential. Conventional systems, which typically prioritize structured data, face limitations when processing and combining these diverse content types, reducing their effectiveness in real-time sports analysis. To address these challenges, recent research highlights the importance of multimodal data integration for capturing the complexity of real-world sports environments. Building on this foundation, this paper introduces GridMind, a multi-agent framework that unifies structured, semi-structured, and unstructured data through Retrieval-Augmented Generation (RAG) and large language models (LLMs) to facilitate natural language querying of NFL data. This approach aligns with the evolving field of multimodal representation learning, where unified models are increasingly essential for real-time, cross-modal interactions. GridMind's distributed architecture includes specialized agents that autonomously manage each stage of a prompt -- from interpretation and data retrieval to response synthesis. This modular design enables flexible, scalable handling of multimodal data, allowing users to pose complex, context-rich questions and receive comprehensive, intuitive responses via a conversational interface.

Abdelwahid Aboulfaraj, Fatiha Laziri, Salah Eddine Haddou et al.

Background: This study aimed to compare the effects of linear sprint training with changes of direction (LSCD) versus small-sided games (SSSG) on physical performance, agility, and soccer-specific skills in young elite female players. Methods: In a randomized crossover study, 27 players aged 15 to 17 were divided into two groups (G1 = 14, G2 = 13). After a two-week baseline period, each group completed a four-week training mesocycle (three sessions per week) consisting of either LSCD or SSG. After a two-week washout period, participants switched interventions and completed the alternate four-week mesocycle. Performance assessments were conducted before and after each mesocycle to evaluate training effects. Results: Both types of training improved physical performance, with different magnitudes. LSCD induced larger gains in sprint speed (5, 10, 20 m; <i>p</i> < 0.05), agility without the ball (<i>t</i>-test; <i>p</i> = 0.05), and explosive power (countermovement jump, repeated jumps over 15 s; <i>p</i> = 0.02 and <i>p</i> = 0.004). In contrast, SSSG led to larger improvements in aerobic endurance (Yo-Yo IR1 test; <i>p</i> = 0.03) and agility with the ball (<i>t</i>-test with ball; <i>p</i> = 0.05). No transfer effect between cycles was observed. Conclusions: In young elite female players, LSCD training was more effective in improving speed, agility, and power, while SSSG was more effective for aerobic endurance and ball agility.

Mingde Cao, Qianwen Wang, Xueyou Zhang et al.

Background: Large Language Models (LLMs) have gained much attention and, in part, have replaced common search engines as a popular channel for obtaining information due to their contextually relevant responses. Osteoarthritis (OA) is a common topic in skeletal muscle disorders, and patients often seek information about it online. Our study evaluated the ability of 3 LLMs (ChatGPT-3.5, ChatGPT-4.0, and Perplexity) to accurately answer common OA-related queries. Methods: We defined 6 themes (pathogenesis, risk factors, clinical presentation, diagnosis, treatment and prevention, and prognosis) based on a generalization of 25 frequently asked questions about OA. Three consultant-level orthopedic specialists independently rated the LLMs' replies on a 4-point accuracy scale. The final ratings for each response were determined using a majority consensus approach. Responses classified as “satisfactory” were evaluated for comprehensiveness on a 5-point scale. Results: ChatGPT-4.0 demonstrated superior accuracy, with 64% of responses rated as “excellent”, compared to 40% for ChatGPT-3.5 and 28% for Perplexity (Pearson's χ2 test with Fisher's exact test, all p < 0.001). All 3 LLM-chatbots had high mean comprehensiveness ratings (Perplexity = 3.88; ChatGPT-4.0 = 4.56; ChatGPT-3.5 = 3.96, out of a maximum score of 5). The LLM-chatbots performed reliably across domains, except for “treatment and prevention” However, ChatGPT-4.0 still outperformed ChatGPT-3.5 and Perplexity, garnering 53.8% “excellent” ratings (Pearson's χ2 test with Fisher's exact test, all p < 0.001). Conclusion: Our findings underscore the potential of LLMs, specifically ChatGPT-4.0 and Perplexity, to deliver accurate and thorough responses to OA-related queries. Targeted correction of specific misconceptions to improve the accuracy of LLMs remains crucial.

Patrick Waldron, D.O., Alvarho Guzman, M.D., Lucas Voyvodic, M.D. et al.

Posterolateral rotatory instability of the elbow results from injury to the lateral collateral ligament complex, most often involving the lateral ulnar collateral ligament (LUCL). In the subacute or chronic setting, LUCL reconstruction is the technique of choice, although traditional graft-based techniques can be limited by morbidity and delayed recovery. Internal bracing offers improved early stability and graft protection. This Technical Note describes a reproducible technique for LUCL and radial collateral ligament reconstruction using semitendinosus allograft with suture tape augmentation. The graft is anatomically positioned to reconstruct both the LUCL and radial collateral ligament origins, restoring lateral collateral ligament complex integrity. This method is designed for subacute or chronic posterolateral rotatory instability and aims to minimize graft elongation and support early mobilization.

Hanguang Xiao, Feizhong Zhou, Xingyue Liu et al.

Since the release of ChatGPT and GPT-4, large language models (LLMs) and multimodal large language models (MLLMs) have attracted widespread attention for their exceptional capabilities in understanding, reasoning, and generation, introducing transformative paradigms for integrating artificial intelligence into medicine. This survey provides a comprehensive overview of the development, principles, application scenarios, challenges, and future directions of LLMs and MLLMs in medicine. Specifically, it begins by examining the paradigm shift, tracing the transition from traditional models to LLMs and MLLMs, and highlighting the unique advantages of these LLMs and MLLMs in medical applications. Next, the survey reviews existing medical LLMs and MLLMs, providing detailed guidance on their construction and evaluation in a clear and systematic manner. Subsequently, to underscore the substantial value of LLMs and MLLMs in healthcare, the survey explores five promising applications in the field. Finally, the survey addresses the challenges confronting medical LLMs and MLLMs and proposes practical strategies and future directions for their integration into medicine. In summary, this survey offers a comprehensive analysis of the technical methodologies and practical clinical applications of medical LLMs and MLLMs, with the goal of bridging the gap between these advanced technologies and clinical practice, thereby fostering the evolution of the next generation of intelligent healthcare systems.

Wasif Khan, Seowung Leem, Kyle B. See et al.

Foundation models (FMs) are large-scale deep learning models trained on massive datasets, often using self-supervised learning techniques. These models serve as a versatile base for a wide range of downstream tasks, including those in medicine and healthcare. FMs have demonstrated remarkable success across multiple healthcare domains. However, existing surveys in this field do not comprehensively cover all areas where FMs have made significant strides. In this survey, we present a comprehensive review of FMs in medicine, focusing on their evolution, learning strategies, flagship models, applications, and associated challenges. We examine how prominent FMs, such as the BERT and GPT families, are transforming various aspects of healthcare, including clinical large language models, medical image analysis, and omics research. Additionally, we provide a detailed taxonomy of FM-enabled healthcare applications, spanning clinical natural language processing, medical computer vision, graph learning, and other biology- and omics- related tasks. Despite the transformative potentials of FMs, they also pose unique challenges. This survey delves into these challenges and highlights open research questions and lessons learned to guide researchers and practitioners. Our goal is to provide valuable insights into the capabilities of FMs in health, facilitating responsible deployment and mitigating associated risks.

Juan Soto, Ramón Carmenaty, Miguel Lastra et al.

Customer segmentation is a fundamental process to develop effective marketing strategies, personalize customer experience and boost their retention and loyalty. This problem has been widely addressed in the scientific literature, yet no definitive solution for every case is available. A specific case study characterized by several individualizing features is thoroughly analyzed and discussed in this paper. Because of the case properties a robust and innovative approach to both data handling and analytical processes is required. The study led to a sound proposal for customer segmentation. The highlights of the proposal include a convenient data partition to decompose the problem, an adaptive distance function definition and its optimization through genetic algorithms. These comprehensive data handling strategies not only enhance the dataset reliability for segmentation analysis but also support the operational efficiency and marketing strategies of sports centers, ultimately improving the customer experience.

Khaled Saab, Tao Tu, Wei-Hung Weng et al.

Excellence in a wide variety of medical applications poses considerable challenges for AI, requiring advanced reasoning, access to up-to-date medical knowledge and understanding of complex multimodal data. Gemini models, with strong general capabilities in multimodal and long-context reasoning, offer exciting possibilities in medicine. Building on these core strengths of Gemini, we introduce Med-Gemini, a family of highly capable multimodal models that are specialized in medicine with the ability to seamlessly use web search, and that can be efficiently tailored to novel modalities using custom encoders. We evaluate Med-Gemini on 14 medical benchmarks, establishing new state-of-the-art (SoTA) performance on 10 of them, and surpass the GPT-4 model family on every benchmark where a direct comparison is viable, often by a wide margin. On the popular MedQA (USMLE) benchmark, our best-performing Med-Gemini model achieves SoTA performance of 91.1% accuracy, using a novel uncertainty-guided search strategy. On 7 multimodal benchmarks including NEJM Image Challenges and MMMU (health & medicine), Med-Gemini improves over GPT-4V by an average relative margin of 44.5%. We demonstrate the effectiveness of Med-Gemini's long-context capabilities through SoTA performance on a needle-in-a-haystack retrieval task from long de-identified health records and medical video question answering, surpassing prior bespoke methods using only in-context learning. Finally, Med-Gemini's performance suggests real-world utility by surpassing human experts on tasks such as medical text summarization, alongside demonstrations of promising potential for multimodal medical dialogue, medical research and education. Taken together, our results offer compelling evidence for Med-Gemini's potential, although further rigorous evaluation will be crucial before real-world deployment in this safety-critical domain.

R. Bonetti, B. Rod, D. Hauw

Over the last decade, there has been a significant increase in the development of mobile applications to deliver various services in sports, including psychological skills training (PST) for athletes. While there are numerous PST-related apps available, little attention has been given to their objective quality. This study aimed to assess the current offerings of PST apps in sports, rate their quality, and provide recommendations for future app development. A scoping review of PST-related apps available on the Apple App Store was conducted, resulting in the retention of 19 apps. The apps used different media types to develop the PST. Of the 19 apps, videos were used by 8 (42%), audios by 7 (37%), articles by 3 (16%), assessment by 4 (21%), ebook by 1 (5%), and both cognitive tasks and personalized journals by 2 (10%). Overall, the app quality measured through the Mobile App Rating Scale (MARS) failed to meet acceptable standards, with a mean rating of 2.78 and only 6 of the apps receiving a score that met the acceptable standards. The findings highlight the need for improvement in the development of PST apps to enhance their quality and usability.

Usman Ali, Sahil Ranmbail, Muhammad Nadeem et al.

Extracting medication names from handwritten doctor prescriptions is challenging due to the wide variability in handwriting styles and prescription formats. This paper presents a robust method for extracting medicine names using a combination of Mask R-CNN and Transformer-based Optical Character Recognition (TrOCR) with Multi-Head Attention and Positional Embeddings. A novel dataset, featuring diverse handwritten prescriptions from various regions of Pakistan, was utilized to fine-tune the model on different handwriting styles. The Mask R-CNN model segments the prescription images to focus on the medicinal sections, while the TrOCR model, enhanced by Multi-Head Attention and Positional Embeddings, transcribes the isolated text. The transcribed text is then matched against a pre-existing database for accurate identification. The proposed approach achieved a character error rate (CER) of 1.4% on standard benchmarks, highlighting its potential as a reliable and efficient tool for automating medicine name extraction.

Scott Hulm, Ryan G. Timmins, Jack T. Hickey et al.

Abstract The aponeurosis is a large fibrous connective tissue structure within and surrounding skeletal muscle and is a critical component of the muscle–tendon unit (MTU). Due to the lack of consensus on terminology and the heterogeneous nature of the aponeurosis between MTUs, there are several questions that remain unanswered. For example, the aponeurosis is often conflated with the free tendon rather than being considered an independent structure. This has subsequent implications when interpreting data regarding the structure, function, and adaptation of the aponeuroses from these studies. In recent years, a body of work has emerged to suggest that acute injury to the myo-aponeurotic complex may have an impact on return-to-sport timeframes and reinjury rates. Therefore, the purpose of this review is to provide a more detailed understanding of the morphology and mechanical behaviour common to all aponeuroses, as well as the unique characteristics of specific lower-limb aponeuroses that are commonly injured. This review provides the practitioner with a current understanding of the mechanical, material, and adaptive properties of lower limb aponeuroses and suggests directions for future research related to the myo-aponeurotic complex.

Porya Rahmani, Mohammad Karimi Zadeh Ardakani, Seyed Mohammad Hosseini

Introduction: Forward head posture is one of the most common postural abnormalities among students, which affects their postural control (balance). Although there is a trend toward using exergames to improve balance, the effectiveness of exergames specifically designed to improve balance in students with forward head posture is unclear. Therefore, the present study aimed to investigate the effect of exergames on the static balance of children with forward head posture. ‌‌Methods: In this quasi-experimental study carried out using a pre-test, post-test, and a control group design, 30 boys with forward head posture in Takestan City with an age range of 7 to 12 years old were purposively selected and assigned to two groups, namely the Exergames and the Control groups. In the pre-test phase, the participants performed three attempts of the Stork test (static balance). The intervention phase was carried out over eight weeks with two sessions per week,each session lasting 30 minutes, during which the participants performed the relevant exercises. Following the training phase, the post-test phase was conducted, where the participants performed the static balance test as in the pre-test phase. The data were analyzed by univariate analysis of covariance.Results: The results showed that exergames significantly improved the static balance of children with forward head posture (F=22.94, P=0.001).Conclusion: In general, the results of the present study highlight the importance of exergames in the static balance of children with head forward posture, and it is recommended that the benefits of these exercises should be used to improve static balance.

Akihiro Hirata, Yuko Oguma, Takeshi Hashimoto

Although physical activities have many health benefits, adverse events and near misses, such as injuries and falls, can occur during these activities. This study aimed to assess the occurrence of adverse events and near misses during sports activities conducted independently by community residents. A survey questionnaire was sent via the internet to the leaders or directors of sports organizations at six public sports centers or associations. In total, 108 individuals answered the survey, with 60% male and 40% female respondents. Individuals aged 50–69 years accounted for 60% of the total number of respondents. All respondents were asked about their experiences of adverse events and near misses within the past three years: the reports of these incidents were obtained using the recall method. Duplicate adverse events and near misses were identified based on the sports discipline, time of occurrence, and sex and age of person involved to determine if there were duplicate reports. Most of the respondents’ activities as staff were performed once a month, with each activity lasting 1–2 h. Forty-five adverse events were reported, including 26 injuries, 13 falls, and 6 others (such as heat stroke, vertigo, and presyncope). Twenty-four near misses were reported, including 12 near collisions with people or objects, five near falls, and seven other incidents. We found that approximately 30% of the respondents experienced adverse events, suggesting the need for documentation of adverse events, implementation of safety measures, and proper safety education for operating staff.

Milos Petrovic, Dimitrije Cabarkapa, Jelena Aleksic et al.

Objective: The purpose of this study was to investigate differences in countermovement jump (CMJ) force–time metrics between male and female youth basketball players. Methods: Twenty-two female and seventeen male basketball players (ages 12–16) performed CMJs on a portable force plate system (VALD Performance). The data collected were analyzed for differences in force–time characteristics, specifically during the concentric and eccentric phases of the CMJ. Results: The results showed no statistically significant differences in anthropometric characteristics between the sexes. However, male athletes demonstrated better performance in several force–time metrics during the concentric phase of the CMJ, including concentric impulse, peak velocity, and mean power, ultimately leading to higher vertical jump heights. Sex-specific differences in the eccentric phase were less pronounced, though males exhibited greater relative eccentric mean power. Conclusions: The findings suggest that male players tend to display greater force and power-producing capabilities during the propulsive (concentric) phase of the CMJ. These differences highlight the importance of tailoring training programs to address specific needs, particularly focusing on enhancing concentric force and power production in female basketball players.

Haider Sultan, Hafiza Farwa Mahmood, Noor Fatima et al.

Like other fields of Traditional Medicines, Unani Medicines have been found as an effective medical practice for ages. It is still widely used in the subcontinent, particularly in Pakistan and India. However, Unani Medicines Practitioners are lacking modern IT applications in their everyday clinical practices. An Online Clinical Decision Support System may address this challenge to assist apprentice Unani Medicines practitioners in their diagnostic processes. The proposed system provides a web-based interface to enter the patient's symptoms, which are then automatically analyzed by our system to generate a list of probable diseases. The system allows practitioners to choose the most likely disease and inform patients about the associated treatment options remotely. The system consists of three modules: an Online Clinical Decision Support System, an Artificial Intelligence Inference Engine, and a comprehensive Unani Medicines Database. The system employs advanced AI techniques such as Decision Trees, Deep Learning, and Natural Language Processing. For system development, the project team used a technology stack that includes React, FastAPI, and MySQL. Data and functionality of the application is exposed using APIs for integration and extension with similar domain applications. The novelty of the project is that it addresses the challenge of diagnosing diseases accurately and efficiently in the context of Unani Medicines principles. By leveraging the power of technology, the proposed Clinical Decision Support System has the potential to ease access to healthcare services and information, reduce cost, boost practitioner and patient satisfaction, improve speed and accuracy of the diagnostic process, and provide effective treatments remotely. The application will be useful for Unani Medicines Practitioners, Patients, Government Drug Regulators, Software Developers, and Medical Researchers.

Dongyeop Jang, Tae-Rim Yun, Choong-Yeol Lee et al.

Traditional Korean medicine (TKM) emphasizes individualized diagnosis and treatment. This uniqueness makes AI modeling difficult due to limited data and implicit processes. Large language models (LLMs) have demonstrated impressive medical inference, even without advanced training in medical texts. This study assessed the capabilities of GPT-4 in TKM, using the Korean National Licensing Examination for Korean Medicine Doctors (K-NLEKMD) as a benchmark. The K-NLEKMD, administered by a national organization, encompasses 12 major subjects in TKM. We optimized prompts with Chinese-term annotation, English translation for questions and instruction, exam-optimized instruction, and self-consistency. GPT-4 with optimized prompts achieved 66.18% accuracy, surpassing both the examination's average pass mark of 60% and the 40% minimum for each subject. The gradual introduction of language-related prompts and prompting techniques enhanced the accuracy from 51.82% to its maximum accuracy. GPT-4 showed low accuracy in subjects including public health & medicine-related law, internal medicine (2) which are localized in Korea and TKM. The model's accuracy was lower for questions requiring TKM-specialized knowledge. It exhibited higher accuracy in diagnosis-based and recall-based questions than in intervention-based questions. A positive correlation was observed between the consistency and accuracy of GPT-4's responses. This study unveils both the potential and challenges of applying LLMs to TKM. These findings underline the potential of LLMs like GPT-4 in culturally adapted medicine, especially TKM, for tasks such as clinical assistance, medical education, and research. But they also point towards the necessity for the development of methods to mitigate cultural bias inherent in large language models and validate their efficacy in real-world clinical settings.

Akriti Upreti, Kartavya Kothari, Utkarsh Thukral et al.

Dream11 takes pride in being a unique platform that enables over 190 million fantasy sports users to demonstrate their skills and connect deeper with their favorite sports. While managing such a scale, one issue we are faced with is duplicate/multiple account creation in the system. This is done by some users with the intent of abusing the platform, typically for bonus offers. The challenge is to detect these multiple accounts before it is too late. We propose a graph-based solution to solve this problem in which we first predict edges/associations between users. Using the edge information we highlight clusters of colluding multiple accounts. In this paper, we talk about our distributed ML system which is deployed to serve and support the inferences from our detection models. The challenge is to do this in real-time in order to take corrective actions. A core part of this setup also involves human-in-the-loop components for validation, feedback, and ground-truth labeling.