Poor reporting of medical and healthcare systematic reviews is a problem from which the sports and exercise medicine, musculoskeletal rehabilitation, and sports science fields are not immune. Transparent, accurate and comprehensive systematic review reporting helps researchers replicate methods, readers understand what was done and why, and clinicians and policy-makers implement results in practice. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Statement and its accompanying Explanation and Elaboration document provide general reporting examples for systematic reviews of healthcare interventions. However, implementation guidance for sport and exercise medicine, musculoskeletal rehabilitation, and sports science does not exist. The Prisma in Exercise, Rehabilitation, Sport medicine and SporTs science (PERSiST) guidance attempts to address this problem. Nineteen content experts collaborated with three methods experts to identify examples of exemplary reporting in systematic reviews in sport and exercise medicine (including physical activity), musculoskeletal rehabilitation (including physiotherapy), and sports science, for each of the PRISMA 2020 Statement items. PERSiST aims to help: (1) systematic reviewers improve the transparency and reporting of systematic reviews and (2) journal editors and peer reviewers make informed decisions about systematic review reporting quality.
Sports have long attracted broad attention as they push the limits of human physical and cognitive capabilities. Amid growing interest in spatial intelligence for vision-language models (VLMs), sports provide a natural testbed for understanding high-intensity human motion and dynamic object interactions. To this end, we present CourtSI, the first large-scale spatial intelligence dataset tailored to sports scenarios. CourtSI contains over 1M QA pairs, organized under a holistic taxonomy that systematically covers spatial counting, distance measurement, localization, and relational reasoning, across representative net sports including badminton, tennis, and table tennis. Leveraging well-defined court geometry as metric anchors, we develop a semi-automatic data engine to reconstruct sports scenes, enabling scalable curation of CourtSI. In addition, we introduce CourtSI-Bench, a high-quality evaluation benchmark comprising 3,686 QA pairs with rigorous human verification. We evaluate 25 proprietary and open-source VLMs on CourtSI-Bench, revealing a remaining human-AI performance gap and limited generalization from existing spatial intelligence benchmarks. These findings indicate that sports scenarios expose limitations in spatial intelligence capabilities captured by existing benchmarks. Further, fine-tuning Qwen3-VL-8B on CourtSI improves accuracy on CourtSI-Bench by 23.5 percentage points. The adapted model also generalizes effectively to CourtSI-Ext, an evaluation set built on a similar but unseen sport, and demonstrates enhanced spatial-aware commentary generation. Together, these findings demonstrate that CourtSI provides a scalable pathway toward advancing spatial intelligence of VLMs in sports.
Floriane Magera, Thomas Hoyoux, Martin Castin
et al.
Single-frame sports field registration often serves as the foundation for extracting 3D information from broadcast videos, enabling applications related to sports analytics, refereeing, or fan engagement. As sports fields have rigorous specifications in terms of shape and dimensions of their line, circle and point components, sports field markings are commonly used as calibration targets for this task. However, because of the sparse and uneven distribution of field markings, close-up camera views around central areas of the field often depict only line and circle markings. On these views, sports field registration is challenging for the vast majority of existing methods, as they focus on leveraging line field markings and their intersections. It is indeed a challenge to include circle correspondences in a set of linear equations. In this work, we propose a novel method to derive a set of points and lines from circle correspondences, enabling the exploitation of circle correspondences for both sports field registration and image annotation. In our experiments, we illustrate the benefits of our bottom-up geometric method against top-performing detectors and show that our method successfully complements them, enabling sports field registration in difficult scenarios.
Advanced analytics have transformed how sports teams operate, particularly in episodic sports like baseball. Their impact on continuous invasion sports, such as soccer and ice hockey, has been limited due to increased game complexity and restricted access to high-resolution game tracking data. In this demo, we present a method to collect and utilize simulated soccer tracking data from the Google Research Football environment to support the development of models designed for continuous tracking data. The data is stored in a schema that is representative of real tracking data and we provide processes that extract high-level features and events. We include examples of established tracking data models to showcase the efficacy of the simulated data. We address the scarcity of publicly available tracking data, providing support for research at the intersection of artificial intelligence and sports analytics.
Sauptik Dhar, Nicholas Buoncristiani, Joe Anakata
et al.
The advent of large (visual) language models (LLM / LVLM) have led to a deluge of automated human-like systems in several domains including social media content generation, search and recommendation, healthcare prognosis, AI assistants for cognitive tasks etc. Although these systems have been successfully integrated in production; very little focus has been placed on sports, particularly accurate identification and natural language description of the game play. Most existing LLM/LVLMs can explain generic sports activities, but lack sufficient domain-centric sports' jargon to create natural (human-like) descriptions. This work highlights the limitations of existing SoTA LLM/LVLMs for generating production-grade sports captions from images in a desired stylized format, and proposes a two-level fine-tuned LVLM pipeline to address that. The proposed pipeline yields an improvement > 8-10% in the F1, and > 2-10% in BERT score compared to alternative approaches. In addition, it has a small runtime memory footprint and fast execution time. During Super Bowl LIX the pipeline proved its practical application for live professional sports journalism; generating highly accurate and stylized captions at the rate of 6 images per 3-5 seconds for over 1000 images during the game play.
Sai Varun Kodathala, Yashwanth Reddy Vutukoori, Rakesh Vunnam
This paper addresses the challenge of automated sports video analysis, which has traditionally been limited by computationally intensive models requiring server-side processing and lacking fine-grained understanding of athletic movements. Current approaches struggle to capture the nuanced biomechanical transitions essential for meaningful sports analysis, often missing critical phases like preparation, execution, and follow-through that occur within seconds. To address these limitations, we introduce SV3.3B, a lightweight 3.3B parameter video understanding model that combines novel temporal motion difference sampling with self-supervised learning for efficient on-device deployment. Our approach employs a DWT-VGG16-LDA based keyframe extraction mechanism that intelligently identifies the 16 most representative frames from sports sequences, followed by a V-DWT-JEPA2 encoder pretrained through mask-denoising objectives and an LLM decoder fine-tuned for sports action description generation. Evaluated on a subset of the NSVA basketball dataset, SV3.3B achieves superior performance across both traditional text generation metrics and sports-specific evaluation criteria, outperforming larger closed-source models including GPT-4o variants while maintaining significantly lower computational requirements. Our model demonstrates exceptional capability in generating technically detailed and analytically rich sports descriptions, achieving 29.2% improvement over GPT-4o in ground truth validation metrics, with substantial improvements in information density, action complexity, and measurement precision metrics essential for comprehensive athletic analysis. Model Available at https://huggingface.co/sportsvision/SV3.3B.
Kornelia Kaźmierkiewicz, Martyna Chojnacka, Marta Ewelina Lis
et al.
Introduction and aim of the study: Neurodegenerative diseases, including Alzheimer's disease, represent a significant health challenge. Therapies are being sought that could delay the development of these diseases and also mitigate their course. Fulvic acid, which is an organic humic compound with antioxidant and anti-inflammatory properties, has attracted increasing interest in the context of treating neurodegenerative diseases. Our study aims to evaluate the effects of fulvic acid on neurodegenerative diseases, mainly Alzheimer's disease, and to determine its therapeutic potential.
Materials and methods: The paper is based on an analysis of studies available in databases such as PubMed, Google Scholar, ResearchGate, and other scientific databases. Clinical trials, preclinical studies, and review papers on the use of fulvic acid in the context of Alzheimer's disease were searched.
Conclusions: Fulvic acid, due to its anti-inflammatory and neuroprotective abilities, shows promising potential in the treatment of Alzheimer's disease, especially in terms of slowing down the loss of cognitive function and protecting against neurodegeneration. It is advisable to conduct further studies aimed at a more thorough evaluation of the efficacy and safety of fulvic acid in the context of the treatment of Alzheimer's disease and other neurodegenerative diseases.
Floriane Magera, Thomas Hoyoux, Olivier Barnich
et al.
Camera calibration is a crucial component in the realm of sports analytics, as it serves as the foundation to extract 3D information out of the broadcast images. Despite the significance of camera calibration research in sports analytics, progress is impeded by outdated benchmarking criteria. Indeed, the annotation data and evaluation metrics provided by most currently available benchmarks strongly favor and incite the development of sports field registration methods, i.e. methods estimating homographies that map the sports field plane to the image plane. However, such homography-based methods are doomed to overlook the broader capabilities of camera calibration in bridging the 3D world to the image. In particular, real-world non-planar sports field elements (such as goals, corner flags, baskets, ...) and image distortion caused by broadcast camera lenses are out of the scope of sports field registration methods. To overcome these limitations, we designed a new benchmarking protocol, named ProCC, based on two principles: (1) the protocol should be agnostic to the camera model chosen for a camera calibration method, and (2) the protocol should fairly evaluate camera calibration methods using the reprojection of arbitrary yet accurately known 3D objects. Indirectly, we also provide insights into the metric used in SoccerNet-calibration, which solely relies on image annotation data of viewed 3D objects as ground truth, thus implementing our protocol. With experiments on the World Cup 2014, CARWC, and SoccerNet datasets, we show that our benchmarking protocol provides fairer evaluations of camera calibration methods. By defining our requirements for proper benchmarking, we hope to pave the way for a new stage in camera calibration for sports applications with high accuracy standards.
A deep understanding of sports, a field rich in strategic and dynamic content, is crucial for advancing Natural Language Processing (NLP). This holds particular significance in the context of evaluating and advancing Large Language Models (LLMs), given the existing gap in specialized benchmarks. To bridge this gap, we introduce SportQA, a novel benchmark specifically designed for evaluating LLMs in the context of sports understanding. SportQA encompasses over 70,000 multiple-choice questions across three distinct difficulty levels, each targeting different aspects of sports knowledge from basic historical facts to intricate, scenario-based reasoning tasks. We conducted a thorough evaluation of prevalent LLMs, mainly utilizing few-shot learning paradigms supplemented by chain-of-thought (CoT) prompting. Our results reveal that while LLMs exhibit competent performance in basic sports knowledge, they struggle with more complex, scenario-based sports reasoning, lagging behind human expertise. The introduction of SportQA marks a significant step forward in NLP, offering a tool for assessing and enhancing sports understanding in LLMs.
The objective of this work is to analyze the usefulness to transform the information to measure sports performance. This analysis is carried out within the field of basketball due to the existing tradition in this sport in data collection, although it is easily adaptable to any other sport. As a result, a modification of the Performance Index Rating (PIR) is proposed to measure sports performance. The results obtained are illustrated from the statistics of Larry Bird, Earvin Johnson, Michael Jordan and Kobe Bryant throughout their careers and can serve to optimize the process of player renewal/firing/hiring in the design of a roster or to aid decision-making in awarding individual awards as best player of a season.
Umile Giuseppe Longo, Alessandro Mazzola, Stefano Campi
et al.
<i>Background and Objectives:</i> Knee osteoarthritis is a serious burden for modern countries. Timing of surgery and treatment choice are still a matter of controversy in the orthopedic literature. The purpose of this study was to ascertain the incidence and hospitalization trends of high tibial osteotomy in Italy from 2001 to 2016. <i>Materials and Methods:</i> Data are sourced from the National Hospital Discharge Reports (SDO) of the Italian Ministry of Health between 2001 and 2016. <i>Results:</i> A total of 34,402 high tibial osteotomies were performed over the study period in Italy. The cumulative incidence was 3.6 cases per 100,000 residents. The age classes 50–54, 55–59 showed the higher number of procedures. In pediatric patients (0–19 years), high tibial osteotomies are also largely performed. The majority of patients having surgery were men with a M/F ratio of 1.5. The mean age of patients was 44.2 ± 19.2 years. Males were significantly younger than females (43.3 ± 20.7 vs. 45.6 ± 17.7). The average length of hospitalization was 6.1 ± 7.3 days. Over the course of the analysis, a declining trend in hospital stay length was seen. The main primary diagnosis codes were “Varus knee” (736.42 ICD-9-CM code, 33.9%), “Osteoarthrosis, localized, primary, leg region” (715.16 ICD-9-CM code, 9.5%). <i>Conclusions:</i> Over the study period, high tibial osteotomies in Italy almost halved. Varus deformity and knee osteoarthritis are the leading causes requiring high tibial osteotomy. Except for the pediatric setting, results showed that from the 20–24 age class to the 50–54 age class, there was an increasing request for knee osteotomy, whereas in those aged >60 years, the incidence progressively decreased. The evident decline in HTO performed over the years in Italy seems to reflect a minor role for knee osteotomy in the management of knee OA, as it seems to be primarily reserved for younger male patients.
The fascial system builds a three-dimensional continuum of soft, collagen-containing, loose and dense fibrous connective tissue that permeates the body and enables all body systems to operate in an integrated manner. Injuries to the fascial system cause a significant loss of performance in recreational exercise as well as high-performance sports, and could have a potential role in the development and perpetuation of musculoskeletal disorders, including lower back pain. Fascial tissues deserve more detailed attention in the field of sports medicine. A better understanding of their adaptation dynamics to mechanical loading as well as to biochemical conditions promises valuable improvements in terms of injury prevention, athletic performance and sports-related rehabilitation. This consensus statement reflects the state of knowledge regarding the role of fascial tissues in the discipline of sports medicine. It aims to (1) provide an overview of the contemporary state of knowledge regarding the fascial system from the microlevel (molecular and cellular responses) to the macrolevel (mechanical properties), (2) summarise the responses of the fascial system to altered loading (physical exercise), to injury and other physiological challenges including ageing, (3) outline the methods available to study the fascial system, and (4) highlight the contemporary view of interventions that target fascial tissue in sport and exercise medicine. Advancing this field will require a coordinated effort of researchers and clinicians combining mechanobiology, exercise physiology and improved assessment technologies.
David Van Bulck, Dries Goossens, Jan-Patrick Clarner
et al.
Any sports competition needs a timetable, specifying when and where teams meet each other. The recent International Timetabling Competition (ITC2021) on sports timetabling showed that, although it is possible to develop general algorithms, the performance of each algorithm varies considerably over the problem instances. This paper provides an instance space analysis for sports timetabling, resulting in powerful insights into the strengths and weaknesses of eight state-of-the-art algorithms. Based on machine learning techniques, we propose an algorithm selection system that predicts which algorithm is likely to perform best when given the characteristics of a sports timetabling problem instance. Furthermore, we identify which characteristics are important in making that prediction, providing insights in the performance of the algorithms, and suggestions to further improve them. Finally, we assess the empirical hardness of the instances. Our results are based on large computational experiments involving about 50 years of CPU time on more than 500 newly generated problem instances.
This paper draws correlations between several challenges and opportunities within the area of team sports analytics and key research areas within multiagent systems (MAS). We specifically consider invasion games, defined as sports where players invade the opposing team's territory and can interact anywhere on a playing surface such as ice hockey, soccer, and basketball. We argue that MAS is well-equipped to study invasion games and will benefit both MAS and sports analytics fields. Our discussion highlights areas for MAS implementation and further development along two axes: short-term in-game strategy (coaching) and long-term team planning (management).
Dorota Pazik, Katarzyna Kosecka, Patryk Rudziński
et al.
Pregnancies after kidney transplantation are considered high risk. Preconceive care is crucial for favorable mother-fetal outcome but also for good renal graft function.
Herein, we report a case of kidney transplant recipient secondary to lupus nephritis with three consecutive successful pregnancies and excellent graft function after 16 post-transplant years. Preconception care included two protocolar biopsies performed prior to immunosuppressive treatment modifications. No signs of rejections were found in either biopsy, no additional treatment was necessary, and the patient was safely converted from mycophenolate mofetil to azathioprine. First pregnancy was naturally conceived, its course was uncomplicated and a healthy female newborn wasdelivered via vaginal birth. Within one year after delivery the patient presented proteinuria, borderline changes were found in the biopsy of allograft and were treated with immunosuppression augmentation and ACEI. At 7th post implantation year, after surveillance biopsy showing no signs of rejection and appropriate pharmacotherapy adjustments, second pregnancy occurred from in vitro fertilization (IVF). It was complicated with deep vein thrombosis, intrauterine growth restriction and premature birth in 32nd week of gestation. Three months after delivery, the patient conceived spontaneously, third pregnancy course was uncomplicated. Close follow up, including protocol and indication biopsies, allowed to preserve excellent graft function in the context of multiple immunosuppressive treatment adjustments. Here we present a case where natural conception and in vitro fertilization intertwine without harming the transplanted organ.
Aleksandra Kułakowska, Monika Kuc, Bartłomiej Lepczyński
et al.
The purpose of this essay is to examine how dialysis treatment affects patients' quality of life at the Department of Nephrology. The course of chronic renal disease and its management greatly influence the standard of living for dialysis patients. Renal insufficiency consequently results in numerous restrictions on patients' social, intellectual, and physical activities. Patients with chronic kidney disease have longer lifespans thanks to renal replacement therapy.
Banoth Thulasya Naik, Mohammad Farukh Hashmi, Neeraj Dhanraj Bokde
Recent developments in video analysis of sports and computer vision techniques have achieved significant improvements to enable a variety of critical operations. To provide enhanced information, such as detailed complex analysis in sports like soccer, basketball, cricket, badminton, etc., studies have focused mainly on computer vision techniques employed to carry out different tasks. This paper presents a comprehensive review of sports video analysis for various applications high-level analysis such as detection and classification of players, tracking player or ball in sports and predicting the trajectories of player or ball, recognizing the teams strategies, classifying various events in sports. The paper further discusses published works in a variety of application-specific tasks related to sports and the present researchers views regarding them. Since there is a wide research scope in sports for deploying computer vision techniques in various sports, some of the publicly available datasets related to a particular sport have been provided. This work reviews a detailed discussion on some of the artificial intelligence(AI)applications in sports vision, GPU-based work stations, and embedded platforms. Finally, this review identifies the research directions, probable challenges, and future trends in the area of visual recognition in sports.
Craig Fernandes, Jason D. Vescovi, Richard Norman
et al.
This paper presents a landmark study of equity, diversity and inclusion (EDI) in the field of sports analytics. We developed a survey that examined personal and job-related demographics, as well as individual perceptions and experiences about EDI in the workplace. We sent the survey to individuals in the five major North American professional leagues, representatives from the Olympic and Paralympic Committees in Canada and the U.S., the NCAA Division I programs, companies in sports tech/analytics, and university research groups. Our findings indicate the presence of a clear dominant group in sports analytics identifying as: young (72.0%), White (69.5%), heterosexual (89.7%) and male (82.0%). Within professional sports, males in management positions earned roughly 30,000 USD (27%) more on average compared to females. A smaller but equally alarming pay gap of 17,000 USD (14%) was found between White and non-White management personnel. Of concern, females were nearly five times as likely to experience discrimination and twice as likely to have considered leaving their job due to isolation or feeling unwelcome. While they had similar levels of agreement regarding fair processes for rewards and compensation, females "strongly agreed" less often than males regarding equitable support, equitable workload, having a voice, and being taken seriously. Over one third (36.3%) of females indicated that they "strongly agreed" that they must work harder than others to be valued equally, compared to 9.8% of males. We conclude the paper with concrete recommendations that could be considered to create a more equitable, diverse and inclusive environment for individuals working within the sports analytics sector.