Computer Methods and Programs in Biomedicine, Год журнала: 2024, Номер 260, С. 108564 - 108564
Опубликована: Дек. 24, 2024
Язык: Английский
Artificial Intelligence Review, Год журнала: 2024, Номер 57(10)
Опубликована: Авг. 20, 2024
Abstract In response to the burgeoning interest in Metaverse—a virtual reality-driven immersive digital world—this study delves into pivotal role of AI shaping its functionalities and elevating user engagement. Focused on recent advancements, prevailing challenges, potential future developments, our research draws from a comprehensive analysis grounded meticulous methodology. The study, informed by credible sources including SD, Scopus, IEEE, WoS, encompasses 846 retrieved studies. Through rigorous selection process, 54 papers were identified as relevant, forming basis for specific taxonomy Metaverse. Our examination spans diverse dimensions Metaverse, encompassing augmented reality, mixed Blockchain, Agent Systems, Intelligent NPCs, Societal Educational Impact, HCI Systems Design, Technical Aspects. Emphasizing necessity adopting trustworthy findings underscore enhance experience, safeguard privacy, promote responsible technology use. This paper not only sheds light scholarly Metaverse but also explores impact human behavior, education, societal norms, community dynamics. Serving foundation development implementation concept, identifies addresses seven open issues, providing indispensable insights subsequent studies integration
Язык: Английский
Процитировано
20
Diagnostics, Год журнала: 2025, Номер 15(3), С. 271 - 271
Опубликована: Янв. 23, 2025
Background/Objectives: Sports-related bone fractures are a common challenge in sports medicine, requiring accurate and timely diagnosis to prevent long-term complications enable effective treatment. Conventional diagnostic methods often rely on manual interpretation, which is prone errors inefficiencies, particularly for subtle localized fractures. This study aims develop lightweight efficient deep learning-based framework improve the accuracy computational efficiency of fracture detection, tailored needs medicine. Methods: We proposed novel detection based DenseNet121 architecture, incorporating modifications initial convolutional block final layers optimized feature extraction. Additionally, Canny edge detector was integrated enhance model ability detect structural discontinuities. A custom-curated dataset radiographic images focused sports-related used, with preprocessing techniques such as contrast enhancement, normalization, data augmentation applied ensure robust performance. The evaluated against state-of-the-art using metrics accuracy, recall, precision, complexity. Results: achieved 90.3%, surpassing benchmarks like ResNet-50, VGG-16, EfficientNet-B0. It demonstrated superior sensitivity (recall: 0.89) specificity (precision: 0.875) while maintaining lowest complexity (FLOPs: 0.54 G, Params: 14.78 M). These results highlight its suitability real-time clinical deployment. Conclusions: offers scalable, accurate, solution addressing critical challenges By enabling rapid reliable diagnostics, it has potential workflows outcomes athletes. Future work will focus expanding applications other imaging modalities types.
Язык: Английский
Процитировано
1
BMC Medical Informatics and Decision Making, Год журнала: 2025, Номер 25(1)
Опубликована: Янв. 31, 2025
Abstract This paper introduces SkinWiseNet (SWNet), a deep convolutional neural network designed for the detection and automatic classification of potentially malignant skin cancer conditions. SWNet optimizes feature extraction through multiple pathways, emphasizing width augmentation to enhance efficiency. The proposed model addresses potential biases associated with conditions, particularly in individuals darker tones or excessive hair, by incorporating fusion assimilate insights from diverse datasets. Extensive experiments were conducted using publicly accessible datasets evaluate SWNet’s effectiveness.This study utilized four datasets-Mnist-HAM10000, ISIC2019, ISIC2020, Melanoma Skin Cancer-comprising images categorized into benign classes. Explainable Artificial Intelligence (XAI) techniques, specifically Grad-CAM, employed interpretability model’s decisions. Comparative analysis was performed three pre-existing learning networks-EfficientNet, MobileNet, Darknet. results demonstrate superiority, achieving an accuracy 99.86% F1 score 99.95%, underscoring its efficacy gradient propagation capture across various levels. research highlights significant advancing classification, providing robust tool accurate early diagnosis. integration enhances mitigates hair tones. outcomes this contribute improved patient healthcare practices, showcasing exceptional capabilities classification.
Язык: Английский
Процитировано
1
The Ewha Medical Journal, Год журнала: 2025, Номер 48(1)
Опубликована: Янв. 31, 2025
Shoulder diseases pose a significant health challenge for older adults, often causing pain, functional decline, and decreased independence. This narrative review explores how deep learning (DL) can address diagnostic challenges by automating tasks such as image segmentation, disease detection, motion analysis. Recent research highlights the effectiveness of DL-based convolutional neural networks machine frameworks in diagnosing various shoulder pathologies. Automated analysis facilitates accurate assessment rotator cuff tear size, muscle degeneration, fatty infiltration MRI or CT scans, frequently matching surpassing accuracy human experts. Convolutional network-based systems are also adept at classifying fractures joint conditions, enabling rapid identification common causes pain from plain radiographs. Furthermore, advanced techniques like estimation provide precise measurements joint's range support personalized rehabilitation plans. These automated approaches have been successful quantifying local osteoporosis, utilizing learning-derived indices to classify bone density status. DL has demonstrated potential improve accuracy, efficiency, consistency management patients. Machine learning-based assessments imaging data parameters help clinicians optimize treatment plans patient outcomes. However, ensure their generalizability, reproducibility, effective integration into routine clinical workflows, large-scale, prospective validation studies necessary. As availability computational resources increase, ongoing development DL-driven applications is expected further advance personalize musculoskeletal care, benefiting both healthcare providers aging population.
Язык: Английский
Процитировано
1
Applied Data Science and Analysis, Год журнала: 2024, Номер 2024, С. 121 - 147
Опубликована: Авг. 7, 2024
There is a considerable threat present in genres such as machine learning due to adversarial attacks which include purposely feeding the system with data that will alter decision region. These are committed presenting different models way model would be wrong its classification or prediction. The field of study still relatively young and has develop strong bodies scientific research eliminate gaps current knowledge. This paper provides literature review defenses based on highly cited articles conference published Scopus database. Through assessment 128 systematic articles: 80 original papers 48 till May 15, 2024, this categorizes reviews from domains, Graph Neural Networks, Deep Learning Models for IoT Systems, others. posits findings identified metrics, citation analysis, contributions these studies while suggesting area’s further development robustness’ protection mechanisms. objective work basic background defenses, need maintaining adaptability platforms. In context, contribute building efficient sustainable mechanisms AI applications various industries
Язык: Английский
Процитировано
5
Scientific Reports, Год журнала: 2025, Номер 15(1)
Опубликована: Апрель 18, 2025
Язык: Английский
Процитировано
0
Intelligent Systems with Applications, Год журнала: 2024, Номер 23, С. 200431 - 200431
Опубликована: Авг. 27, 2024
Язык: Английский
Процитировано
2
Computers in Biology and Medicine, Год журнала: 2024, Номер 184, С. 109391 - 109391
Опубликована: Ноя. 22, 2024
Язык: Английский
Процитировано
2
Applied Data Science and Analysis, Год журнала: 2024, Номер 2024, С. 148 - 164
Опубликована: Сен. 8, 2024
Monkeypox is a rather rare viral infectious disease that initially did not receive much attention but has recently become subject of concern from the point view public health. Artificial intelligence (AI) techniques are considered beneficial when it comes to diagnosis and identification through medical big data, including imaging other details patients’ information systems. Therefore, this work performs bibliometric analysis incorporate fields AI bibliometrics discuss trends future research opportunities in Monkeypox. A search over various databases was performed title abstracts articles were reviewed, resulting total 251 articles. After eliminating duplicates irrelevant papers, 108 found be suitable for study. In reviewing these studies, given on who contributed topics or fields, what new appeared time, papers most notable. The main added value outline reader process how conduct correct comprehensive by examining real case study related disease. As result, shows great potential improve diagnostics, treatment, health recommendations connected with Possibly, application can enhance responses outcomes since hasten effective interventions.
Язык: Английский
Процитировано
1
International Journal of Computational Intelligence Systems, Год журнала: 2024, Номер 17(1)
Опубликована: Дек. 18, 2024
Язык: Английский
Процитировано
1