SLAS Technology最新文献

{"title":"Titanium surface functionalization with calcium-doped ZnO nanoparticles for hard tissue implant applications","authors":"Komel Tariq,&nbsp;Nosheen Fatima Rana,&nbsp;Sabah Javaid,&nbsp;Muneeba Khadim","doi":"10.1016/j.slast.2025.100337","DOIUrl":"10.1016/j.slast.2025.100337","url":null,"abstract":"<div><div>Implant-associated infections remain a significant challenge in orthopaedic and dental implants because they frequently result in implant failure, extended hospital stays, reoperations, and increased healthcare costs. Studies have shown that the cost of managing orthopaedic implant infections can range from USD 30,000 to over USD 100,000 per case, depending on severity and required surgical interventions. One of the primary pathogens responsible for these infections is <em>Staphylococcus aureus,</em> known for its potential to make biofilms on the surfaces of implants. To address this problem, this study investigates the formation of calcium phosphate-based biomimetic coatings substituted with calcium-doped ZnO nanoparticles on titanium discs to strengthen the antibacterial properties and enhance tissue integration. The SEM analysis of discs revealed uniform and dense coating layers with negligible surface defects, indicating a strong adhesive coating on titanium discs. The biomimetic-coated titanium implants with Ca-doped ZnO NPs were then evaluated for antibacterial activity using a closed system in an <em>in vitro</em> biofilm model. In case of 14 days treated disc, a significant increase in the antibacterial properties was observed against (<em>Staphylococcus aureus, p</em> &lt; 0.0001)<em>.</em> These findings suggest that calcium phosphate-based biomimetic coatings, doped with calcium-doped ZnO NPs show great potential for reducing the risk for implant-associated infections and improving the success rate of implants in clinical settings.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"33 ","pages":"Article 100337"},"PeriodicalIF":3.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient microaneurysm segmentation in retinal images via a lightweight Attention U-Net for early DR diagnosis","authors":"Muhammad Zeeshan Tahir ,&nbsp;Xingzheng Lyu ,&nbsp;Muhammad Nasir ,&nbsp;Wengan He ,&nbsp;Abeer Aljohani ,&nbsp;Sanyuan Zhang","doi":"10.1016/j.slast.2025.100323","DOIUrl":"10.1016/j.slast.2025.100323","url":null,"abstract":"<div><div>Diabetic Retinopathy (DR) is a complication of diabetes that can cause vision impairment and lead to permanent blindness if left undiagnosed. The increasing number of diabetic patients, coupled with a shortage of ophthalmologists, highlights the urgent need for automated screening tools for early DR diagnosis. Among the earliest and most detectable signs of DR are microaneurysms (MAs). However, detecting MAs in fundus images remains challenging due to several factors, including image quality limitations, the subtle appearance of MA features, and the wide variability in color, shape, and texture. To address these challenges, we propose a novel preprocessing pipeline that enhances the overall image quality, facilitating feature learning and improving the detection of subtle MA features in low-quality fundus images. Building on this preprocessing technique, we further develop a lightweight Attention U-Net model that significantly reduces the number of model parameters while achieving superior performance. By incorporating an attention mechanism, the model focuses on the subtle features of MAs, leading to more precise segmentation results. We evaluated our method on the IDRID dataset, achieving a sensitivity of 0.81 and specificity of 0.99, outperforming existing MA segmentation models. To validate its generalizability, we tested it on the E-Ophtha dataset, where it achieved a sensitivity of 0.59 and specificity of 0.99. Despite its lightweight design, our model demonstrates robust performance under challenging conditions such as noise and varying lighting, making it a promising tool for clinical applications and large-scale DR screening.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"34 ","pages":"Article 100323"},"PeriodicalIF":3.7,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable clinical diagnosis through unexploited yet optimized fine-tuned ConvNeXt Models for accurate monkeypox disease classification","authors":"Muhammad Waqar ,&nbsp;Zeshan Aslam Khan ,&nbsp;Shanzey Tariq Khawaja ,&nbsp;Naveed Ishtiaq Chaudhary ,&nbsp;Saadia Khan ,&nbsp;Khalid Mehmood Cheema ,&nbsp;Muhammad Farhan Khan ,&nbsp;Syed Sohail Ahmed ,&nbsp;Muhammad Asif Zahoor Raja","doi":"10.1016/j.slast.2025.100336","DOIUrl":"10.1016/j.slast.2025.100336","url":null,"abstract":"<div><div>Deep learning (DL) has had an incredible influence on many different scientific areas over the past couple of decades. Particularly in the field of healthcare, DL strategies were able to outclass other existing methodologies in image processing. The rapid expansion of the monkeypox endemic to over 40 nations apart from Africa has prompted serious worries in the realm of public health. Given that monkeypox can have symptoms that are akin to both chickenpox and measles, early detection can be difficult. Fortunately, due to the developments in artificial intelligence approaches, it can be implemented to promptly and accurately identify monkeypox disease using visual data information. Many DL driven techniques have already been exploited in the literature for skin related issues, which have provided accurate results to some extent. These models were dependent on extensive computational and time resources due to which the real-time applicability is difficult. Rather of building and training CNNs from scratch, this study uses transfer learning (TL) technique to fine-tune pre-trained networks, particularly exploiting various versions of ConvNeXt, by substituting last layer with additional task specific ones. A number of pre-processing and data augmentation methods have also been assessed and adjusted with regard to computing time and performance. The proposed study performs the binary and multi class monkeypox disease classification task. Promising accurate results of 99.9 % on the benchmark MSLD (binary class) dataset and 94 % on the MSLD v2.0 (multi-class) dataset is obtained by fine-tuned TL-based ConvNeXtSmall and ConvNeXtBase architecture with Adafactor optimization technique, demonstrating the practicality of the suggested framework as a substitute for the current ones. The proposed model is assessed through both standard train-test split and k-fold cross validation techniques. Furthermore, performance of models is also assessed on several other metrics including recall, F1 score, precision and multiple statistical tests incorporated with explainable AI methods for better interpretability of results. The concerns regarding the real-time applicability are tackled by utilizing the less time consuming and computationally efficient networks through the exploitation of transfer learning capabilities. Moreover, the explainable findings of the proposed study will be highly valuable for the healthcare professionals to understand the decisive behavior of the model and make informed clinical decisions.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"33 ","pages":"Article 100336"},"PeriodicalIF":3.7,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144719154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BrainCNN: Automated Brain Tumor Grading from Magnetic Resonance Images Using a Convolutional Neural Network-Based Customized Model.","authors":"Jing Yang, Muhammad Abubakar Siddique, Hafeez Ullah, Ghulam Gilanie, Lip Yee Por, Samah Alshathri, Walid El-Shafai, Haya Aldossary, Thippa Reddy Gadekallu","doi":"10.1016/j.slast.2025.100334","DOIUrl":"https://doi.org/10.1016/j.slast.2025.100334","url":null,"abstract":"<p><p>Brain tumors pose a significant risk to human life, making accurate grading essential for effective treatment planning and improved survival rates. Magnetic Resonance Imaging (MRI) plays a crucial role in this process. The objective of this study was to develop an automated brain tumor grading system utilizing deep learning techniques. A dataset comprising 293 MRI scans from patients was obtained from the Department of Radiology at Bahawal Victoria Hospital in Bahawalpur, Pakistan. The proposed approach integrates a specialized Convolutional Neural Network (CNN) with pre-trained models to classify brain tumors into low-grade (LGT) and high-grade (HGT) categories with high accuracy. To assess the model's robustness, experiments were conducted using various methods: (1) raw MRI slices, (2) MRI segments containing only the tumor area, (3) feature-extracted slices derived from the original images through the proposed CNN architecture, and (4) feature-extracted slices from tumor area-only segmented images using the proposed CNN. The MRI slices and the features extracted from them were labeled using machine learning models, including Support Vector Machine (SVM) and CNN architectures based on transfer learning, such as MobileNet, Inception V3, and ResNet-50. Additionally, a custom model was specifically developed for this research. The proposed model achieved an impressive peak accuracy of 99.45%, with classification accuracies of 99.56% for low-grade tumors and 99.49% for high-grade tumors, surpassing traditional methods. These results not only enhance the accuracy of brain tumor grading but also improve computational efficiency by reducing processing time and the number of iterations required.</p>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":" ","pages":"100334"},"PeriodicalIF":2.5,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144719153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Integrated Deep Learning Framework Using Adaptive Enhanced Vision Fusion and Modified MobileNet Architecture for Precision Classification of Skin Diseases with Enhanced Diagnostic Performance.","authors":"Ahsan Bilal Tariq, Muhammad Zaheer Sajid, Nauman Ali Khan, Muhammad Fareed Hamid, Anwaar UlHaq, Jarrar Amjad","doi":"10.1016/j.slast.2025.100331","DOIUrl":"https://doi.org/10.1016/j.slast.2025.100331","url":null,"abstract":"<p><p>Due to challenges such as illumination variability, noise, and visual distortions, machine learning (ML) and deep learning (DL) approaches for skin disease evaluation remain complex. Traditional methods often neglect these issues, leading to skewed predictions and poor performance. This research leverages a diverse dataset and robust image processing techniques to enhance diagnostic accuracy under such demanding conditions. We propose Dermo-Transfer, a novel architecture that combines MobileNet with dense blocks and residual connections to improve skin disease severity classification by addressing problems such as vanishing gradients and overfitting. Our method incorporates multi-scale Retinex, gamma correction, and histogram equalization to enhance image quality and visibility. Furthermore, a quantum support vector machine (QSVM) classifier is employed to improve classification performance, providing confidence scores and effectively handling multi-class problems. The proposed approach significantly enhances diagnostic accuracy and outperforms previous models. Dermo-Transfer not only improves pattern recognition and classification accuracy but also robustly handles varying image quality and lighting conditions. Dermo-Transfer was trained on 77,314 images covering skin conditions such as molluscum, warts, eczema, psoriasis, lichen planus, seborrheic keratoses, atopic dermatitis, melanoma, basal cell carcinoma (BCC), melanocytic nevi (NV), benign keratosis, and other benign tumors. The Dermo-Transfer classification method achieved accuracies of 99%, 98.5%, 97.5%, and 89% across four datasets, demonstrating its effectiveness and potential utility for clinical diagnostics. Additionally, Dermo-Transfer outperformed SkinLesNet and MobileNet V2-LSTM in terms of classification accuracy. Experimental results also highlight how IoT devices and mobile applications can enhance the computational efficiency and practical deployment of the Dermo-Transfer model.</p>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":" ","pages":"100331"},"PeriodicalIF":2.5,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144669002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Construction and validation of a nomogram model for predicting rebleeding in high-risk peptic ulcer bleeding patients based on lasso regression: A single center retrospective research","authors":"Qingrong Chen,&nbsp;Xuefeng Chen,&nbsp;Rongna You,&nbsp;Huaxin Huang","doi":"10.1016/j.slast.2025.100332","DOIUrl":"10.1016/j.slast.2025.100332","url":null,"abstract":"<div><div><strong>Objective</strong> To construct a Nomogram prediction model for high-risk Peptic Ulcer Bleeding (PUB) rebleeding using Lasso regression analysis and verify its predictive performance.</div><div><strong>Methods</strong> Retrospective research was performed on 279 cases with PUB admitted from January 2020 to December 2023 in a hospital's medical record information system. Clinical data were collected and randomly separated into a modeling group and a validation group in a 7:3. The overfitting in the constructed model was verified by comparing the clinical data. According to the clinical data of the modeling group, Lasso regression analysis was used to screen variables and conduct multiple factor analysis. A Nomogram model was constructed accordingly, and its predictive performance was validated.</div><div><strong>Results</strong> Among 279 patients included in this study, 45 cases had rebleeding, with an incidence rate of 16.13 %. The Lasso regression analysis demonstrated that a total of 15 variables were screened, taking <em>λ</em>_min as the standard. Multivariate analysis showed that diastolic blood pressure, hematocrit, blood transfusion volume, GBS score, endoscopic examination, and mechanical hemostasis were all independent risk factors for rebleeding in PUB cases. The Nomogram model based on multiple factor analysis demonstrated that the AUC of the modeling group and the validation group were 0.832 (95 %<em>CI</em> = 0.744-0.921) and 0.814 (95 %<em>CI</em> = 0.672-0.956), and Hosmer-Lemeshow <em>χ</em>² = 13.520 (<em>P</em> = 0.095). The DCA and CIC curve analysis results showed that using this model for patient intervention achieved positive benefits and relatively accurately predicted the rebleeding in PUB patients.</div><div><strong>Conclusion</strong> This research constructs a Nomogram model based on Lasso regression analysis that can effectively predict the rebleeding in PUB patients, providing reference for early prevention of clinical PUB rebleeding.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"33 ","pages":"Article 100332"},"PeriodicalIF":2.5,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144661070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and development of a rotating hotel as an enabler for mobile robotics integration in the lab of the future","authors":"Richard Rösch ,&nbsp;Tobias Sauer ,&nbsp;Christina Mavreas,&nbsp;Stefan Scheuermann,&nbsp;Andreas Traube","doi":"10.1016/j.slast.2025.100330","DOIUrl":"10.1016/j.slast.2025.100330","url":null,"abstract":"<div><div>The development of the rotating hotel system marks a significant advancement in laboratory automation. It is particularly impactful for integrating mobile robots with closed systems like high-throughput screening (HTS) workstations, which are central to the Lab of the Future. This technical brief presents the design and integration of a modular rotating hotel unit, specifically engineered to support autonomous, flexible sample handling. With up to four SBS-compatible plate nests, customizable configurations, and built-in presence sensors, the rotating hotel enables for the first time, mobile robots to transfer samples and labware seamlessly between HTS workstations automatically, enhancing workflow efficiency and reducing manual handling.</div><div>Key use cases for the rotating hotel include continuous sample loading and unloading during HTS runs, cross-station sample retrieval and contamination-free handling for sensitive assays. The system’s compact, removable design allows for straightforward maintenance and cleaning, supporting safe and contamination-free environments. By eliminating process interruptions and enabling 24/7 operation, the rotating hotel can significantly improve throughput and flexibility, essential features for next-generation labs. This modular storage solution demonstrates how tailored automation can transform workflows, positioning the rotating hotel as a key enabling component in the adaptive, high-efficiency Lab of the Future.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"33 ","pages":"Article 100330"},"PeriodicalIF":2.5,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144651182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Magnetic nanoparticles-based targeted drug delivery system in tumor pain management","authors":"Xiaoli Lv ,&nbsp;Fei Wang ,&nbsp;Xiaomei Liu ,&nbsp;Ting Xu ,&nbsp;Xiaofeng Zhou","doi":"10.1016/j.slast.2025.100333","DOIUrl":"10.1016/j.slast.2025.100333","url":null,"abstract":"<div><h3>Background</h3><div>the precise management of tumor-related pain is a critical challenge in improving the quality of life (QoL) of cancer patients. This study aimed to develop a magnetic nanoparticle-based transdermal drug delivery system (MNPs-TDDS) using green nanotechnology, incorporating folic acid targeting and magnetic-controlled release mechanisms, to achieve efficient and low-toxicity pain intervention.</div></div><div><h3>Methods</h3><div>folic acid-modified magnetic nanocomplexes (catHEC·FA@SPIO) were synthesized via a water-phase co-precipitation method. The structural and morphological characteristics were verified using Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and transmission electron microscopy (TEM). A total of 70 nasopharyngeal carcinoma (NPC) patients were enrolled and randomly divided into the experimental group (EG, MNPs-TDDS) and the control group (CG, conventional analgesia). Pain scores (NRS), psychological status (SAS/SDS), activities of daily living (Barthel index), and cancer cell apoptosis rates were assessed.</div></div><div><h3>Results</h3><div>the particle size of catHEC·FA@SPIO was 150±20 nm, exhibiting pH-responsive release properties (82.4 % cumulative release over 72 h at pH 5.5). The NRS scores (2.35±0.47 vs<em>.</em> 4.47±0.87), SAS (41.46±1.13 vs<em>.</em> 55.32±1.24), and SDS (40.06±0.75 vs<em>.</em> 54.11±1.52) in the EG were significantly lower than those in the CG (<em>P</em> &lt; 0.05), with cytotoxicity to normal cells being under 10 %. The nursing satisfaction rate in the EG was 94.29 %, significantly higher than the 68.57 % in the CG (<em>P</em> &lt; 0.001).</div></div><div><h3>Conclusion</h3><div>this study is the first to combine a green synthesis strategy with multidimensional clinical evaluation, demonstrating the comprehensive advantages of MNPs-TDDS in pain relief, improving psychological state, and enhancing activities of daily living. This approach provides an innovative solution for the precise management of tumor-related pain. Future research should further validate its long-term safety and applicability across various cancer types.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"33 ","pages":"Article 100333"},"PeriodicalIF":2.5,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144651191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PRIMDEx: Prototyping rapid innovation of microfluidics devices for experimentation","authors":"Patrick B. Kruk,&nbsp;Jose A. Wippold","doi":"10.1016/j.slast.2025.100326","DOIUrl":"10.1016/j.slast.2025.100326","url":null,"abstract":"<div><div>Microfluidics has quickly become an established technology in the transformative fields that make up broader biotechnology. Microfluidics has applications spanning the entire breadth of the discipline, from chemical synthesis, environmental monitoring, biomedical diagnostics, to lab- and organ-on-a-chip. New demands for novel microfluidic chips have outpaced their contemporary manufacturing methods, thus limiting their scientific applicability. This predicament is particularly accentuated for R&amp;D and research laboratories where resources (time &amp; money) are limited. Manufacturing a microfluidic device (MFD) for mass production typically involves outsourcing a design for CNC machining of the negative mold, followed by Injection Molding (IM) the positive-feature consumables or MFDs. This process can cost ∼$1000-$5000 depending on complexity and can require a 1–2-week lead time. In comparison, 3D Printing (3DP) is limited by long print times, limited resolutions, and higher per unit material cost. This leaves traditional commercial fabrication processes impractical to implement into a typical biotech experimental procedure, where they could be subjected to constantly changing experimental demands and redesigns. Each redesign and subsequent round of fabrication demands greater cost and time investments. Here, we present PRIMDEx, or <u>P</u>rototyping <u>R</u>apid <u>I</u>nnovation of <u>M</u>icrofluidic <u>D</u>evices for <u>Ex</u>perimentation, to address this by integrating both 3DP and rapid IM into a single manufacturing workflow. PRIMDEx implemented the advantages of both manufacturing methods to establish an approach more conducive to the design-test-build cycles of biotech and biomedical research regimes.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"33 ","pages":"Article 100326"},"PeriodicalIF":2.5,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144576957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data acquisition of exercise and fitness pressure measurement based on artificial intelligence technology","authors":"Ru Liu ,&nbsp;Wenxi Shen","doi":"10.1016/j.slast.2025.100328","DOIUrl":"10.1016/j.slast.2025.100328","url":null,"abstract":"<div><div>This project aims to improve the accuracy of fitness and physical pressure ratings, focusing on basketball, by integrating artificial intelligence (AI) into data collection and training. Athletes and fitness fanatics can benefit greatly from the data collected using complex AI algorithms to determine stress levels. This study employs the Intelligent Physiological Monitoring Framework for Exercise and Fitness Pressure Measurement (IPM-EFPM) to perform automated stress tests that employ AI to enhance the precision of exercise and fitness pressure measurements. Basketball training programs can benefit from this framework's utilization of state-of-the-art technology, meticulous monitoring of exercise-induced stress, and continuous validation and improvement. The IPM-EFPM system gathers data from wearable sensors, uses real-time location systems, and employs artificial intelligence's Long Short-Term Memory (LSTM) and machine learning algorithms to uncover new insights in healthcare and sports. To accurately record fitness strain, physical activity, exercise-induced stress, and sports like basketball, this system employs cutting-edge artificial intelligence technologies, such as wearable sensors and current gathering data methods. Placement of sensors, real-time data collecting, data preprocessing and integrating, evaluation of stress by artificial intelligence algorithms, discovery and application of new information, validation and improvement are all parts of an iterative method that has been fine-tuned for use in sports and fitness settings by the IPM-EFPM. Examining the intricate relationship between AI, physical activity, and psychological stress is the main objective of this research. This could have real-world uses tailored to the sports world, particularly for basketball players.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"33 ","pages":"Article 100328"},"PeriodicalIF":2.5,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144576956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}