bluuebunny/crossref_metadata_2025_split huggingface dataset:
Toward an intelligent computing system for the early diagnosis of Alzheimer’s disease based on the modular hybrid growing neural gas
Authors: Cabrera-León Ylermi , Fernández-López Pablo , García Báez Patricio , Kluwak Konrad , Navarro-Mesa Juan Luis , Suárez-Araujo Carmen Paz ,   for the Alzheimer’s Disease Neuroimaging Initiative *
Published: October 2024

Objective The proportion of older people will soon include nearly a quarter of the world population. This leads to an increased prevalence of non-communicable diseases such as Alzheimer’s disease (AD), a progressive neurodegenerative disorder and the most common dementia. mild cognitive impairment (MCI) can be considered its prodromal stage. The early diagnosis of AD is a huge issue. We face it by solving these classification tasks: MCI-AD and cognitively normal (CN)-MCI-AD. Methods An intelligent computing system has been developed and implemented to face both challenges. A non-neural preprocessing module was followed by a processing one based on a hybrid and ontogenetic neural architecture, the modular hybrid growing neural gas (MyGNG). The MyGNG is hierarchically organized, with a growing neural gas (GNG) for clustering followed by a perceptron for labeling. For each task, 495 and 819 patients from the Alzheimer’s disease neuroimaging initiative (ADNI) database were used, respectively, each with 211 characteristics. Results Encouraging results have been obtained in the MCI-AD classification task, reaching values of area under the curve (AUC) of 0.96 and sensitivity of 0.91, whereas 0.86 and 0.9 in CN-MCI-AD. Furthermore, a comparative study with popular machine learning (ML) models was also performed for each of these tasks. Conclusions The MyGNG proved to be a better computational solution than the other ML methods analyzed. Also, it had a similar performance to other deep learning schemes with neuroimaging. Our findings suggest that our proposal may be an interesting computing solution for the early diagnosis of AD.
Brain tumor classification using fine-tuned transfer learning models on magnetic resonance imaging (MRI) images
Authors: Rasa Sadia Maduri , Islam Md. Manowarul , Talukder Md. Alamin , Uddin Md. Ashraf , Khalid Majdi , Kazi Mohsin , Kazi Md. Zobayer 
Published: October 2024

Objective Brain tumors are a leading global cause of mortality, often leading to reduced life expectancy and challenging recovery. Early detection significantly improves survival rates. This paper introduces an efficient deep learning model to expedite brain tumor detection through timely and accurate identification using magnetic resonance imaging images. Methods Our approach leverages deep transfer learning with six transfer learning algorithms: VGG16, ResNet50, MobileNetV2, DenseNet201, EfficientNetB3, and InceptionV3. We optimize data preprocessing, upsample data through augmentation, and train the models using two optimizers: Adam and AdaMax. We perform three experiments with binary and multi-class datasets, fine-tuning parameters to reduce overfitting. Model effectiveness is analyzed using various performance scores with and without cross-validation. Results With smaller datasets, the models achieve 100% accuracy in both training and testing without cross-validation. After applying cross-validation, the framework records an outstanding accuracy of 99.96% with a receiver operating characteristic of 100% on average across five tests. For larger datasets, accuracy ranges from 96.34% to 98.20% across different models. The methodology also demonstrates a small computation time, contributing to its reliability and speed. Conclusion The study establishes a new standard for brain tumor classification, surpassing existing methods in accuracy and efficiency. Our deep learning approach, incorporating advanced transfer learning algorithms and optimized data processing, provides a robust and rapid solution for brain tumor detection.
A sustainable artificial-intelligence-augmented digital care pathway for epilepsy: Automating seizure tracking based on electroencephalogram data using artificial intelligence
Authors: Keikhosrokiani Pantea , Isomursu Minna , Uusimaa Johanna , Kortelainen Jukka 
Published: October 2024

Objective Scalp electroencephalograms (EEGs) are critical for neurological evaluations, particularly in epilepsy, yet they demand specialized expertise that is often lacking in many regions. Artificial intelligence (AI) offers potential solutions to this gap. While existing AI models address certain aspects of EEG analysis, a fully automated system for routine EEG interpretation is required for effective epilepsy management and healthcare professionals' decision-making. This study aims to develop an AI-augmented model for automating EEG seizure tracking, thereby supporting a sustainable digital care pathway for epilepsy (DCPE). The goal is to improve patient monitoring, facilitate collaborative decision-making, ensure timely medication adherence, and promote patient compliance. Method The study proposes an AI-augmented framework using machine learning, focusing on quantitative analysis of EEG data to automate DCPE. A focus group discussion was conducted with healthcare professionals to find the problem of the current digital care pathway and assess the feasibility, usability, and sustainability of the AI-augmented system in the digital care pathway. Results The study found that a combination of random forest with principal component analysis and support vector machines with KBest feature selection achieved high accuracy rates of 96.52% and 95.28%, respectively. Additionally, the convolutional neural networks model outperformed other deep learning algorithms with an accuracy of 97.65%. The focus group discussion revealed that automating the diagnostic process in digital care pathway could reduce the time needed to diagnose epilepsy. However, the sustainability of the AI-integrated framework depends on factors such as technological infrastructure, skilled personnel, training programs, patient digital literacy, financial resources, and regulatory compliance. Conclusion The proposed AI-augmented system could enhance epilepsy management by optimizing seizure tracking accuracy, improving monitoring and timely interventions, facilitating collaborative decision-making, and promoting patient-centered care, thereby making the digital care pathway more sustainable.
To read or not to read – A cross-sectional study of Swedish primary care patients’ adoption of patient accessible electronic health records
Authors: Muli Irene , Cajander Åsa , Hvitfeldt Helena , Lagerros Ylva Trolle , Söderberg Daniel , Sjöblom Linnea , Dahlgren Anna , Bertilson Bo C. , Farrokhnia Nasim , Amer-Wåhlin Isis , Taloyan Marina , Hägglund Maria 
Published: October 2024

Objective Patient-accessible electronic health records (PAEHR) were implemented in the Stockholm region of Sweden seven years ago. This study examines socio-demographic and psychographic factors associated with reading/not reading these records, as well as the common reasons for such behaviours. Methods Patients or guardians of minors seeking face-to-face or digital primary healthcare in the Stockholm region responded to a questionnaire about whether they were aware that they could read their PAEHR, and if so, if they had read it and reasons for reading or not reading. We conducted a comparative analysis of readers and non-readers and a stepwise multiple logistic regression. Results The majority of participants were aware that they could read the PAEHR (86%) and among those aware, 77% had read it. The odds of reading decreased with increased age, unfavourable opinion of PAEHR, low information literacy and being single. Access to a smartphone increased the probability of reading. Participants who had read their PAEHR had commonly read it to get an overview of their health and care (65%) and to follow up on a healthcare visit (55%). Participants who had not read their PAEHR stated generally that they did not need to (63%) and/or had received sufficient information from their providers (38%). Conclusions While most people were aware they could read the PAEHR and many had read it, a digital divide and several barriers to reading the PAEHR were identified. Efforts to increase PAEHR reading may be targeted at older people, people needing more informal support, those who may be excluded due to limited information literacy, and towards improving the patient portals’ usability.
Classification of coronary artery disease severity based on SPECT MPI polarmap images and deep learning: A study on multi-vessel disease prediction
Authors: Chen Jui-Jen , Su Ting-Yi , Huang Chien-Che , Yang Ta-Hsin , Chang Yen-Hsiang , Lu Henry Horng-Shing 
Published: October 2024

Background Coronary artery disease (CAD) is a global health concern. Conventional single photon emission computed tomography myocardial perfusion imaging (SPECT MPI) is a noninvasive method for assessing the severity of CAD. However, it relies on manual classification by clinicians, which can lead to visual fatigue and potential errors. Deep learning techniques have displayed promising results in CAD diagnosis and prediction, providing efficient and accurate analysis of medical images. Methods In this study, we explore the application of deep learning methods for assessing the severity of CAD and identifying cases of multivessel disease (MVD). We utilized the EfficientNet-V2 model in combination with DeepSMOTE to evaluate CAD severity using SPECT MPI images. Results Utilizing a dataset consisting of 254 patients (176 with MVD and 78 with single-vessel disease [SVD]), our model achieved an accuracy rate of 84.31% and area under the receiver operating characteristic curve (AUC) value of 0.8714 in predicting cases of MVD. These results underline the promising potential of our approach in MVD prediction, offering valuable diagnostic insights and the prospect of reducing medical costs. Conclusion This study emphasizes the feasibility of employing deep learning techniques for predicting MVD based on SPECT MPI images. The integration of Efficient-Net-V2 and DeepSMOTE methods effectively evaluates CAD severity and distinguishes MVD from SVD. Our research presents a practical approach to the early prediction and diagnosis of MVD, ultimately leading to enhanced patient outcomes and reduced healthcare costs.
The Role of Biomarkers in Predicting Outcomes of Anterior Cruciate Ligament Reconstruction: A Systematic Review
Authors: Batty Lachlan M. , Mackenzie Christopher , Landwehr Chelsea , Webster Kate E. , Feller Julian A. 
Published: October 2024

Background: Anterior cruciate ligament (ACL) injury is frequently associated with injuries to other parts of the knee, including the menisci and articular cartilage. After ACL injury and reconstruction, there may be progressive chondral degradation. Biomarkers in blood, urine, and synovial fluid can be measured after ACL injury and reconstruction and have been proposed as a means of measuring the associated cellular changes occurring in the knee. Purpose: To systematically review the literature regarding biomarkers in urine, serum, or synovial fluid that have been associated with an outcome measure after ACL reconstruction. Study Design: Systematic review; Level of evidence, 3. Methods: This review was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The MEDLINE, Embase, CINAHL, and Web of Science databases were searched to identify studies published before September 2023 that reported on patients undergoing ACL reconstruction where a biomarker was measured and related to an outcome variable. Of 9360 results, 16 studies comprising 492 patients were included. Findings were reported as descriptive summaries synthesizing the available literature. Results: A total of 45 unique biomarkers or biomarker ratios were investigated (12 serum, 3 urine, and 38 synovial fluid; 8 biomarkers were measured from >1 source). Nineteen different outcome measures were identified, including the International Knee Documentation Committee Subjective Knee Form, Knee injury and Osteoarthritis Outcome Score, numeric pain scores, radiological outcomes (magnetic resonance imaging and radiography), rates of arthrofibrosis and cyclops lesions, and gait biomechanics. Across the included studies, 17 biomarkers were found to have a statistically significant association ( P < .05) with an outcome variable. Serum interleukin 6 (s-IL-6), serum and synovial fluid matrix metalloproteinase-3 (s-MMP-3 and sf-MMP-3), urinary and synovial fluid C-terminal telopeptide of type 2 collagen (u-CTX-II and sf-CTX-II), and serum collagen type 2 cleavage product (s-C2C) showed promise in predicting outcomes after ACL reconstruction, specifically regarding patient-reported outcome measures (s-IL-6 and u-CTX-II), gait biomechanical parameters (s-IL-6, sf-MMP-3, s-MMP-3, and s-C2C), pain (s-IL-6 and u-CTX-II), and radiological osteoarthritis (ratio of u-CTX-II to serum procollagen 2 C-propeptide). Conclusion: The results highlight several biomarkers that have been associated with clinically important postoperative outcome measures and may warrant further research to understand if they can provide meaningful information in the clinical environment.
Association Between Hip Translation and Hip Rotation and Anatomy: A Pilot Quasi-static MRI Study
Authors: Kiapour Ata M. , Mitchell Charles , Hosseinzadeh Shayan , Emami Alex , Lewis Cara L. , Warfield Simon K. , Bixby Sarah , Stracciolini Andrea , Novais Eduardo N. , Kim Young-Jo 
Published: October 2024

Background: There is little known about translation of the hip and the relationship with hip rotation and morphology in asymptomatic patients. Hypotheses: (1) Femoral head would exhibit significant translations in asymptomatic hips, (2) femoral head translations would correlate to femoral rotations, and (3) range of femoral head translations would correlate to hip morphology. Study Design: Cross-sectional study; Level of evidence, 3. Methods: A total of 11 individuals (age, 23-47 years; 64% female) with asymptomatic hips underwent hip magnetic resonance imaging (MRI) in the following postures: neutral (supine), midflexion, maximum-flexion, internal rotation, internal rotation + midflexion, internal rotation + maximum-flexion, adduction, flexion-abduction-external rotation (FABER), extension, and lateral abduction. All rotations were passive. MRI-generated 3-dimensional hip models were used to quantify femoral rotations and translations. Femoral head diameter, acetabular diameter, lateral center-edge angle, alpha angle, femoral anteversion, acetabular version and inclination, and neck-shaft angle were measured from MRI. A t test was used if measured translations were statistically significant. Linear regression was used to assess the associations between translation and rotation. Pearson correlation was used to assess the relationships between hip anatomy and range of femoral head translations. Results: In all tested positions, the femoral head translated anteriorly by 2 ± 1 mm (maximum 5 mm, P < .001), posteriorly by 1 ± 1 mm (maximum 6 mm, P < .001), superiorly by 2 ± 2 mm (maximum 7 mm, P < .001), inferiorly by 2 ± 2 mm (maximum 6 mm, P < .001), laterally by 1 ± 1 mm (maximum 4 mm, P < .001), and medially by 2 ± 1 mm (maximum 5 mm, P < .001), relative to the rested supine position. Femoral flexion was associated with posterior translation of the femoral head ( P = .038). Femoral abduction was associated with medial translation of the femoral head ( P = .042). Higher femoral anteversion and smaller alpha angle were associated with a higher total magnitude of femoral head translation in the anterior-posterior direction ( P < .04). Smaller femoral anteversion, higher acetabular inclination, smaller lateral center-edge angle, and lower neck-shaft angle were associated with a higher total magnitude of femoral head translation in the superior-inferior direction ( P ≤ .03). Conclusion: Our study demonstrated that, during passive physiologic movement, asymptomatic hips on average translated up to 2 mm (with up to 7 mm maximum translation in some positions), which is potentially related to hip rotations and morphology. Further investigations are warranted to understand the normal and pathologic hip translations and their impact on hip function (ie, instability and impingement).
Factors Associated With a Successful Return to Performance After Anterior Cruciate Ligament Reconstruction: A Multiparametric Evaluation in Soccer Players
Authors: Mercurio Michele , Cerciello Simone , Corona Katia , Guerra Germano , Simonetta Roberto , Familiari Filippo , Galasso Olimpio , Gasparini Giorgio 
Published: October 2024

Background: Anterior cruciate ligament reconstruction (ACLR) is highly recommended in patients with ACL deficiency who must perform at a high physical level. A combination of functional and psychological outcome measures is necessary to provide a comprehensive evaluation of functional status after successful return to sport after ACLR. Purpose: To identify factors associated with higher functional outcomes among soccer players who had returned to full sports participation after ACLR. Study Design: Cohort study; Level of evidence, 3. Methods: A total of 168 out of 231 patients who underwent primary unilateral arthroscopic anatomic single-bundle ACLR were available at follow-up. Postoperatively, knee function, generic health outcomes, and psychological impact were assessed using the International Knee Documentation Committee (IKDC), the Knee injury and Osteoarthritis Outcome Score (KOOS), the Lysholm Knee Scoring Scale, the 12-item Short Form Health Survey (SF-12), and the ACL–Return to Sport after Injury scale. Results: After a mean follow-up of 35.5 ± 22.6 months, 85% of soccer players returned to performance. Midfielder position was associated with a better SF-12 Physical Component Summary (PCS) score ( P = .013), IKDC ( P = .003), total KOOS ( P < .001), KOOS Symptoms ( P = .004), KOOS Pain ( P = .029), KOOS Activities of Daily Living (ADL) ( P = .044), KOOS Sport and Recreation (Sport/Rec) ( P = .001), KOOS Quality of Life (QoL) ( P < .001), and Lysholm score ( P = .008). Playing only on natural grass was associated with lower SF-12 PCS scores ( P = .003), total KOOS ( P = .001), and KOOS Sport/Rec ( P = .011). Playing only on artificial turf was associated with lower Lysholm score ( P = .018) and total KOOS ( P = .014). The contact mechanism was associated with higher IKDC ( P = .044) and KOOS QoL ( P = .048), and injury affecting the dominant limb was associated with lower SF-12 Mental Component Summary scores ( P = .012). Playing at a nonprofessional level was associated with lower total KOOS ( P = .028), KOOS Symptoms ( P = .002), KOOS ADL ( P = .033), and KOOS Sport/Rec ( P = .016). Conclusion: Professional soccer players and the midfielder position are associated with better functional scores upon returning to the sport. A history of noncontact ACL injury and playing on a single type of surface are associated with lower functional outcomes upon returning to the sport. Lower mental health scores can be expected after injury of the dominant limb.
Exploring the Factors That Impact Recruitment and Retention of Pediatricians in Irish Community Hospitals Through the Attitudes of Trainees and Physicians-in-Practice
Authors: Healy Lydia , O'Grady Michael J , Fancourt Nigel , Mema Briseida 
Published: October 2024

BACKGROUND Difficulty attracting physicians to work in rural and remote areas is a worldwide problem. Specific to pediatrics, barriers to recruitment include burdensome on-call rosters, a lack of career opportunities for partners and professional isolation. METHODS To examine attitudes to working in a community hospital in Ireland, a mixed-methods sequential analysis approach was undertaken. Pediatricians-in training (70) and attending community pediatricians (25) completed surveys. Six semistructured interviews were used to triangulate survey results. RESULTS Most trainees planned to stay in Ireland (66/70), with five (eight%) stating that a career in a community hospital was their first preference. Personal factors such as a partner's career prospects and closeness to family and friends were the most important deterrents to working in a community hospital for trainees. Both trainees and attendings were concerned regarding professional isolation. Trainees were concerned about the poor reputation of community units. This converged with attendings feeling their role was not adequately respected, even though their job had more variability and exposure to emergencies, with less support, than working in a large center. Both groups agreed that targeted postgraduate training pathways and better training opportunities within Ireland were the best way to improve recruitment. Financial bonuses were not highly ranked as potential incentives. Motivators for considering a career in community units included the desire to make an impact and to build something new. Concerns about job satisfaction, professional recognition, and limited support for service development were prevalent. CONCLUSION This study reveals critical challenges and motivators influencing the shortage of pediatricians in Irish community units. Addressing these issues requires a multifaceted approach, incorporating targeted training, support structures, and recognition to enhance recruitment and retention in these underserved areas. Insights from the Irish context could be applied to improve recruitment and retention of pediatricians in regions with similar contexts.
Implementation of Computerized Prescriber Order Entry Systems: A Review of Impacts, Barriers, and Facilitators
Authors: Farrugia Ian , Vella Bonanno Patricia 
Published: October 2024

Objective: This review evaluated the impact of a digitized computerized prescriber order entry (CPOE) system and described barriers and facilitators for introducing a digitized system. Data Sources: A systematic literature search was conducted in PubMed, Medline, and CINAHL using keywords. Articles in English during the last 10 years were included. Study Selection and Data Extraction: Study selection was presented using a PRISMA flow diagram. Forty-eight studies were included. Data from the articles were presented to address each of the three objectives. Data Synthesis: CPOE systems improved the quality of care provided but also introduced new types of errors. Facilitating factors for implementation included leadership, stakeholder engagement, training, and user-centered design. Inadequate training, software design, changes in workload, and workflow disruptions were identified as barriers. Recommendations for implementation included dedicated training of users, user-centered design, a backup for system downtimes, and stakeholder engagement. Conclusion: Application of knowledge of the facilitators and barriers for the introduction of a CPOE system supports this change-management process within the specific context and augurs for more successful implementation.