Hasil untuk "Computer applications to medicine. Medical informatics"

Zhentong Li, Linlin Li, Yuan Deng et al.

The allotriploid grouper, Epinephelus fuscoguttatus ♀ × Epinephelus tukula ♂, is an economically valuable aquaculture species owing to its fast growth rate and desirable meat quality. Despite its many favorable traits, there is still a lack of records on allotriploid grouper mitochondrial genome. In this study, we determined and analyzed the complete mitochondrial genome of the triploid hybrid grouper Epinephelus fuscoguttatus ♀ × Epinephelus tukula ♂. The mitogenome was 16,610 bp in length and contains 13 protein-coding genes, 2 rRNA genes, 22 tRNA genes and a control region. The order and composition of these genes were similar to other vertebrates. The overall base composition was 29.11 % for A, 28.40 % for C, 15.65 % for G, and 26.84 % for T, with a higher A + T content (55.95 %). Phylogenetic analysis showed that the allotriploid grouper, Epinephelus fuscoguttatus ♀ × Epinephelus tukula ♂, was most closely related to Epinephelus fuscoguttatus. This study provides the first mitochondrial genome record of allotriploid grouper, which will facilitate future research on evolution and phylogeny of Epinephelidae.

Cindy Lorena Gómez-Heredia, Jose David Ardila-Useda, Andrés Felipe Cerón-Molina et al.

Accurate color property measurements are critical for advancing artificial vision in real-time industrial applications. RGB imaging remains highly applicable and widely used due to its practicality, accessibility, and high spatial resolution. However, significant uncertainties in extracting chromatic information highlight the need to define when conventional digital images can reliably provide accurate color data. This work simultaneously compares six chromatic properties across 700 Pantone^® TCX fabric samples, using optical data acquired simultaneously from both hyperspectral (HSI) and digital (RGB) cameras. The results indicate that the accurate interpretation of optical information from RGB (sRGB and REC2020) images is significantly influenced by lightness (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>L</mi></mrow><mrow><mo>*</mo></mrow></msup></mrow></semantics></math></inline-formula>) values. Samples with bright and unsaturated colors (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>L</mi></mrow><mrow><mo>*</mo></mrow></msup><mo>></mo></mrow></semantics></math></inline-formula> 50) reach ratio-to-performance-deviation (RPD) values above 2.5 for four properties (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>L</mi></mrow><mrow><mo>*</mo></mrow></msup><mo>,</mo><mo> </mo><msup><mrow><mi>a</mi></mrow><mrow><mo>*</mo></mrow></msup><mo>,</mo><mo> </mo><msup><mrow><mi>b</mi></mrow><mrow><mo>*</mo></mrow></msup></mrow></semantics></math></inline-formula> <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>h</mi></mrow><mrow><mi>a</mi><mi>b</mi></mrow></msub></mrow></semantics></math></inline-formula>), indicating a good correlation between HSI and RGB information. Absolute color difference comparisons (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mo>∆</mo><mi>E</mi></mrow><mrow><mi>a</mi></mrow></msub></mrow></semantics></math></inline-formula>) between HSI and RGB images yield values exceeding 5.5 units for red-yellow-green samples and up to 9.0 units for blue and purple tones. In contrast, relative color differences (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mo>∆</mo><mi>E</mi></mrow><mrow><mi>r</mi></mrow></msub></mrow></semantics></math></inline-formula>) comparisons show a significant decrease, with values falling below 3.0 for all lightness values, indicating the practical equivalence of both methodologies according to the Two One-Sided Test (TOST) statistical analysis. These results confirm that RGB imagery achieves reliable color consistency when evaluated against a practical reference.

Laura Lorenzo-Gallego, Silvia Muñoz-Pastor, Maria Remedios Menéndez-Calvo et al.

BackgroundPhysical inactivity represents a significant public health issue with substantial socioeconomic costs. In the autonomous Community of Madrid, 39.17% of the population does not meet the World Health Organization recommendations for physical activity (PA). Gender, sex, and occupational factors are well-established determinants of leisure-time physical activity (LTPA); yet, few studies have examined these factors among university staff. ObjectiveThis study aims to analyze the relationship between LTPA and work ability among university staff in the autonomous Community of Madrid, considering the potential modifying effect of occupational PA. Secondary objectives include examining associations between LTPA, musculoskeletal disorders, health-related quality of life, physical and mental workload, and working conditions, with a focus on sex and gender differences. MethodsA cross-sectional study was designed involving 885 university staff members from the University of Alcalá, Madrid, Spain. Participants will complete an online survey, including sociodemographic questions and validated instruments: the Global Physical Activity Questionnaire, Work Ability Index, Nordic Musculoskeletal Questionnaire, Short Form-12 Health Survey, and the National Aeronautics and Space Administration Task Load Index. Descriptive and inferential statistics will be performed to assess the associations between LTPA, occupational PA, and work ability, adjusted for relevant covariates. ResultsThis study was approved by the ethics committee of the University of Alcalá in November 2024. Recruitment began in December 2024 and will continue until June 2027. Data analysis will be conducted progressively. Results will be disseminated in peer-reviewed journals and presented at scientific conferences following gender-sensitive and transparent reporting standards. ConclusionsUnderstanding the determinants of PA and their interaction with work ability and gender may inform the development of targeted, culturally sensitive interventions to reduce sedentary behavior and its associated health and economic burdens in university staff. Trial RegistrationClinicalTrials.gov NCT06723808; https://clinicaltrials.gov/study/NCT06723808 International Registered Report Identifier (IRRID)DERR1-10.2196/80298

Helen Guyatt, Nicola Doherty, Jenny Limond et al.

Abstract Background Limited resources in health and social care and long waiting lists for autism assessment are resulting in high numbers of autistic people not being adequately supported. We sought to explore the feasibility and effectiveness of meeting this support need through an end-to-end digital self-referral and digital mental health service. Methods Together with health and social care teams and young autistic people we developed a self-referral pathway that allowed young autistic people (aged 16–25) to access the digital self-management support system, Brain in Hand (BiH), without the need for diagnosis or referral by an external agency. Participants were reached using digital media channels which linked to a BiH landing page. Reach, progress and engagement through the pathway was monitored and participants were surveyed on their eligibility and suitability for BiH. Results A total of 243 BiH licences were issued within 9 weeks of the start of the digital media campaign which reached nearly half a million people with close to 20,000 clicking through to the BiH landing page. Most of the young people being issued with the digital support tool demonstrated high levels of need, 69% experienced clinically significant depression, 83% anxiety, 99% moderate or high executive function challenges, and 60% lacked current support. Conclusions This pilot demonstrates that young people understand their needs and directing them to a support service through a digital media campaign presents an efficient and effective approach to reaching young autistic people in need. This suggests that digital media channels and self-referral could offer a practical solution to broaden access to a range of digital mental health platforms without placing additional resource burden on health and social care teams.

Danae Kala Rodriguez Bardaji, Girish Kumar, Samantha Tran et al.

This dataset comprises whole genome sequencing burdock (Arctium lappa) naturalized in a residential yard in Rochester, New York, USA. Total DNA was extracted from a leaf sample and processed using the Illumina Nextera XT DNA library preparation kit. Sequencing on the NextSeq 2000 platform produced 127.4 GB of raw data, yielding 125.8 GB of high-quality reads after filtering, with an average genome coverage of 75x. The genome was assembled de novo into 792,817 contigs, achieving a total genome length of 1,075,454,921 base pairs with a GC content of 37.03 %. Scaffolding against a Chinese A. lappa reference genome improved genome completeness from 49.1 % to 94.93 %, successfully recovering the majority of protein-coding genes.Variant analysis identified approximately 20.8 million Single Nucleotide Polymorphisms (SNPs) and 1.3 million indels, including functionally significant mutations. The Internal Transcribed Spacer 2 (ITS2) ribosomal region was isolated and compared with global references, revealing significant genetic differentiation between the U.S.A and Chinese populations. This comprehensive genomic dataset has been deposited in publicly accessible repositories, including National Center for Biotechnology Information (NC and Zenodo. The sequencing of this sample provides a valuable resource for comparative genomics, population genetics, and investigations into bioactive compounds with antimicrobial properties, supporting agricultural and pharmaceutical applications. Direct access to the dataset is available at 10.5281/zenodo.14607136

Aslı Semerci, Celine AlBalaa, Brian Corrie et al.

Recent advancements in sequencing technologies have led to an exponential increase in adaptive immune receptor repertoire (AIRR) data. These receptors, crucial to the adaptive immune system, are believed to have strong potential for diagnostic applications. The immune repertoires represent a wealth of data, creating a growing demand for robust computational methods to analyze and interpret this vast amount of information.In this review, we examine the application of machine learning algorithms for the classification and analysis of AIRR-seq data for different diagnostic applications. We provide a high-level division of current approaches based on their focus on repertoire-level or sequence-level features. We provide an overview of the current state of public AIRR data sets available for model training. Finally, we briefly highlight what lessons can be learned from successful AIRR diagnostic approaches and what hurdles still must be overcome.

Felix Richter, Emma Holmes, Florian Richter et al.

Abstract AI is transforming healthcare, yet pediatric adoption remains limited and governance is underdeveloped. We review existing frameworks and identify pediatric-specific gaps: insufficient stakeholder engagement, developmentally appropriate consent/assent, limited bias mitigation, and unclear accountability. An analysis of FDA-cleared pediatric SaMDs shows radiology dominance while other specialties lag. We call for a pediatric-centric governance approach emphasizing transparency, inclusive participation, equitable data practices, and rigorous post-deployment monitoring to ensure safe, responsible integration.

Justin Pointer, Angelica E Lang, Denise Balogh et al.

Background: Shoulder range of motion (ROM) limitations following breast cancer treatments are common. Remotely monitoring ROM changes after treatments through smartphone applications can expand rehabilitation options for breast cancer patients. The aim of the study was to investigate the ability of ShApp—a breast cancer smartphone application—to distinguish between different clinically useful shoulder ROM target levels, as well as the consistency of ROM measurements recorded by ShApp. Methods: Ten healthy, cancer-free, participants (mean age 32 ± 10.9 years, 4 females) with full shoulder ROM performed five shoulder movements to pre-determined target angles while holding the smartphone with ShApp open. Each movement was repeated three times bilaterally. Results: Agreement of ROM values between ShApp and the target values was assessed with interclass correlation coefficients (ICCs) and Bland–Altman analysis. Inter- and intra-rater reliability of ROM values were also assessed with ICCs, and ShApp's ability to distinguish between high, mid, and low ROM target angles with t -tests. Results showed good to excellent reliability between ShApp and target values (ICC 0.68–0.95) and mean differences were less than 10° for all movements except abduction. The reliability of ShApp measurements between participants was excellent for all movements (ICC >0.79) and within participants was excellent (ICC >0.90) for all movements except extension (ICC = 0.67). For all movements, significant differences between high, mid, and low angles were found ( P  < 0.001). Conclusion: ShApp shows promise as a reliable and valid tool to remotely monitor shoulder ROM. Its ability to distinguish between clinically useful threshold angles at the shoulder highlights its clinical potential, particularly in the acute and early phases of patient recovery from breast cancer surgery.

Zhi-Geng Jin, Zhe-Qi Zhang, Bin-Bin Liu et al.

BackgroundVenous thromboembolism (VTE) is a significant public health issue, with a rising global incidence despite extensive research efforts. Patient-centered care, which tailors treatment to individual needs, has shown potential in enhancing outcomes. The integration of smart technologies with psychological frameworks such as the health belief model and the knowledge-attitude-practice (KAP) model may further improve patient engagement and adherence. To address this, we have developed a smart technique–assisted patient-centered care mobile health app for managing VTE (mVTEA), which integrates psychological frameworks to improve patient outcomes in VTE management. ObjectiveThis study aims to investigate the impact of the mVTEA app on the knowledge, attitudes, and practices of VTE in patients with or at high risk of VTE. MethodsThe SmaVTE (smart technology facilitated patient-centered venous thromboembolism management) study is a 2-armed, single-center, parallel-group, randomized controlled trial. A total of 256 hospitalized patients with or at high risk of VTE will be recruited on the day of their discharge from August 2024 to June 2025. Participants will be randomly allocated to either the mVTEA management group or the routine management group in a 1:1 ratio. The mVTEA management group (n=128) will receive patient-centered VTE management facilitated by the mVTEA app after discharge. The routine management group (n=128) will be administered conventional postdischarge management according to local clinical practice. The KAP of patients will be assessed by a structured KAP questionnaire on VTE. The primary outcome is the difference in patients’ KAP on VTE at 3-month follow-up between the 2 groups. Secondary outcomes include scores on each domain of the questionnaire, quality of life, VTE events, chronic thromboembolic pulmonary hypertension, chronic thromboembolic pulmonary disease, postpulmonary embolism syndrome, major bleeding events, VTE-related hospitalizations or rehospitalizations, deaths, and new-onset atrial fibrillation or atrial flutter at 3-month follow-up. ResultsParticipants are currently being recruited. The first participant was enrolled in August 2024, which marked the official start of the study. The recruitment process is expected to be completed in June 2025. As of the submission of the paper, 185 patients had been enrolled in this clinical trial. At present, all included patients are being followed up according to the outlined schedule. ConclusionsThe SmaVTE study offers a pioneering approach to VTE prevention and treatment by combining smart technology with patient-centered care and established theoretical frameworks. The findings could significantly impact clinical practice and inspire further research into the integration of smart technologies with behavioral science theories. Trial RegistrationClinicalTrials.gov NCT06350331; https://clinicaltrials.gov/study/NCT06350331 International Registered Report Identifier (IRRID)DERR1-10.2196/67254

S. Pestotnik, D. Classen, R. Evans et al.

Frank P.-W. Lo, Jianing Qiu, Modou L. Jobarteh et al.

Abstract We have developed a population-level method for dietary assessment using low-cost wearable cameras. Our approach, EgoDiet, employs an egocentric vision-based pipeline to learn portion sizes, addressing the shortcomings of traditional self-reported dietary methods. To evaluate the functionality of this method, field studies were conducted in London (Study A) and Ghana (Study B) among populations of Ghanaian and Kenyan origin. In Study A, EgoDiet’s estimations were contrasted with dietitians’ assessments, revealing a performance with a Mean Absolute Percentage Error (MAPE) of 31.9% for portion size estimation, compared to 40.1% for estimates made by dietitians. We further evaluated our approach in Study B, comparing its performance to the traditional 24-Hour Dietary Recall (24HR). Our approach demonstrated a MAPE of 28.0%, showing a reduction in error when contrasted with the 24HR, which exhibited a MAPE of 32.5%. This improvement highlights the potential of using passive camera technology to serve as an alternative to the traditional dietary assessment methods.

Vasi Naganathan, Angus Ritchie, Michael Solomon et al.

Objectives This project aimed to determine where health technology can support best-practice perioperative care for patients waiting for surgery.Methods An exploratory codesign process used personas and journey mapping in three interprofessional workshops to identify key challenges in perioperative care across four health districts in Sydney, Australia. Through participatory methodology, the research inquiry directly involved perioperative clinicians. In three facilitated workshops, clinician and patient participants codesigned potential digital interventions to support perioperative pathways. Workshop output was coded and thematically analysed, using design principles.Results Codesign workshops, involving 51 participants, were conducted October to November 2022. Participants designed seven patient personas, with consumer representatives confirming acceptability and diversity. Interprofessional team members and consumers mapped key clinical moments, feelings and barriers for each persona during a hypothetical perioperative journey. Six key themes were identified: ‘preventative care’, ‘personalised care’, ‘integrated communication’, ‘shared decision-making’, ‘care transitions’ and ‘partnership’. Twenty potential solutions were proposed, with top priorities a digital dashboard and virtual care coordination.Discussion Our findings emphasise the importance of interprofessional collaboration, patient and family engagement and supporting health technology infrastructure. Through user-based codesign, participants identified potential opportunities where health technology could improve system efficiencies and enhance care quality for patients waiting for surgical procedures. The codesign approach embedded users in the development of locally-driven, contextually oriented policies to address current perioperative service challenges, such as prolonged waiting times and care fragmentation.Conclusion Health technology innovation provides opportunities to improve perioperative care and integrate clinical information. Future research will prototype priority solutions for further implementation and evaluation.

Michele Furlani, Nicole Riberti, Maria Laura Gatto et al.

Phase-contrast X-ray imaging is becoming increasingly considered since its first applications, which occurred almost 30 years ago. Particular emphasis was placed on studies that use this technique to investigate soft tissues, which cannot otherwise be investigated at a high resolution and in a three-dimensional manner, using conventional absorption-based settings. Indeed, its consistency and discrimination power in low absorbing samples, unified to being a not destructive analysis, are pushing interests on its utilization from researchers of different specializations, from botany, through zoology, to human physio-pathology research. In this regard, a challenging method for 3D imaging and quantitative analysis of collagenous tissues has spread in recent years: it is based on the unique characteristics of synchrotron radiation phase-contrast microTomography (PhC-microCT). In this review, the focus has been placed on the research based on the exploitation of synchrotron PhC-microCT for the investigation of collagenous tissue physio-pathologies from solely human samples. Collagen tissues’ elasto-mechanic role bonds it to the morphology of the site it is extracted from, which could weaken the results coming from animal experimentations. Encouraging outcomes proved this technique to be suitable to access and quantify human collagenous tissues and persuaded different researchers to approach it. A brief mention was also dedicated to the results obtained on collagenous tissues using new and promising high-resolution phase-contrast tomographic laboratory-based setups, which will certainly represent the real step forward in the diffusion of this relatively young imaging technique.

V. A. Rudenko, L. N. Kakaulina

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Yaguang Guo, Qi Zou, Lu Jin

Abstract Because of the popularisation of high‐resolution images, detecting objects in these images quickly and accurately has attracted increasing attention in recent studies. Current convolutional neural networks (CNN)‐based detection methods have limitations in detecting small objects owing to the interference of scale variation. In this work, we propose an improved generic framework based on YOLOv3. Equipped with multiresolution supervision for training and multiresolution aggregation for inference, this method can deal with the challenge of scale variation in high‐resolution images. At first, we move up the multiscale prediction position and add a dilated convolution module on YOLOv3 to improve the accuracy of detection, especially for small objects. Then, we present a coarse to fine method to reduce the detection time. Experiments on a COCO dataset show that our approach achieves 2.8% better accuracy compared with the previous YOLOv3. On a Dataset for Object deTection in Aerial images dataset (a high‐resolution remote sensing dataset), our approach outperformed the YOLOv3 by nearly three percentage points in mean average precision. Moreover, it is up to three times faster as well and two times smaller than the previous YOLOv3.

S. McLachlan, Kudakwashe Dube, E. Kyrimi et al.

Problem Learning health systems (LHS) are an underexplored concept. How LHS will operate in clinical practice is not well understood. This paper investigates the relationships between LHS, clinical care process specifications (CCPS) and the established levels of medical practice to enable LHS integration into daily healthcare practice. Methods Concept analysis and thematic analysis were used to develop an LHS characterisation. Pathway theory was used to create a framework by relating LHS, CCPS, health information systems and the levels of medical practice. A case study approach evaluates the framework in an established health informatics project. Results Five concepts were identified and used to define the LHS learning cycle. A framework was developed with five pathways, each having three levels of practice specificity spanning population to precision medicine. The framework was evaluated through application to case studies not previously understood to be LHS. Discussion Clinicians show limited understanding of LHS, increasing resistance and limiting adoption and integration into care routine. Evaluation of the presented framework demonstrates that its use enables: (1) correct analysis and characterisation of LHS; (2) alignment and integration into the healthcare conceptual setting; (3) identification of the degree and level of patient application; and (4) impact on the overall healthcare system. Conclusion This paper contributes a theoretical framework for analysis, characterisation and use of LHS. The framework allows clinicians and informaticians to correctly identify, characterise and integrate LHS within their daily routine. The overall contribution improves understanding, practice and evaluation of the LHS application in healthcare.

V. Texier, N. Henda, Stephanie W Cox et al.

Background: Multiple precision medicine programs in oncology have been launched, leading to the collection of large amount of clinical and genomic data. Tumor heterogeneity and the accumulation of rare and of unknown significance genomic alterations require to study thousands of individuals to identify clinically relevant genomic drivers. Better the scale is or will be, better our understanding of a disease is or would be. In this context, data sharing appears as a precondition of the success of precision medicine in oncology. The work we present here attempts to describe the current stage of data sharing in precision medicine with a focus on oncology. Methods: A scientometric study of the publications indexed in the Web of Science (WoS) database was conducted by applying quantitative methods. A search string was defined by selecting relevant keywords, and specific metrics such as the research area, publication year, funding organization, and geographical localization were studied. A third-party software (VOSViewer) was used for analyzing and visualizing bibliometric networks. Results: A set of 672 documents were obtained between 1900 and 2019, year 2005 was a turning point, and the trend reached 86–113 publications per year over the last three years. Western Europe and Northern America accounted for 80% of the whole world production. From the 672 publications, diverse research areas were identified (i.e., computer science and medical informatics), as well as specific medical specialties (i.e., medical genetics and oncology). The term co-occurrences map identified the main challenges associated with data sharing. Conclusions: This area of research is relatively new with an unequal quantitative production of scientific literature across countries and institutions. The presence of non-medical scientific disciplines such as computer science was not that surprising as data sharing had to face major technical challenges. The results of term occurrences reflected the main parameters that govern data sharing in precision medicine but also its obstacles. Our study provided a picture of an emerging and interdisciplinary field that could be of interest to all stakeholders facing common challenges to promote data sharing in precision medicine.