Stud Health Technol Inform. 2024 Aug 22;316:1463-1464. doi: 10.3233/SHTI240689.

ABSTRACT

This paper presents a versatile solution to formally represent the contents of electronic health records. It is based on the knowledge graph paradigm, and semantic web standards RDF and OWL. It employs the established semantic standards SNOMED CT and FHIR, which warrant international interoperability. A graph-based form is not only useful to feed different target visualizations, but it can also be subject to AI-powered services such as (fuzzy) retrieval and summarization.

PMID:39176479 | DOI:10.3233/SHTI240689

Nat Hum Behav. 2024 Aug 22. doi: 10.1038/s41562-024-01957-x. Online ahead of print.

ABSTRACT

How does scientific knowledge grow? This question has occupied a central place in the philosophy of science, stimulating heated debates but yielding no clear consensus. Many explanations can be understood in terms of whether and how they view the expansion of knowledge as proceeding through the accretion of scientific concepts into larger conceptual structures. Here we examine these views empirically by analysing 2,605,224 papers spanning five decades from both the social sciences (Web of Science) and the physical sciences (American Physical Society). Using natural language processing techniques, we create semantic networks of concepts, wherein noun phrases become linked when used in the same paper abstract. We then detect the core/periphery structures of these networks, wherein core concepts are densely connected sets of highly central nodes and periphery concepts are sparsely connected nodes that are highly connected to the core. For both the social and physical sciences, we observe increasingly rigid conceptual cores accompanied by the proliferation of periphery concepts. Subsequently, we examine the relationship between conceptual structure and the growth of scientific knowledge, finding that scientific works are more innovative in fields with cores that have higher conceptual churn and with larger cores. Furthermore, scientific consensus is associated with reduced conceptual churn and fewer conceptual cores. Overall, our findings suggest that while the organization of scientific concepts is important for the growth of knowledge, the mechanisms vary across time.

PMID:39174726 | DOI:10.1038/s41562-024-01957-x

BMC Infect Dis. 2024 Aug 15;24(1):828. doi: 10.1186/s12879-024-09736-9.

ABSTRACT

BACKGROUND: Malaria is a serious, fatal disease, and a high-risk determinant for human health globally. Children, pregnant women, and migrants are vulnerable groups for malaria infection in African regions. Recently, malaria is an endemic disease in Ethiopia.

OBJECTIVES: This study aimed to determine the pooled prevalence of malaria and its determinant factors among the most vulnerable populations in Ethiopia.

METHODS: Electronic databases, including PubMed, Google Scholar, Web of Science, Semantic Scholar, and Scopus were used for searching articles published since the 2020 Gregorian calendar and onwards. All peer-reviewed Ethiopian journals, health institutions, and Universities were considered for article searching. A PRISMA flow chart and Endnote software were used for article screening, and to remove duplications, respectively. The modified version of the Newcastle-Ottawa Scale was used for potential risk of bias assessments. The heterogeneity among the included studies was evaluated using the indicator of heterogeneity (I2). Egger's test and funnel plot were used to examine the possible publication bias. A random-effects analysis was used to assess the pooled prevalence of malaria, and its determinant factors with a 95% CI. The screening process, data extraction, and quality assessment were done independently, and any disagreements were resolved through discussions.

RESULTS: A total of twelve studies were included in this study. The pooled malaria prevalence was 11.10% (95% CI: 6.10, 16.11). Stagnant water (AOR: 4.19, 95% CI: 2.47, 7.11), no insecticide-treated net utilization (AOR: 3.15, 95% CI: 1.73, 5.73), and staying outdoors at night (AOR: 5.19, 95% CI: 2.08, 12.94) were the pooled estimated statistically risk factors for malaria prevalence. Whereas, insecticide-treated bed net utilization (AOR: 1.59, 95% CI: 0.23, 10.95) reduces the risk of malaria infection.

CONCLUSIONS: The pooled prevalence of malaria is high among vulnerable populations. Creating awareness regarding utilization of insecticide-treated bed nets, and draining stagnant water from the environment are possible interventions to reduce the prevalence of malaria.

PMID:39148027 | DOI:10.1186/s12879-024-09736-9

JMIR Hum Factors. 2024 Aug 15;11:e57670. doi: 10.2196/57670.

ABSTRACT

BACKGROUND: The rapid growth of web-based medical services has highlighted the significance of smart triage systems in helping patients find the most appropriate physicians. However, traditional triage methods often rely on department recommendations and are insufficient to accurately match patients' textual questions with physicians' specialties. Therefore, there is an urgent need to develop algorithms for recommending physicians.

OBJECTIVE: This study aims to develop and validate a patient-physician hybrid recommendation (PPHR) model with response metrics for better triage performance.

METHODS: A total of 646,383 web-based medical consultation records from the Internet Hospital of the First Affiliated Hospital of Xiamen University were collected. Semantic features representing patients and physicians were developed to identify the set of most similar questions and semantically expand the pool of recommended physician candidates, respectively. The physicians' response rate feature was designed to improve candidate rankings. These 3 characteristics combine to create the PPHR model. Overall, 5 physicians participated in the evaluation of the efficiency of the PPHR model through multiple metrics and questionnaires as well as the performance of Sentence Bidirectional Encoder Representations from Transformers and Doc2Vec in text embedding.

RESULTS: The PPHR model reaches the best recommendation performance when the number of recommended physicians is 14. At this point, the model has an F1-score of 76.25%, a proportion of high-quality services of 41.05%, and a rating of 3.90. After removing physicians' characteristics and response rates from the PPHR model, the F1-score decreased by 12.05%, the proportion of high-quality services fell by 10.87%, the average hit ratio dropped by 1.06%, and the rating declined by 11.43%. According to whether those 5 physicians were recommended by the PPHR model, Sentence Bidirectional Encoder Representations from Transformers achieved an average hit ratio of 88.6%, while Doc2Vec achieved an average hit ratio of 53.4%.

CONCLUSIONS: The PPHR model uses semantic features and response metrics to enable patients to accurately find the physician who best suits their needs.

PMID:39146009 | DOI:10.2196/57670

Disabil Rehabil Assist Technol. 2024 Aug 14:1-10. doi: 10.1080/17483107.2024.2387101. Online ahead of print.

ABSTRACT

INTRODUCTION: A modern and accessible healthcare system requires digital innovation and connectivity. The term "Digital health" covers vide range technologies, such as mobile health and applications, electronic records, telehealth and telemedicine, wearable devices, robotics, virtual reality and artificial intelligence.

METHODS: Scientometrics is the method that we have done in this study by Cite Space and VOSviewer software, and the result of searching the Web of Science database in plain text format to perform analysis and scientometrics and create outputs in the form of graphs and tables in the field of digital health has been used in stroke rehabilitation.

RESULT: A total of 2933 documents related to digital health technologies in stroke rehabilitation were identified by searching for the terms "stroke rehabilitation" or "stroke recovery" in the title and "digital health" across all fields. The strongest citations related to cerebrovascular disease spanned from 1994 to 2007, with randomised clinical trials occurring almost simultaneously and ended by 2012. Consequently, stroke rehabilitation by virtual reality technology has obtained the most citations and clinical trials and as an important part of digital health in the future research process.

CONCLUSION: This scientometric study offers insights into how digital health technology can assist stroke patients in self-managing their health and well-being, in addition to supporting integrated stroke rehabilitation. The analysis revealed that three themes were present: author contributors and collaboration networks, temporal evolution, the strongest citation explosions for digital health technologies in stroke rehabilitation research, and semantic analysis.

PMID:39140131 | DOI:10.1080/17483107.2024.2387101

Psychol Res. 2024 Aug 6. doi: 10.1007/s00426-024-02018-8. Online ahead of print.

ABSTRACT

In real-world scenes, the different objects and events are often interconnected within a rich web of semantic relationships. These semantic links help parse information efficiently and make sense of the sensory environment. It has been shown that, during goal-directed search, hearing the characteristic sound of an everyday life object helps finding the affiliate objects in artificial visual search arrays as well as in naturalistic, real-life videoclips. However, whether crossmodal semantic congruence also triggers orienting during spontaneous, not goal-directed observation is unknown. Here, we investigated this question addressing whether crossmodal semantic congruence can attract spontaneous, overt visual attention when viewing naturalistic, dynamic scenes. We used eye-tracking whilst participants (N = 45) watched video clips presented alongside sounds of varying semantic relatedness with objects present within the scene. We found that characteristic sounds increased the probability of looking at, the number of fixations to, and the total dwell time on semantically corresponding visual objects, in comparison to when the same scenes were presented with semantically neutral sounds or just with background noise only. Interestingly, hearing object sounds not met with an object in the scene led to increased visual exploration. These results suggest that crossmodal semantic information has an impact on spontaneous gaze on realistic scenes, and therefore on how information is sampled. Our findings extend beyond known effects of object-based crossmodal interactions with simple stimuli arrays and shed new light on the role that audio-visual semantic relationships out in the perception of everyday life scenarios.

PMID:39105825 | DOI:10.1007/s00426-024-02018-8

J Palliat Med. 2024 Aug 2. doi: 10.1089/jpm.2024.0007. Online ahead of print.

ABSTRACT

Background and Objectives: Spiritual care is an essential component of care for the terminally ill, because of its potential to positively impact patient perception of quality of life and dignity. However, it continues to be the least cultivated or even most overlooked aspect of palliative care and end of life. We performed a methodological review using bibliometric analysis to provide a holistic view of the scientific output published on this topic in the literature at the same time outlining present perspectives and research trends. Methods: In accordance with the BIBLIO checklist for reporting the bibliometric reviews of the biomedical literature, pertinent articles were retrieved from the Web of Science (WOS) database. The search string included "spiritual care," "end of life," and their synonyms. The VOSviewer (version 1.6.17) software was used to conduct comprehensive analyses. Semantic and research networks, bibliographic coupling, and journal analysis were examined. Results: A total of 924 articles were identified in WOS, and 842 were retrieved. An increasing trend in the number of publications is observed from 1981 to date, with a peak in the 2019-2021 timeframe. Most articles focused on palliative care, spirituality, spiritual care, religion, end of life, and cancer. The Journal of Pain and Symptom Management contributed the highest number of published documents, while the Journal of Palliative Medicine was the top-cited journal. The highest number of publications originated from collaborations of authors from the United Kingdom, the United States, and Australia. Conclusion: The remarkable increase in the number of publications on spiritual care observed in the years of the COVID-19 pandemic likely reflected global concerns, reasserting the importance of prioritizing spiritual care for whole-person palliation. Spiritual care is integrated with palliative care, in line with the latter's holistic nature and the recognition of spirituality as a fundamental aspect of end-of-life care. Nurses and chaplains exhibited more involvement in palliative-spiritual care than physicians reflecting the belief that chaplains are perceived as specialized providers, and nurses, owing to their direct exposure to spiritual suffering and ethos, are deemed suitable for providing spiritual care.

PMID:39093919 | DOI:10.1089/jpm.2024.0007

Clin J Gastroenterol. 2024 Jul 30. doi: 10.1007/s12328-024-02010-0. Online ahead of print.

ABSTRACT

We present a unique case of a prepyloric gastric muscular ring, a pathology distinct from a gastric web. There is scarcity of literature on this topic, nearly all cases of prepyloric antral rings or webs published in literature are mucosal or submucosal in nature with no evidence of muscle hypertrophy. Given the prevalence of pyloric stenosis as the most common gastric outlet malformation in neonates, gastric rings and webs are not readily considered in the differential diagnosis of gastric outlet obstruction. While most cases of gastric outlet obstruction are diagnosed radiologically, less common pathologies will be confirmed with direct visual inspection during surgery. The term "congenital gastric outlet obstruction" has been used to encompass rare cases, making it appropriate to include a muscular ring in this category. We propose the term "gastric ring" be used with a semantic modifier of "muscular" versus "submucosal/mucosal" to avoid confusion.

PMID:39080179 | DOI:10.1007/s12328-024-02010-0

J Pers Med. 2024 Jun 24;14(7):676. doi: 10.3390/jpm14070676.

ABSTRACT

Communication and cooperation are fundamental for the correct deployment of P5 medicine, and this can be achieved only by correct comprehension of semantics so that it can aspire to medical knowledge sharing. There is a hierarchy in the operations that need to be performed to achieve this goal that brings to the forefront the complete understanding of the real-world business system by domain experts using Domain Ontologies, and only in the last instance acknowledges the specific transformation at the pure information and communication technology level. A specific feature that should be maintained during such types of transformations is versioning that aims to record the evolution of meanings in time as well as the management of their historical evolution. The main tool used to represent ontology in computing environments is the Ontology Web Language (OWL), but it was not created for managing the evolution of meanings in time. Therefore, we tried, in this paper, to find a way to use the specific features of Common Terminology Service-Release 2 (CTS2) to perform consistent and validated transformations of ontologies written in OWL. The specific use case managed in the paper is the Alzheimer's Disease Ontology (ADO). We were able to consider all of the elements of ADO and map them with CTS2 terminological resources, except for a subset of elements such as the equivalent class derived from restrictions on other classes.

PMID:39063930 | DOI:10.3390/jpm14070676

Comput Methods Programs Biomed. 2024 Jul 18;255:108326. doi: 10.1016/j.cmpb.2024.108326. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: Researchers commonly use automated solutions such as Natural Language Processing (NLP) systems to extract clinical information from large volumes of unstructured data. However, clinical text's poor semantic structure and domain-specific vocabulary can make it challenging to develop a one-size-fits-all solution. Large Language Models (LLMs), such as OpenAI's Generative Pre-Trained Transformer 3 (GPT-3), offer a promising solution for capturing and standardizing unstructured clinical information. This study evaluated the performance of InstructGPT, a family of models derived from LLM GPT-3, to extract relevant patient information from medical case reports and discussed the advantages and disadvantages of LLMs versus dedicated NLP methods.

METHODS: In this paper, 208 articles related to case reports of foreign body injuries in children were identified by searching PubMed, Scopus, and Web of Science. A reviewer manually extracted information on sex, age, the object that caused the injury, and the injured body part for each patient to build a gold standard to compare the performance of InstructGPT.

RESULTS: InstructGPT achieved high accuracy in classifying the sex, age, object and body part involved in the injury, with 94%, 82%, 94% and 89%, respectively. When excluding articles for which InstructGPT could not retrieve any information, the accuracy for determining the child's sex and age improved to 97%, and the accuracy for identifying the injured body part improved to 93%. InstructGPT was also able to extract information from non-English language articles.

CONCLUSIONS: The study highlights that LLMs have the potential to eliminate the necessity for task-specific training (zero-shot extraction), allowing the retrieval of clinical information from unstructured natural language text, particularly from published scientific literature like case reports, by directly utilizing the PDF file of the article without any pre-processing and without requiring any technical expertise in NLP or Machine Learning. The diverse nature of the corpus, which includes articles written in languages other than English, some of which contain a wide range of clinical details while others lack information, adds to the strength of the study.

PMID:39029416 | DOI:10.1016/j.cmpb.2024.108326

Ann Nucl Med. 2024 Jul 19. doi: 10.1007/s12149-024-01958-w. Online ahead of print.

ABSTRACT

Primary progressive aphasia (PPA) is a disease known to affect the frontal and temporal regions of the left hemisphere. PPA is often an indication of future development of dementia, specifically semantic dementia (SD) for frontotemporal dementia (FTD) and logopenic progressive aphasia (LPA) as an atypical presentation of Alzheimer's disease (AD). The purpose of this review is to clarify the value of 2-deoxy-2-[18F]fluoro-D-glucose (FDG)-positron emission tomography (PET) in the detection and diagnosis of PPA. A comprehensive review of literature was conducted using Web of Science, PubMed, and Google Scholar. The three PPA subtypes show distinct regions of hypometabolism in FDG-PET imaging with SD in the anterior temporal lobes, LPA in the left temporo-parietal junction, and nonfluent/agrammatic Variant PPA (nfvPPA) in the left inferior frontal gyrus and insula. Despite the distinct patterns, overlapping hypometabolic areas can complicate differential diagnosis, especially in patients with SD who are frequently diagnosed with AD. Integration with other diagnostic tools could refine the diagnostic process and lead to improved patient outcomes. Future research should focus on validating these findings in larger populations and exploring the therapeutic implications of early, accurate PPA diagnosis with more targeted therapeutic interventions.

PMID:39028529 | DOI:10.1007/s12149-024-01958-w

Gigascience. 2024 Jan 2;13:giae033. doi: 10.1093/gigascience/giae033.

ABSTRACT

BACKGROUND: As biological data increase, we need additional infrastructure to share them and promote interoperability. While major effort has been put into sharing data, relatively less emphasis is placed on sharing metadata. Yet, sharing metadata is also important and in some ways has a wider scope than sharing data themselves.

RESULTS: Here, we present PEPhub, an approach to improve sharing and interoperability of biological metadata. PEPhub provides an API, natural-language search, and user-friendly web-based sharing and editing of sample metadata tables. We used PEPhub to process more than 100,000 published biological research projects and index them with fast semantic natural-language search. PEPhub thus provides a fast and user-friendly way to finding existing biological research data or to share new data.

AVAILABILITY: https://pephub.databio.org.

PMID:38991851 | DOI:10.1093/gigascience/giae033

JMIR Res Protoc. 2024 Jul 11;13:e56608. doi: 10.2196/56608.

ABSTRACT

BACKGROUND: Social communication is a key factor in maintaining cognitive function and contributes to well-being in later life.

OBJECTIVE: This study will examine the effects of "Photo-Integrated Conversation Moderated by Application version 2" (PICMOA-2), which is a web-based conversational intervention, on cognitive performance, frailty, and social and psychological indicators among community-dwelling older adults.

METHODS: This study is a randomized controlled trial with an open-label, 2-parallel group trial and 1:1 allocation design. Community dwellers aged 65 years and older were enrolled in the trial and divided into the intervention and control groups. The intervention group receives the PICMOA-2 program, a web-based group conversation, once every 2 weeks for 6 months. The primary outcome is verbal fluency, including phonemic and semantic fluency. The secondary outcomes are other neuropsychiatric batteries, including the Mini-Mental State Examination, Logical Memory (immediate and delay), verbal paired associates, and comprehensive functional status evaluated by questionnaires, including frailty, social status, and well-being. The effect of the intervention will be examined using a mixed linear model. As a secondary aim, we will test whether the intervention effects vary with the covariates at baseline to examine the effective target attributes.

RESULTS: Recruitment was completed in July 2023. A total of 66 participants were randomly allocated to intervention or control groups. As of January 1, 2024, the intervention is ongoing. Participants are expected to complete the intervention at the end of February 2024, and the postintervention evaluation will be conducted in March 2024.

CONCLUSIONS: This protocol outlines the randomized controlled trial study design evaluating the effect of a 6-month intervention with PICMOA-2. This study will provide evidence on the effectiveness of social interventions on cognitive function and identify effective target images for remote social intervention.

TRIAL REGISTRATION: UMIN Clinical Trials UMIN000050877; https://tinyurl.com/5eahsy66.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/56608.

PMID:38990615 | DOI:10.2196/56608

Front Immunol. 2024 Jun 20;15:1393839. doi: 10.3389/fimmu.2024.1393839. eCollection 2024.

ABSTRACT

INTRODUCTION: Therapeutic monoclonal antibodies (mAbs) have demonstrated promising outcomes in diverse clinical indications, including but not limited to graft rejection, cancer, and autoimmune diseases lately.Recognizing the crucial need for the scientific community to quickly and easily access dependable information on monoclonal antibodies (mAbs), IMGT®, the international ImMunoGeneTics information system®, provides a unique and invaluable resource: IMGT/mAb-DB, a comprehensive database of therapeutic mAbs, accessible via a user-friendly web interface. However, this approach restricts more sophisticated queries and segregates information from other databases.

METHODS: To connect IMGT/mAb-DB with the rest of the IMGT databases, we created IMGT/mAb-KG, a knowledge graph for therapeutic monoclonal antibodies connected to IMGT structures and genomics databases. IMGT/mAb-KG is developed using the most effective methodologies and standards of semantic web and acquires data from IMGT/mAb-DB. Concerning interoperability, IMGT/mAb-KG reuses terms from biomedical resources and is connected to related resources.

RESULTS AND DISCUSSION: In February 2024, IMGT/mAb-KG, encompassing a total of 139,629 triplets, provides access to 1,489 mAbs, approximately 500 targets, and over 500 clinical indications. It offers detailed insights into the mechanisms of action of mAbs, their construction, and their various products and associated studies. Linked to other resources such as Thera-SAbDab (Therapeutic Structural Antibody Database), PharmGKB (a comprehensive resource curating knowledge on the impact of genetic variation on drug response), PubMed, and HGNC (HUGO Gene Nomenclature Committee), IMGT/mAb-KG is an essential resource for mAb development. A user-friendly web interface facilitates the exploration and analyse of the content of IMGT/mAb-KG.

PMID:38975336 | PMC:PMC11225432 | DOI:10.3389/fimmu.2024.1393839

Sensors (Basel). 2024 Jun 9;24(12):3761. doi: 10.3390/s24123761.

ABSTRACT

The growing interest in building data management, especially the building information model (BIM), has significantly influenced urban management, materials supply chain analysis, documentation, and storage. However, the integration of BIM into 3D GIS tools is becoming more common, showing progress beyond the traditional problem. To address this, this study proposes data transformation methods involving mapping between three domains: industry foundation classes (IFC), city geometry markup language (CityGML), and web ontology framework (OWL)/resource description framework (RDF). Initially, IFC data are converted to CityGML format using the feature manipulation engine (FME) at CityGML standard's levels of detail 4 (LOD4) to enhance BIM data interoperability. Subsequently, CityGML is converted to the OWL/RDF diagram format to validate the proposed BIM conversion process. To ensure integration between BIM and GIS, geometric data and information are visualized through Cesium Ion web services and Unreal Engine. Additionally, an RDF graph is applied to analyze the association between the semantic mapping of the CityGML standard, with Neo4j (a graph database management system) utilized for visualization. The study's results demonstrate that the proposed data transformation methods significantly improve the interoperability and visualization of 3D city models, facilitating better urban management and planning.

PMID:38931546 | DOI:10.3390/s24123761

J Clin Exp Neuropsychol. 2024 Jun 21:1-30. doi: 10.1080/13803395.2024.2331827. Online ahead of print.

ABSTRACT

INTRODUCTION: Semantic verbal fluency (SVF) is a widely used measure of frontal executive function and access to semantic memory. SVF scoring metrics include the number of unique words generated, perseverations, intrusions, semantic cluster size and switching between clusters, and scores vary depending on the language the test is administered in. In this paper, we review the existing normative data for Turkish, the main metrics used for scoring SVF data in Turkish, and the most frequently used categories.

METHOD: We conducted a systematic review of peer-reviewed papers using Medline, EMBASE, PsycInfo, Web of Science, and two Turkish databases, TR-Dizin and Yok-Tez. Included papers contained data on the SVF performance of healthy adult native speakers of Turkish, and reported the categories used. Versions of the SVF that required participants to alternate categories were excluded. We extracted and tabulated demographics, descriptions of groups, metrics used, categories used, and sources of normative data. Studies were assessed for level of detail in reporting findings.

RESULTS: 1400 studies were retrieved. After deduplication, abstract, full text screening, and merging of theses with their published versions, 121 studies were included. 114 studies used the semantic category "animal", followed by first names (N = 14, 12%). All studies reported word count. More complex measures were rare (perseverations: N = 12, 10%, clustering and switching: N = 5, 4%). Four of seven normative studies reported only word count, two also measured perseverations, and one reported category violations and perseverations. Two normative studies were published in English.

CONCLUSIONS: There is a lack of normative Turkish SVF data with more complex metrics, such as clustering and switching, and a lack of normative data published in English. Given the size of the Turkish diaspora, normative SVF data should include monolingual and bilingual speakers. Limitations include a restriction to key English and Turkish databases.

PMID:38904178 | DOI:10.1080/13803395.2024.2331827

Eur Rev Med Pharmacol Sci. 2024 Jun;28(11):3702-3710. doi: 10.26355/eurrev_202406_36375.

ABSTRACT

OBJECTIVE: Monitoring Jackson Pratt and Hemovac drains plays a crucial role in assessing a patient's recovery and identifying potential postoperative complications. Accurate and regular monitoring of the blood volume in the drain is essential for making decisions about patient care. However, transferring blood to a measuring cup and recording it is a challenging task for both patients and doctors, exposing them to bloodborne pathogens such as the human immunodeficiency virus (HIV), hepatitis B virus (HBV), and hepatitis C virus (HCV). To automate the recording process with a non-contact approach, we propose an innovative approach that utilizes deep learning techniques to detect a drain in a photograph, compute the blood level in the drain, estimate the blood volume, and display the results on both web and mobile interfaces.

MATERIALS AND METHODS: Our system employs semantic segmentation on images taken with mobile phones to effectively isolate the blood-filled portion of the drain from the rest of the image and compute the blood volume. These results are then sent to mobile and web applications for convenient access. To validate the accuracy and effectiveness of our system, we collected the Drain Dataset, which consists of 1,004 images taken under various background and lighting conditions.

RESULTS: With an average error rate of less than 5% in milliliters, our proposed approach achieves highly accurate blood level detection and estimation, as demonstrated by our trials on this dataset. The system also exhibits robustness to variations in lighting conditions and drain shapes, ensuring its applicability in different clinical scenarios.

CONCLUSIONS: The proposed automated blood volume estimation system can significantly reduce the time and effort required for manual measurements, enabling healthcare professionals to focus on other critical tasks. The dataset and annotations are available at: https://www.kaggle.com/datasets/ayenahin/liquid-volume-detection-from-drain-images and the code for the web application is available at https://github.com/itsjustaplant/AwesomeProject.git.

PMID:38884505 | DOI:10.26355/eurrev_202406_36375

Int J Telemed Appl. 2024 Jun 6;2024:5578056. doi: 10.1155/2024/5578056. eCollection 2024.

ABSTRACT

Introduction: Teleradiology allows distant facilities to electronically transmit images for interpretation, thereby bridging the radiology service gap between urban and rural areas. The technology improves healthcare quality, treatment options, and diagnostic accuracy. However, in low resource settings like Ethiopia, teleradiology services are limited, posing challenges for implementation. Therefore, this study is aimed at exploring the factors that facilitated or hindered the implementation of web-based teleradiology in the public hospitals of the South Gondar Zone, Northwest Ethiopia. Methods: In this study, a purposive sampling method was employed to select seventeen participants, including hospital managers, physicians, emergency surgeons, and radiologists, for an in-depth interview (IDI). The interviews were conducted from March to May 2023. A reflexive thematic analysis was conducted using an abductive coding technique at the semantic/explicit level. Data were collected through semistructured interviews conducted face-to-face and virtually, with audio recordings transcribed, translated, and analyzed using Open Code version 4.02 software. Trustworthiness was ensured through prolonged engagement, reflective journaling, and review by coauthors. Results: The study examined eight main themes, with barriers to sustainable teleradiology implementation falling into five categories: technological, organizational, environmental, individual, and workflow and communication. Conversely, identified facilitators included improved radiology service efficiency, system accessibility, collaboration opportunities, and user trust in the radiology ecosystem. Within each theme, factors with potential impacts on teleradiology system sustainability were identified, such as the lack of system handover mechanisms, absence of a central image consultation center, and inadequate staffing of full-time radiologists and technical personnel. Conclusions: The study highlights the positive user perception of a web-based teleradiology system's user-friendliness and efficiency. Overcoming challenges and leveraging facilitators are crucial for optimizing teleradiology and improving service delivery and patient outcomes. A centralized consultation center with dedicated radiologists and technical personnel is recommended for maximizing efficiency.

PMID:38883327 | PMC:PMC11178418 | DOI:10.1155/2024/5578056

Transl Cancer Res. 2024 May 31;13(5):2544-2560. doi: 10.21037/tcr-23-964. Epub 2024 May 22.

ABSTRACT

BACKGROUND AND OBJECTIVE: Cancer is a leading cause of morbidity and mortality worldwide. The emergence of digital pathology and deep learning technologies signifies a transformative era in healthcare. These technologies can enhance cancer detection, streamline operations, and bolster patient care. A substantial gap exists between the development phase of deep learning models in controlled laboratory environments and their translations into clinical practice. This narrative review evaluates the current landscape of deep learning and digital pathology, analyzing the factors influencing model development and implementation into clinical practice.

METHODS: We searched multiple databases, including Web of Science, Arxiv, MedRxiv, BioRxiv, Embase, PubMed, DBLP, Google Scholar, IEEE Xplore, Semantic Scholar, and Cochrane, targeting articles on whole slide imaging and deep learning published from 2014 and 2023. Out of 776 articles identified based on inclusion criteria, we selected 36 papers for the analysis.

KEY CONTENT AND FINDINGS: Most articles in this review focus on the in-laboratory phase of deep learning model development, a critical stage in the deep learning lifecycle. Challenges arise during model development and their integration into clinical practice. Notably, lab performance metrics may not always match real-world clinical outcomes. As technology advances and regulations evolve, we expect more clinical trials to bridge this performance gap and validate deep learning models' effectiveness in clinical care. High clinical accuracy is vital for informed decision-making throughout a patient's cancer care.

CONCLUSIONS: Deep learning technology can enhance cancer detection, clinical workflows, and patient care. Challenges may arise during model development. The deep learning lifecycle involves data preprocessing, model development, and clinical implementation. Achieving health equity requires including diverse patient groups and eliminating bias during implementation. While model development is integral, most articles focus on the pre-deployment phase. Future longitudinal studies are crucial for validating models in real-world settings post-deployment. A collaborative approach among computational pathologists, technologists, industry, and healthcare providers is essential for driving adoption in clinical settings.

PMID:38881914 | PMC:PMC11170525 | DOI:10.21037/tcr-23-964

Res Synth Methods. 2024 Jun 14. doi: 10.1002/jrsm.1729. Online ahead of print.

ABSTRACT

Citation indices providing information on backward citation (BWC) and forward citation (FWC) links are essential for literature discovery, bibliographic analysis, and knowledge synthesis, especially when language barriers impede document identification. However, the suitability of citation indices varies. While some have been analyzed, the majority, whether new or established, lack comprehensive evaluation. Therefore, this study evaluates the citation coverage of the citation indices of 59 databases, encompassing the widely used Google Scholar, Scopus, and Web of Science alongside many others never previously analyzed, such as the emerging Lens, Scite, Dimensions, and OpenAlex or the subject-specific PubMed and JSTOR. Through a comprehensive analysis using 259 journal articles from across disciplines, this research aims to guide scholars in selecting indices with broader document coverage and more accurate and comprehensive backward and forward citation links. Key findings highlight Google Scholar, ResearchGate, Semantic Scholar, and Lens as leading options for FWC searching, with Lens providing superior download capabilities. For BWC searching, the Web of Science Core Collection can be recommended over Scopus for accuracy. BWC information from publisher databases such as IEEE Xplore or ScienceDirect was generally found to be the most accurate, yet only available for a limited number of articles. The findings will help scholars conducting systematic reviews, meta-analyses, and bibliometric analyses to select the most suitable databases for citation searching.

PMID:38877607 | DOI:10.1002/jrsm.1729