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  1. Article ; Online: Return to work after Post-COVID: describing affected employees' perceptions of personal resources, organizational offerings and care pathways.

    Straßburger, Claudia / Hieber, Daniel / Karthan, Maximilian / Jüster, Markus / Schobel, Johannes

    Frontiers in public health

    2023  Volume 11, Page(s) 1282507

    Abstract: Background: Most individuals recover from the acute phase of infection with the SARS-CoV-2 virus, however, some encounter prolonged effects, referred to as the Post-COVID syndrome. Evidence exists that such persistent symptoms can significantly impact ... ...

    Abstract Background: Most individuals recover from the acute phase of infection with the SARS-CoV-2 virus, however, some encounter prolonged effects, referred to as the Post-COVID syndrome. Evidence exists that such persistent symptoms can significantly impact patients' ability to return to work. This paper gives a comprehensive overview of different care pathways and resources, both personal and external, that aim to support Post-COVID patients during their work-life reintegration process. By describing the current situation of Post-COVID patients pertaining their transition back to the workplace, this paper provides valuable insights into their needs.
    Methods: A quantitative research design was applied using an online questionnaire as an instrument. Participants were recruited via Post-COVID outpatients, rehab facilities, general practitioners, support groups, and other healthcare facilities.
    Results: The analyses of 184 data sets of Post-COVID affected produced three key findings: (1) The evaluation of different types of personal resources that may lead to a successful return to work found that particularly the individuals' ability to cope with their situation (measured with the FERUS questionnaire), produced significant differences between participants that had returned to work and those that had not been able to return so far (F = 4.913,
    Conclusion: The results of the in-depth descriptive analysis allows to suggests that the level of ability to cope with the Post-COVID syndrome and its associated complaints, as well as the structural adaptation of the workplace to meet the needs and demands of patients better, might be important determinants of a successful return. While the latter might be addressed by employers directly, it might be helpful to integrate training on coping behavior early in care pathways and treatment plans for Post-COVID patients to strengthen their coping abilities aiming to support their successful return to work at an early stage.
    MeSH term(s) Humans ; COVID-19 ; Critical Pathways ; Return to Work ; SARS-CoV-2 ; Workplace
    Language English
    Publishing date 2023-11-27
    Publishing country Switzerland
    Document type Clinical Study ; Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2711781-9
    ISSN 2296-2565 ; 2296-2565
    ISSN (online) 2296-2565
    ISSN 2296-2565
    DOI 10.3389/fpubh.2023.1282507
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Brain Tumor Classification and Segmentation Using Dual-Outputs for U-Net Architecture: O2U-Net.

    Zargari, Seyed Aman / Kia, Zahra Sadat / Nickfarjam, Ali Mohammad / Hieber, Daniel / Holl, Felix

    Studies in health technology and informatics

    2023  Volume 305, Page(s) 93–96

    Abstract: We propose a modified version of the U-Net architecture for segmenting and classifying brain tumors, introducing another output between down- and up-sampling. Our proposed architecture utilizes two outputs, adding a classification output beside the ... ...

    Abstract We propose a modified version of the U-Net architecture for segmenting and classifying brain tumors, introducing another output between down- and up-sampling. Our proposed architecture utilizes two outputs, adding a classification output beside the segmentation output. The central idea is to use fully connected layers to classify each image before applying U-Net's up-sampling operations. This is achieved by utilizing the features extracted during the down-sampling procedure and combining them with fully connected layers for classification. Afterward, the segmented image is generated by U-Net's up-sampling process. Initial tests show competitive results against comparable models with 80.83%, 99.34%, and 77.39% for the dice coefficient, accuracy, and sensitivity, respectively. The tests were conducted on the well-established dataset from Nanfang Hospital, Guangzhou, China, and General Hospital, Tianjin Medical University, China, from 2005 to 2010 containing MRI images of 3064 brain tumors.
    MeSH term(s) Humans ; Brain ; Brain Neoplasms/diagnostic imaging ; China ; Hospitals, General ; Universities
    Language English
    Publishing date 2023-06-19
    Publishing country Netherlands
    Document type Journal Article
    ISSN 1879-8365
    ISSN (online) 1879-8365
    DOI 10.3233/SHTI230432
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Evaluation of Completeness, Comparability, Validity, and Timeliness in Cancer Registries: A Scoping Review.

    Shokrizadeharani, Leila / Batooli, Zahra / Heydarian, Saeedeh / Sharif, Reihane / Ghaderkhany, Shady / Tamehbidgoli, Maryam / Ataiejahanbegloo, Fatemehzahra / Hieber, Daniel / Kuhn, Peter

    Studies in health technology and informatics

    2023  Volume 305, Page(s) 160–163

    Abstract: An essential aspect of cancer registration is data quality. Data quality for Cancer Registries has been reviewed in this paper using four main criteria (comparability, validity, timeliness, and completeness). Medline (via PubMed), Scopus, and Web of ... ...

    Abstract An essential aspect of cancer registration is data quality. Data quality for Cancer Registries has been reviewed in this paper using four main criteria (comparability, validity, timeliness, and completeness). Medline (via PubMed), Scopus, and Web of Science databases were searched for relevant English articles published from inception until December 2022. Each study was analyzed for its characteristics, measurement method, and data quality features. According to the present study, the majority of articles evaluated the completeness feature, and the fewest evaluated the timeliness feature. A completeness rate of 36% to 99.3% and a timeliness rate of 9% to 98.5% were observed. Standardizing metrics and reporting of data quality is necessary to maintain confidence in the usefulness of cancer registries.
    MeSH term(s) Registries ; Benchmarking ; Data Accuracy ; Databases, Factual ; MEDLINE ; Neoplasms/diagnosis ; Neoplasms/epidemiology
    Language English
    Publishing date 2023-06-19
    Publishing country Netherlands
    Document type Review ; Journal Article
    ISSN 1879-8365
    ISSN (online) 1879-8365
    DOI 10.3233/SHTI230451
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Machine Learning Approaches for Detecting Coronary Artery Disease Using Angiography Imaging: A Scoping Review.

    Rangraz Jeddi, Fatemeh / Rajabi Moghaddam, Hasan / Sharif, Reihane / Heydarian, Saeedeh / Holl, Felix / Hieber, Daniel / Ghaderkhany, Shady

    Studies in health technology and informatics

    2023  Volume 305, Page(s) 244–248

    Abstract: This scoping review aims to identify and summarize the current literature on Machine learning (ML) approaches for detecting coronary artery disease (CAD) using angiography imaging. We comprehensively searched several databases and identified 23 studies ... ...

    Abstract This scoping review aims to identify and summarize the current literature on Machine learning (ML) approaches for detecting coronary artery disease (CAD) using angiography imaging. We comprehensively searched several databases and identified 23 studies that met the inclusion criteria. They employed different types of angiography imaging including computed tomography and invasive coronary angiography. Several studies have used deep learning algorithms for image classification and segmentation, and our findings show that various machine learning algorithms, such as convolutional neural networks, different types of U-Net, and hybrid approaches. Studies also varied in the outcomes measured, identifying stenosis, and assessing the severity of CAD. ML approaches can improve the accuracy and efficiency of CAD detection by using angiography. The performance of the algorithms differed depending on the dataset used, algorithm employed, and features selected for analysis. Therefore, there is a need to develop ML tools that can be easily integrated into clinical practice to aid in the diagnosis and management of CAD.
    MeSH term(s) Humans ; Coronary Artery Disease/diagnostic imaging ; Angiography ; Algorithms ; Databases, Factual ; Machine Learning
    Language English
    Publishing date 2023-06-19
    Publishing country Netherlands
    Document type Review ; Journal Article
    ISSN 1879-8365
    ISSN (online) 1879-8365
    DOI 10.3233/SHTI230474
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Multiscale quantification of morphological heterogeneity with creation of a predictor of longer survival in glioblastoma.

    Prokop, Georg / Wiestler, Benedikt / Hieber, Daniel / Withake, Fynn / Mayer, Karoline / Gempt, Jens / Delbridge, Claire / Schmidt-Graf, Friederike / Pfarr, Nicole / Märkl, Bruno / Schlegel, Jürgen / Liesche-Starnecker, Friederike

    International journal of cancer

    2023  Volume 153, Issue 9, Page(s) 1658–1670

    Abstract: Intratumor heterogeneity is a main cause of the dismal prognosis of glioblastoma (GBM). Yet, there remains a lack of a uniform assessment of the degree of heterogeneity. With a multiscale approach, we addressed the hypothesis that intratumor ... ...

    Abstract Intratumor heterogeneity is a main cause of the dismal prognosis of glioblastoma (GBM). Yet, there remains a lack of a uniform assessment of the degree of heterogeneity. With a multiscale approach, we addressed the hypothesis that intratumor heterogeneity exists on different levels comprising traditional regional analyses, but also innovative methods including computer-assisted analysis of tumor morphology combined with epigenomic data. With this aim, 157 biopsies of 37 patients with therapy-naive IDH-wildtype GBM were analyzed regarding the intratumor variance of protein expression of glial marker GFAP, microglia marker Iba1 and proliferation marker Mib1. Hematoxylin and eosin stained slides were evaluated for tumor vascularization. For the estimation of pixel intensity and nuclear profiling, automated analysis was used. Additionally, DNA methylation profiling was conducted separately for the single biopsies. Scoring systems were established to integrate several parameters into one score for the four examined modalities of heterogeneity (regional, cellular, pixel-level and epigenomic). As a result, we could show that heterogeneity was detected in all four modalities. Furthermore, for the regional, cellular and epigenomic level, we confirmed the results of earlier studies stating that a higher degree of heterogeneity is associated with poorer overall survival. To integrate all modalities into one score, we designed a predictor of longer survival, which showed a highly significant separation regarding the OS. In conclusion, multiscale intratumor heterogeneity exists in glioblastoma and its degree has an impact on overall survival. In future studies, the implementation of a broadly feasible heterogeneity index should be considered.
    MeSH term(s) Humans ; Glioblastoma/pathology ; Brain Neoplasms/pathology ; Prognosis
    Language English
    Publishing date 2023-07-28
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 218257-9
    ISSN 1097-0215 ; 0020-7136
    ISSN (online) 1097-0215
    ISSN 0020-7136
    DOI 10.1002/ijc.34665
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Conference proceedings: Evaluating the Segment Anything Model for Histopathological Tissue Segmentation

    Hieber, Daniel / Karthan, Maximilian / Holl, Felix / Prokop, Georg / Märkl, Bruno / Pryss, Rüdiger / Liesche-Starnecker, Friederike / Schobel, Johannes

    2023  , Page(s) Abstr. 159

    Event/congress 68. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS); Heilbronn; Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie; 2023
    Keywords Medizin, Gesundheit ; computer vision ; machine learning ; glioblastoma ; tumor segmentation
    Publishing date 2023-09-15
    Publisher German Medical Science GMS Publishing House; Düsseldorf
    Document type Conference proceedings
    DOI 10.3205/23gmds099
    Database German Medical Science

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