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  • 1
    In: Clinical Lung Cancer, Elsevier BV, Vol. 19, No. 4 ( 2018-07), p. e441-e463
    Type of Medium: Online Resource
    ISSN: 1525-7304
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2018
    detail.hit.zdb_id: 2193644-4
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  • 2
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 12, No. 1 ( 2022-12-01)
    Abstract: In cirrhotic patients with hepatocellular carcinoma (HCC), right-sided radioembolization (RE) with Yttrium-90-loaded microspheres is an established palliative therapy and can be considered a “curative intention” treatment when aiming for sequential tumor resection. To become surgical candidate, hypertrophy of the left liver lobe to  〉  40% (future liver remnant, FLR) is mandatory, which can develop after RE. The amount of radiation-induced shrinkage of the right lobe and compensatory hypertrophy of the left lobe is difficult for clinicians to predict. This study aimed to utilize machine learning to predict left lobe liver hypertrophy in patients with HCC and cirrhosis scheduled for right lobe RE, with external validation. The results revealed that machine learning can accurately predict relative and absolute volume changes of the left liver lobe after right lobe RE. This prediction algorithm could help to estimate the chances of conversion from palliative RE to curative major hepatectomy following significant FLR hypertrophy.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2615211-3
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  • 3
    Online Resource
    Online Resource
    FapUNIFESP (SciELO) ; 2013
    In:  Journal of Applied Oral Science Vol. 21, No. 4 ( 2013-07), p. 307-313
    In: Journal of Applied Oral Science, FapUNIFESP (SciELO), Vol. 21, No. 4 ( 2013-07), p. 307-313
    Type of Medium: Online Resource
    ISSN: 1678-7757
    Language: Unknown
    Publisher: FapUNIFESP (SciELO)
    Publication Date: 2013
    detail.hit.zdb_id: 2152066-5
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  • 4
    Online Resource
    Online Resource
    Georg Thieme Verlag KG ; 2023
    In:  RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren
    In: RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren, Georg Thieme Verlag KG
    Abstract: Background In recent years, AI has made significant advancements in medical diagnosis and prognosis. However, the incorporation of AI into clinical practice is still challenging and under-appreciated. We aim to demonstrate a possible vertical integration approach to close the loop for AI-ready radiology. Method This study highlights the importance of two-way communication for AI-assisted radiology. As a key part of the methodology, it demonstrates the integration of AI systems into clinical practice with structured reports and AI visualization, giving more insight into the AI system. By integrating cooperative lifelong learning into the AI system, we ensure the long-term effectiveness of the AI system, while keeping the radiologist in the loop.  Results We demonstrate the use of lifelong learning for AI systems by incorporating AI visualization and structured reports. We evaluate Memory Aware-Synapses and Rehearsal approach and find that both approaches work in practice. Furthermore, we see the advantage of lifelong learning algorithms that do not require the storing or maintaining of samples from previous datasets. Conclusion In conclusion, incorporating AI into the clinical routine of radiology requires a two-way communication approach and seamless integration of the AI system, which we achieve with structured reports and visualization of the insight gained by the model. Closing the loop for radiology leads to successful integration, enabling lifelong learning for the AI system, which is crucial for sustainable long-term performance. Key Points: 
    Type of Medium: Online Resource
    ISSN: 1438-9029 , 1438-9010
    RVK:
    Language: English
    Publisher: Georg Thieme Verlag KG
    Publication Date: 2023
    detail.hit.zdb_id: 2031079-1
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  • 5
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2023
    In:  BMC Health Services Research Vol. 23, No. 1 ( 2023-07-06)
    In: BMC Health Services Research, Springer Science and Business Media LLC, Vol. 23, No. 1 ( 2023-07-06)
    Abstract: We present FHIR-PYrate, a Python package to handle the full clinical data collection and extraction process. The software is to be plugged into a modern hospital domain, where electronic patient records are used to handle the entire patient’s history. Most research institutes follow the same procedures to build study cohorts, but mainly in a non-standardized and repetitive way. As a result, researchers spend time writing boilerplate code, which could be used for more challenging tasks. Methods The package can improve and simplify existing processes in the clinical research environment. It collects all needed functionalities into a straightforward interface that can be used to query a FHIR server, download imaging studies and filter clinical documents. The full capacity of the search mechanism of the FHIR REST API is available to the user, leading to a uniform querying process for all resources, thus simplifying the customization of each use case. Additionally, valuable features like parallelization and filtering are included to make it more performant. Results As an exemplary practical application, the package can be used to analyze the prognostic significance of routine CT imaging and clinical data in breast cancer with tumor metastases in the lungs. In this example, the initial patient cohort is first collected using ICD-10 codes. For these patients, the survival information is also gathered. Some additional clinical data is retrieved, and CT scans of the thorax are downloaded. Finally, the survival analysis can be computed using a deep learning model with the CT scans, the TNM staging and positivity of relevant markers as input. This process may vary depending on the FHIR server and available clinical data, and can be customized to cover even more use cases. Conclusions FHIR-PYrate opens up the possibility to quickly and easily retrieve FHIR data, download image data, and search medical documents for keywords within a Python package. With the demonstrated functionality, FHIR-PYrate opens an easy way to assemble research collectives automatically.
    Type of Medium: Online Resource
    ISSN: 1472-6963
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2050434-2
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  • 6
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 12, No. 1 ( 2022-09-30)
    Abstract: The complex process of manual biomarker extraction from body composition analysis (BCA) has far restricted the analysis of SARS-CoV-2 outcomes to small patient cohorts and a limited number of tissue types. We investigate the association of two BCA-based biomarkers with the development of severe SARS-CoV-2 infections for 918 patients (354 female, 564 male) regarding disease severity and mortality (186 deceased). Multiple tissues, such as muscle, bone, or adipose tissue are used and acquired with a deep-learning-based, fully-automated BCA from computed tomography images of the chest. The BCA features and markers were univariately analyzed with a Shapiro–Wilk and two-sided Mann–Whitney-U test. In a multivariate approach, obtained markers were adjusted by a defined set of laboratory parameters promoted by other studies. Subsequently, the relationship between the markers and two endpoints, namely severity and mortality, was investigated with regard to statistical significance. The univariate approach showed that the muscle volume was significant for female ( p severity  ≤ 0.001, p mortality  ≤ 0.0001) and male patients ( p severity  = 0.018, p mortality  ≤ 0.0001) regarding the severity and mortality endpoints. For male patients, the intra- and intermuscular adipose tissue (IMAT) ( p  ≤ 0.0001), epicardial adipose tissue (EAT) ( p  ≤ 0.001) and pericardial adipose tissue (PAT) ( p  ≤ 0.0001) were significant regarding the severity outcome. With the mortality outcome, muscle ( p  ≤ 0.0001), IMAT ( p  ≤ 0.001), EAT ( p  = 0.011) and PAT ( p  = 0.003) remained significant. For female patients, bone ( p  ≤ 0.001), IMAT ( p  = 0.032) and PAT ( p  = 0.047) were significant in univariate analyses regarding the severity and bone ( p  = 0.005) regarding the mortality. Furthermore, the defined sarcopenia marker ( p  ≤ 0.0001, for female and male) was significant for both endpoints. The cardiac marker was significant for severity (p female  = 0.014, p male  ≤ 0.0001) and for mortality (p female  ≤ 0.0001, p male  ≤ 0.0001) endpoint for both genders. The multivariate logistic regression showed that the sarcopenia marker was significant ( p severity  = 0.006, p mortality  = 0.002) for both endpoints (OR severity  = 0.42, 95% CI severity : 0.23–0.78, OR mortality  = 0.34, 95% CI mortality : 0.17–0.67). The cardiac marker showed significance (p = 0.018) only for the severity endpoint (OR = 1.42, 95% CI 1.06–1.90). The association between BCA-based sarcopenia and cardiac biomarkers and disease severity and mortality suggests that these biomarkers can contribute to the risk stratification of SARS-CoV-2 patients. Patients with a higher cardiac marker and a lower sarcopenia marker are at risk for a severe course or death. Whether those biomarkers hold similar importance for other pneumonia-related diseases requires further investigation.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2615211-3
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