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  1. Article ; Online: Authors' Reply.

    Elhakim, Tarig / Som, Avik

    Journal of the American College of Radiology : JACR

    2024  

    Language English
    Publishing date 2024-02-01
    Publishing country United States
    Document type Letter
    ZDB-ID 2274861-1
    ISSN 1558-349X ; 1546-1440
    ISSN (online) 1558-349X
    ISSN 1546-1440
    DOI 10.1016/j.jacr.2024.01.016
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 6636: Show issues Location:
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  2. Article: Role of artificial intelligence in risk prediction, prognostication, and therapy response assessment in colorectal cancer: current state and future directions.

    Mansur, Arian / Saleem, Zain / Elhakim, Tarig / Daye, Dania

    Frontiers in oncology

    2023  Volume 13, Page(s) 1065402

    Abstract: Artificial Intelligence (AI) is a branch of computer science that utilizes optimization, probabilistic and statistical approaches to analyze and make predictions based on a vast amount of data. In recent years, AI has revolutionized the field of oncology ...

    Abstract Artificial Intelligence (AI) is a branch of computer science that utilizes optimization, probabilistic and statistical approaches to analyze and make predictions based on a vast amount of data. In recent years, AI has revolutionized the field of oncology and spearheaded novel approaches in the management of various cancers, including colorectal cancer (CRC). Notably, the applications of AI to diagnose, prognosticate, and predict response to therapy in CRC, is gaining traction and proving to be promising. There have also been several advancements in AI technologies to help predict metastases in CRC and in Computer-Aided Detection (CAD) Systems to improve miss rates for colorectal neoplasia. This article provides a comprehensive review of the role of AI in predicting risk, prognosis, and response to therapies among patients with CRC.
    Language English
    Publishing date 2023-01-25
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 2649216-7
    ISSN 2234-943X
    ISSN 2234-943X
    DOI 10.3389/fonc.2023.1065402
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: Role of Machine Learning-Based CT Body Composition in Risk Prediction and Prognostication: Current State and Future Directions.

    Elhakim, Tarig / Trinh, Kelly / Mansur, Arian / Bridge, Christopher / Daye, Dania

    Diagnostics (Basel, Switzerland)

    2023  Volume 13, Issue 5

    Abstract: CT body composition analysis has been shown to play an important role in predicting health and has the potential to improve patient outcomes if implemented clinically. Recent advances in artificial intelligence and machine learning have led to high speed ...

    Abstract CT body composition analysis has been shown to play an important role in predicting health and has the potential to improve patient outcomes if implemented clinically. Recent advances in artificial intelligence and machine learning have led to high speed and accuracy for extracting body composition metrics from CT scans. These may inform preoperative interventions and guide treatment planning. This review aims to discuss the clinical applications of CT body composition in clinical practice, as it moves towards widespread clinical implementation.
    Language English
    Publishing date 2023-03-03
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 2662336-5
    ISSN 2075-4418
    ISSN 2075-4418
    DOI 10.3390/diagnostics13050968
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Spontaneous pneumomediastinum, pneumothorax and subcutaneous emphysema in COVID-19 pneumonia: a rare case and literature review.

    Elhakim, Tarig Sami / Abdul, Haleem S / Pelaez Romero, Carlos / Rodriguez-Fuentes, Yoandy

    BMJ case reports

    2020  Volume 13, Issue 12

    Abstract: Spontaneous pneumomediastinum (SPM) and pneumothorax (PNX) unrelated to positive pressure ventilation has been recently reported as an unusual complication in cases of severe COVID-19 pneumonia. The presumed pathophysiological mechanism is diffuse ... ...

    Abstract Spontaneous pneumomediastinum (SPM) and pneumothorax (PNX) unrelated to positive pressure ventilation has been recently reported as an unusual complication in cases of severe COVID-19 pneumonia. The presumed pathophysiological mechanism is diffuse alveolar injury leading to alveolar rupture and air leak. We present a case of COVID-19 pneumonia complicated on day 13 post admission by SPM, PNX and subcutaneous emphysema in a patient with no identifiable risk factors for such complication. The patient received medical treatment for his COVID-19 infection without the use of an invasive or non-invasive ventilator. Moreover, he is a non-smoker with no lung comorbidities and never reported a cough. He was eventually discharged home in stable condition. A comprehensive literature review revealed 15 cases of SPM developing in patients with COVID-19 pneumonia.
    MeSH term(s) COVID-19/complications ; COVID-19/diagnostic imaging ; Humans ; Male ; Mediastinal Emphysema/virology ; Middle Aged ; Pneumothorax/virology ; SARS-CoV-2 ; Subcutaneous Emphysema/virology
    Language English
    Publishing date 2020-12-12
    Publishing country England
    Document type Case Reports ; Journal Article ; Review
    ISSN 1757-790X
    ISSN (online) 1757-790X
    DOI 10.1136/bcr-2020-239489
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Virtual Research Groups Increase Research Opportunities for Trainees: Evaluation of the Massachusetts General Hospital Radiology Research Training Collaborative.

    Elhakim, Tarig / Di Capua, John / Carter, Ashley / Deng, Francis / Chonde, Daniel B / Lang, Min / Dai, Harrison / Garg, Tushar / Kalva, Sanjeeva P / Huang, Junjian / Uppot, Raul / Daye, Dania / Som, Avik

    Journal of the American College of Radiology : JACR

    2023  Volume 20, Issue 11, Page(s) 1152–1158

    Language English
    Publishing date 2023-09-01
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2274861-1
    ISSN 1558-349X ; 1546-1440
    ISSN (online) 1558-349X
    ISSN 1546-1440
    DOI 10.1016/j.jacr.2023.08.027
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 6636: Show issues Location:
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