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Everything you need to know about Computed Tomography (CT) & CT Scanning

Deep Learning: Deep Learning and Outcomes Imaging Pearls - Educational Tools | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ Deep Learning ❯ Deep Learning and Outcomes

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  • “Overfitting is a major obstacle for AI technology, but what exactly, is overfitting? Burnham describes “the essence of overfitting is to have unknowingly extracted some of the residual variation as if that variation represented underlying model structure” . In layman's terms, overfitting means that an AI model has learned in a manner that is only applicable to the training sample and is no longer generalizable to the overall population .”
    Understanding artificial intelligence based radiology studies: What is overfitting?
    Simukayi Mutasa, Shawn Sun, Richard Ha
    Clinical Imaging 65 (2020) 96–99
  • "For example, if an algorithm designed to distinguish between dogs and cats is trained only with the German shepherd dogs and Siamese cats in, it will perform well if subsequently tested only on German shepherd dogs and Siamese cats. However, if the algorithm is then asked to distinguish other types of dogs and cats, which it has not seen before, its performance will decrease substantially.”
    Understanding artificial intelligence based radiology studies: What is overfitting?
    Simukayi Mutasa, Shawn Sun, Richard Ha
    Clinical Imaging 65 (2020) 96–99
  • “The exciting results of recent AI radiology studies certainly generate much anticipation towards a future where radiologists utilize AI to better save lives. However, the pitfall of overfitting really highlights the need for external validation of AI before clinical implementation. There have been cases of neural network performance being affected by data from a different institution . To prove to clinicians the validity of results, deep neural networks need to de- monstrate performance on external data different from its training data. Some researchers have even emphasized the need for prospective, multi-center, cohort studies and to hold AI technology to the same level of scrutiny as new clinical drugs. Undoubtedly, the field of AI in medical imaging is still in its infancy, as studies achieving that level of validation are extremely rare.”
    Understanding artificial intelligence based radiology studies: What is overfitting?
    Simukayi Mutasa, Shawn Sun, Richard Ha
    Clinical Imaging 65 (2020) 96–99
  • "What seems ethically imperative at present, though, is a steady and informed rebuttal of AI hype, especially as it is aimed at image-dependent technologies like radiology. Today’s hospitals simply cannot function without radiologists, who are core to their diagnostic functions. To allow a deterioration in the quality of radiology services because of the promulgation of false narratives imperils the public welfare. Rather than being caricatured as in a state of near-future extinction, radiology might well advance to a new era of excellence.”
    AI Hype and Radiology: A Plea for Realism and Accuracy
    Banja J et al.
    Radiology: Artificial Intelligence 2020; 2(4):e190223
  • "However, perhaps a better explanation as to why innovation in AI may be slowing is that much of the private sector seems frankly disinterested. Today’s deep learning models appear in- creasingly focused on merchandizing applications that forecast product demand and facilitate sales rather than on humanitarian welfare concerns.”
    AI Hype and Radiology: A Plea for Realism and Accuracy
    Banja J et al.
    Radiology: Artificial Intelligence 2020; 2(4):e190223
  • “It’s hard to predict the future, and what immensely complicates predictions over seemingly promising technologies like gene therapy or AI is how their complex construction will interface with other equally complex and dynamic technologies, all of which operate in an environment of unceasing economic and institutional flux. It remains anyone’s guess as to how AI applications will be affected by their integration with PACS, how liability trends or regulatory efforts will affect AI, whether reimbursement for AI will justify its use, how mergers and acquisitions will affect AI implementation, and how well AI models will accommodate ethical requirements related to informed consent, privacy, and patient access.”
    AI Hype and Radiology: A Plea for Realism and Accuracy
    Banja J et al.
    Radiology: Artificial Intelligence 2020; 2(4):e190223

  • Machine Learning Approaches to Predict 6-Month Mortality Among Patients With Cancer. 
    Parikh RB, Manz C, Chivers C, et al.
    JAMA Netw Open. Published online October 25, 20192(10):e1915997. doi:10.1001/jamanetworkopen.2019.15997
  • Question  Can machine learning algorithms identify oncology patients at risk of short-term mortality to inform timely conversations between patients and physicians regrading serious illness?
    Findings  In this cohort study of 26 525 patients seen in oncology practices within a large academic health system, machine learning algorithms accurately identified patients at high risk of 6-month mortality with good discrimination and positive predictive value. When the gradient boosting algorithm was applied in real time, most patients who were classified as having high risk were deemed appropriate by oncology clinicians for a conversation regarding serious illness.
    Meaning  In this study, machine learning algorithms accurately identified patients with cancer who were at risk of 6-month mortality, suggesting that these models could facilitate more timely conversations between patients and physicians regarding goals and values.
    Machine Learning Approaches to Predict 6-Month Mortality Among Patients With Cancer. 
    Parikh RB, Manz C, Chivers C, et al.
    JAMA Netw Open. Published online October 25, 20192(10):e1915997. doi:10.1001/jamanetworkopen.2019.15997
  • Objectives  To develop, validate, and compare machine learning algorithms that use structured electronic health record data before a clinic visit to predict mortality among patients with cancer.
    Design, Setting, and Participants  Cohort study of 26 525 adult patients who had outpatient oncology or hematology/oncology encounters at a large academic cancer center and 10 affiliated community practices between February 1, 2016, and July 1, 2016. Patients were not required to receive cancer-directed treatment. Patients were observed for up to 500 days after the encounter. Data analysis took place between October 1, 2018, and September 1, 2019.
    Machine Learning Approaches to Predict 6-Month Mortality Among Patients With Cancer. 
    Parikh RB, Manz C, Chivers C, et al.
    JAMA Netw Open. Published online October 25, 20192(10):e1915997. doi:10.1001/jamanetworkopen.2019.15997
  • Conclusions and Relevance  In this cohort study, machine learning algorithms based on structured electronic health record data accurately identified patients with cancer at risk of short-term mortality. When the gradient boosting algorithm was applied in real time, clinicians believed that most patients who had been identified as having high risk were appropriate for a timely conversation about treatment and end-of-life preferences.
    Machine Learning Approaches to Predict 6-Month Mortality Among Patients With Cancer. 
    Parikh RB, Manz C, Chivers C, et al.
    JAMA Netw Open. Published online October 25, 20192(10):e1915997. doi:10.1001/jamanetworkopen.2019.15997
  • “This cohort study demonstrated that, in a large heterogeneous population of patients seeking outpatient oncology care, ML algorithms based on structured real-time EHR data had adequate performance in identifying outpatients with cancer who had high risk of short-term mortality. According to clinician surveys, most patients flagged as having high risk by one of the ML models were appropriate for a timely conversation about goals and end-of-life preferences. Our findings suggest that ML tools hold promise for integration into clinical workflows to ensure that patients with cancer have timely conversations about their goals and values.”
    Machine Learning Approaches to Predict 6-Month Mortality Among Patients With Cancer. 
    Parikh RB, Manz C, Chivers C, et al.
    JAMA Netw Open. Published online October 25, 20192(10):e1915997. doi:10.1001/jamanetworkopen.2019.15997
  • Key Points
    Question Can machine learning algorithms identify oncology patients at risk of short-term mortality to inform timely conversations between patients and physicians regrading serious illness?
    Findings In this cohort study of 26525 patients seen in oncology practices within a large academic health system, machine learning algorithms accurately identified patients at high risk of 6-month mortality with good discrimination and positive predictive value. When the gradient boosting algorithm was applied in real time, most patients who were classified as having high risk were deemed appropriate by oncology clinicians for a conversation regarding serious illness.
    Meaning In this study, machine learning algorithms accurately identified patients with cancer who were at risk of 6-month mortality, suggesting that these models could facilitate more timely conversations between patients and physicians regarding goals and values.

  • Machine Learning Approaches to Predict 6-Month Mortality Among Patients With Cancer. 
    Parikh RB, Manz C, Chivers C, et al.
    JAMA Netw Open. Published online October 25, 20192(10):e1915997. doi:10.1001/jamanetworkopen.2019.15997
  • Question  Can machine learning algorithms identify oncology patients at risk of short-term mortality to inform timely conversations between patients and physicians regrading serious illness?
    Findings  In this cohort study of 26 525 patients seen in oncology practices within a large academic health system, machine learning algorithms accurately identified patients at high risk of 6-month mortality with good discrimination and positive predictive value. When the gradient boosting algorithm was applied in real time, most patients who were classified as having high risk were deemed appropriate by oncology clinicians for a conversation regarding serious illness.
    Meaning  In this study, machine learning algorithms accurately identified patients with cancer who were at risk of 6-month mortality, suggesting that these models could facilitate more timely conversations between patients and physicians regarding goals and values.
    Machine Learning Approaches to Predict 6-Month Mortality Among Patients With Cancer. 
    Parikh RB, Manz C, Chivers C, et al.
    JAMA Netw Open. Published online October 25, 20192(10):e1915997. doi:10.1001/jamanetworkopen.2019.15997
  • Objectives  To develop, validate, and compare machine learning algorithms that use structured electronic health record data before a clinic visit to predict mortality among patients with cancer.
    Design, Setting, and Participants  Cohort study of 26 525 adult patients who had outpatient oncology or hematology/oncology encounters at a large academic cancer center and 10 affiliated community practices between February 1, 2016, and July 1, 2016. Patients were not required to receive cancer-directed treatment. Patients were observed for up to 500 days after the encounter. Data analysis took place between October 1, 2018, and September 1, 2019.
    Machine Learning Approaches to Predict 6-Month Mortality Among Patients With Cancer. 
    Parikh RB, Manz C, Chivers C, et al.
    JAMA Netw Open. Published online October 25, 20192(10):e1915997. doi:10.1001/jamanetworkopen.2019.15997
  • Conclusions and Relevance  In this cohort study, machine learning algorithms based on structured electronic health record data accurately identified patients with cancer at risk of short-term mortality. When the gradient boosting algorithm was applied in real time, clinicians believed that most patients who had been identified as having high risk were appropriate for a timely conversation about treatment and end-of-life preferences.
    Machine Learning Approaches to Predict 6-Month Mortality Among Patients With Cancer. 
    Parikh RB, Manz C, Chivers C, et al.
    JAMA Netw Open. Published online October 25, 20192(10):e1915997. doi:10.1001/jamanetworkopen.2019.15997
  • “This cohort study demonstrated that, in a large heterogeneous population of patients seeking outpatient oncology care, ML algorithms based on structured real-time EHR data had adequate performance in identifying outpatients with cancer who had high risk of short-term mortality. According to clinician surveys, most patients flagged as having high risk by one of the ML models were appropriate for a timely conversation about goals and end-of-life preferences. Our findings suggest that ML tools hold promise for integration into clinical workflows to ensure that patients with cancer have timely conversations about their goals and values.”
    Machine Learning Approaches to Predict 6-Month Mortality Among Patients With Cancer. 
    Parikh RB, Manz C, Chivers C, et al.
    JAMA Netw Open. Published online October 25, 20192(10):e1915997. doi:10.1001/jamanetworkopen.2019.15997
  • Key Points
    Question Can machine learning algorithms identify oncology patients at risk of short-term mortality to inform timely conversations between patients and physicians regrading serious illness?
    Findings In this cohort study of 26525 patients seen in oncology practices within a large academic health system, machine learning algorithms accurately identified patients at high risk of 6-month mortality with good discrimination and positive predictive value. When the gradient boosting algorithm was applied in real time, most patients who were classified as having high risk were deemed appropriate by oncology clinicians for a conversation regarding serious illness.
    Meaning In this study, machine learning algorithms accurately identified patients with cancer who were at risk of 6-month mortality, suggesting that these models could facilitate more timely conversations between patients and physicians regarding goals and values.
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