Unsupervised machine learning identifies predictive progression markers of ipf

HIGHLIGHTS

• who: Jeanny Pan from the (DiMeC), University of Parma, Parma, Italy have published the Article: Unsupervised machine learning identifies predictive progression markers of IPF, in the Journal: (JOURNAL)
• what: It was the aim of this study to develop an unsupervised machine_learning approach to identify novel radiological disease progression imaging marker patterns and evaluate if these patterns predict outcome. This study showed that unsupervised machine_learning can identify predictive CT patterns associated with radiological disease progression in IPF. Although the results of the machine_learning methods show comparable performance in comparison with the expert radiologists, this work has . . .
  

   

  

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