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A User Interface for Optimizing Radiologist Engagement in Image Data Curation for Artificial Intelligence.

Mutlu Demirer1, Sema Candemir1, Matthew T Bigelow1

  • 1Department of Radiology, Laboratory for Augmented Intelligence in Imaging-Division of Medical Imaging Informatics, Ohio State University College of Medicine, OSU Wexner Medical Center, 395 W 12th Ave, Suite 452, Columbus, OH 43210 (M.D., S.C., M.T.B., S.M.Y., V.G., L.M.P., R.D.W., J.S.Y., B.S.E.); Siemens Healthineers, Erlangen, Germany (R.G., M.W., A.W.); NVIDIA, Santa Clara, Calif (A.H.H., A.I.); and Siemens Healthineers, Malvern, Pa (T.P.O.).

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Summary
This summary is machine-generated.

A new graphical user interface (GUI) streamlines medical image annotation for artificial intelligence (AI) development. This tool enhances radiologist efficiency in curating data for AI applications in medical imaging.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Developing artificial intelligence (AI) for medical imaging requires extensive, accurately curated datasets.
  • Radiologists play a crucial role in image annotation, but current tools can be inefficient.
  • There is a need for specialized graphical user interfaces (GUIs) to streamline this process.

Purpose of the Study:

  • To identify image data curation requirements for AI in medical imaging.
  • To describe a novel, locally designed GUI to assist radiologists with image annotation.
  • To support the development and evolution of AI applications in medical imaging.

Main Methods:

  • The GUI was designed with components supporting various image analysis toolboxes, PACS integration, and deep learning libraries.
  • Clinical AI application components included 2D/3D segmentation, classification, and quantification.
  • Radiologist performance was assessed via annotation rate and speed in hip fracture detection and coronary plaque analysis.

Main Results:

  • Hip fracture detection: 1050 radiographs annotated in 7 days (150/day), with a median speed of 10 seconds/study.
  • Coronary plaque analysis: 294 studies annotated over 23 days (15.2 studies/day), with median speeds of 6.08 min/study and 73 sec/vessel.
  • The GUI facilitated efficient radiologist engagement in image annotation tasks.

Conclusions:

  • GUI compatibility with existing tools enhances radiologist participation in data curation for AI.
  • An integrated workflow, supported by the GUI, enables agile deep neural network development.
  • The developed GUI supports the creation and refinement of AI models in medical imaging.