Artificial intelligence for detection of effusion and lipo-hemarthrosis in X-rays and CT of the knee.

Journal: European journal of radiology
PMID: 38608501

Abstract

BACKGROUND: Traumatic knee injuries are challenging to diagnose accurately through radiography and to a lesser extent, through CT, with fractures sometimes overlooked. Ancillary signs like joint effusion or lipo-hemarthrosis are indicative of fractures, suggesting the need for further imaging. Artificial Intelligence (AI) can automate image analysis, improving diagnostic accuracy and help prioritizing clinically important X-ray or CT studies.

Authors

  • Israel Cohen
    Department of Diagnostic Imaging, Chaim Sheba Medical Center, Emek Haela St. 1, 52621, Ramat Gan, Israel.
  • Vera Sorin
    Department of Diagnostic Imaging, Chaim Sheba Medical Center, Tel Hashomer, Israel.
  • Ruth Lekach
    Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; Department of Nuclear Medicine, Sourasky Medical Center, Tel-Aviv, Israel. Electronic address: Ruth88l@gmail.com.
  • Daniel Raskin
    Department of Diagnostic Imaging, Chaim Sheba Medical Center, Emek Haela St. 1, 52621, Ramat Gan, Israel.
  • Maria Segev
    Department of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, Israel; Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel. Electronic address: Maria.strzelak@sheba.gov.il.
  • Eyal Klang
    Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Iris Eshed
    Department of Radiology, Sheba Medical Center, Tel Hashomer, Israel; Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
  • Yiftach Barash
    Department of Diagnostic Imaging, Chaim Sheba Medical Center, Tel Hashomer, Israel.

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