Development and validation of computer-aided detection for colorectal neoplasms using deep learning incorporated with computed tomography colonography.

Journal: BMC gastroenterology
PMID: 40055612

Abstract

OBJECTIVES: Computed tomography (CT) colonography is increasingly recognized as a valuable modality for diagnosing colorectal lesions, however, the interpretation workload remains challenging for physicians. Deep learning-based artificial intelligence (AI) algorithms have been employed for imaging diagnoses. In this study, we examined the sensitivity of neoplastic lesions in CT colonography images.

Authors

  • Shungo Endo
    Department of Coloproctology, Aizu Medical Center, Fukushima Medical University, Fukushima, Japan.
  • Koichi Nagata
    Department of Gastroenterology, Fukushima Medical University, Fukushima City, Japan.
  • Kenichi Utano
    Department of Coloproctology, Aizu Medical Center, Fukushima Medical University, Fukushima, Japan.
  • Satoshi Nozu
    Department of Radiology, Saitama Cancer Center, Kita-Adachi-gun, Saitama, Japan.
  • Takaaki Yasuda
    Department of Radiology, Nagasaki Kamigoto Hospital, Shin Kamigoto-cho, Nagasaki, Japan.
  • Ken Takabayashi
    Department of Radiology, Tonan Hospital, Sapporo City, Japan.
  • Michiaki Hirayama
    Department of Gastroenterology, Tonan Hospital, Sapporo City, Japan.
  • Kazutomo Togashi
    Department of Coloproctology, Aizu Medical Center, Fukushima Medical University, Fukushima, Japan.
  • Hiromasa Ohira

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