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With over 5 million deaths each year worldwide, traumatic injuries are one of the leading causes of death. Blunt-force abdominal trauma usually arises from vehicle accidents and leads to severe internal injury to abdominal organs. These severe injuries are frequently challenging to diagnose with clinical examinations and routine laboratory procedures. Although computed tomography (CT) scans produce comprehensive images, they are complex to interpret, mainly when there are many wounds or minute bleeding. Thus, it is crucial for patient care that abdominal trauma is promptly diagnosed via medical imaging. The ability to start suitable and timely therapies as soon as a traumatic injury is identified is essential for improving patient outcomes and survival rates. This paper aimed to improve injury detection using machine learning, improve trauma care and patient outcomes globally, and help medical practitioners identify injuries and determine their severity quickly and accurately.

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Enhancing Abdominal Trauma Diagnosis with AI: Improving Detection, Care, and Outcomes Through Machine Learning

  • 会议论文

  • 发表日期：

    2025年

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  • K. Mallikharjuna Rao,
  • Rachamadugu Subramanya Buvan,
  • Mudumba Karthikey Prachodhan,
  • Aneesh Narayan Bandaru

摘要

With over 5 million deaths each year worldwide, traumatic injuries are one of the leading causes of death. Blunt-force abdominal trauma usually arises from vehicle accidents and leads to severe internal injury to abdominal organs. These severe injuries are frequently challenging to diagnose with clinical examinations and routine laboratory procedures. Although computed tomography (CT) scans produce comprehensive images, they are complex to interpret, mainly when there are many wounds or minute bleeding. Thus, it is crucial for patient care that abdominal trauma is promptly diagnosed via medical imaging. The ability to start suitable and timely therapies as soon as a traumatic injury is identified is essential for improving patient outcomes and survival rates. This paper aimed to improve injury detection using machine learning, improve trauma care and patient outcomes globally, and help medical practitioners identify injuries and determine their severity quickly and accurately.