지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.6
- 수록면
- 6 - 12 (7page)
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초록· 키워드
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Purpose: The calculation of the intraperitoneal organ surface area is important for understanding their anatomical structure and for conducting basic and clinical studies on diseases related to the peritoneum. To measure the intraperitoneal surface area in a living body by applying artificial intelligence (AI) techniques to the abdominal cavity using computed tomography and to prepare clinical indicators for application to the abdominal cavity.
Methods: Computed tomography images of ten adult males and females with a healthy body mass index and ten adults diagnosed with colon cancer were analyzed to determine the peritoneal and intraperitoneal surface areas of the organs. The peritoneal surface was segmented and three-dimensionally modeled using AI medical imaging software. In addition to manual work, three-dimensional editing, filtering, and connectivity checks were performed to improve work efficiency and accuracy. The colon and small intestine surface areas were calculated using the mean length and diameter. The abdominal cavity surface area was defined as the sum of the intraperitoneal area and the surface areas of each organ.
Results: The mean peritoneal surface area of all participants was measured as 10,039±241 cm² (males 10,224±171 cm2 and females 9,854±134 cm²). Males had a 3.7% larger peritoneal surface area than females, with a statistically significant difference (P<0.001).
Conclusion: The abdominal cavity surface area can be measured using AI techniques and is expected to be used as basic data for clinical applications.
Methods: Computed tomography images of ten adult males and females with a healthy body mass index and ten adults diagnosed with colon cancer were analyzed to determine the peritoneal and intraperitoneal surface areas of the organs. The peritoneal surface was segmented and three-dimensionally modeled using AI medical imaging software. In addition to manual work, three-dimensional editing, filtering, and connectivity checks were performed to improve work efficiency and accuracy. The colon and small intestine surface areas were calculated using the mean length and diameter. The abdominal cavity surface area was defined as the sum of the intraperitoneal area and the surface areas of each organ.
Results: The mean peritoneal surface area of all participants was measured as 10,039±241 cm² (males 10,224±171 cm2 and females 9,854±134 cm²). Males had a 3.7% larger peritoneal surface area than females, with a statistically significant difference (P<0.001).
Conclusion: The abdominal cavity surface area can be measured using AI techniques and is expected to be used as basic data for clinical applications.
목차
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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