Evidence map›Paper›PMID 38988013›Full record

ArticleKorean journal of clinical oncology2024

Measurement of human peritoneal surface area using artificial intelligence software in abdominal computed tomography.

Seung Joon Choi, Ji-Hyeon Park, Youngbae Jeon, Donghyuk Lee, Jeong-Heum Baek

Abstract read
In one paragraph

Article in Korean journal of clinical oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Seung Joon ChoiDepartment of Radiology, Gachon University Gil Medical Center, Gachon University College of Medicine, Incheon, Korea.
Ji-Hyeon ParkDepartment of Surgery, Gachon University Gil Medical Center, Gachon University College of Medicine, Incheon, Korea.
Youngbae JeonDepartment of Surgery, Gachon University Gil Medical Center, Gachon University College of Medicine, Incheon, Korea.
Donghyuk LeeDepartment of Biomedical Engineering, Gachon University College of Medicine, Incheon, Korea.
Jeong-Heum BaekDepartment of Surgery, Gachon University Gil Medical Center, Gachon University College of Medicine, Incheon, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe 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.

methodsComputed 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.

resultsThe 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).

conclusionThe abdominal cavity surface area can be measured using AI techniques and is expected to be used as basic data for clinical applications.

Indexed as

Artificial intelligenceComputed tomographyPeritoneal cavity

Identifiers

PMID38988013
PMCPMC11261175

What Socratic holds

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LicenceCC BY-NC
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.