Comparative analysis of non-invasive methods for measuring abdominal cavity volume

Kanakhina L.B.; Protasov A.V.; Mazurova O.I.; Topchiev A.M.; Navid M.N.

doi:https://doi.org/10.17116/dokgastro20241303150

Kanakhina L.B.

Peoples’ Friendship University of Russia named after Patrice Lumumba

ORCID: 0000-0003-2677-6272

Protasov A.V.

Peoples’ Friendship University of Russia named after Patrice Lumumba

ORCID: 0000-0001-5439-9262

Mazurova O.I.

Peoples’ Friendship University of Russia named after Patrice Lumumba

ORCID: 0000-0003-2677-6272

Topchiev A.M.

Astrakhan State Medical University

ORCID: 0000-0001-8402-1009

Navid M.N.

Peoples’ Friendship University of Russia named after Patrice Lumumba

ORCID: 0000-0003-1790-1158

Comparative analysis of non-invasive methods for measuring abdominal cavity volume

Authors:

Kanakhina L.B., Protasov A.V., Mazurova O.I., Topchiev A.M., Navid M.N.

More about the authors

Journal: Russian Journal of Evidence-Based Gastroenterology. 2024;13(3): 50‑59

DOI: 10.17116/dokgastro20241303150

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To cite this article:

Kanakhina LB, Protasov AV, Mazurova OI, Topchiev AM, Navid MN. Comparative analysis of non-invasive methods for measuring abdominal cavity volume. Russian Journal of Evidence-Based Gastroenterology. 2024;13(3):50‑59. (In Russ.)
https://doi.org/10.17116/dokgastro20241303150

Подписка 2026 Доказательная гастроэнтерология (Russian Journal of Evidence-Based Gastroenterology)

Использование инфографики авторами научных работ

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OBJECTIVE

To evaluate the potential use of CT hernioabdominometry for predicting the development of abdominal compartment syndrome (ACS) in patients with large and giant ventral hernias.

MATERIALS AND METHODS

The study included 35 healthy patients undergoing examinations for primary conditions unrelated to ventral hernia diagnosis. All patients underwent computed tomography of the chest, abdomen, and pelvis (three zones) with intravenous contrast enhancement. Measurements and analysis of CT scans were performed using the Vidar Dicom Viewer program. For statistical processing and building a machine learning model, Python 3.11.0 and the libraries pandas, sklearn, matplotlib, and scipy.stats were used. Three methods for measuring abdominal cavity volume (ACV) were used for comparative analysis: manual measurement using Vidar Dicom Viewer software, and the methods by E.Y. Tanaka et al. and N.S. Nikityaev et al. The obtained data were statistically processed using the Python programming language with appropriate libraries.

RESULTS

Manual measurement can be inaccurate due to the complex structure of the abdominal cavity and requires a significant amount of time. The method by E.Y. Tanaka showed more accurate results and statistical proximity to the conditionally true volume. This study revealed a direct relationship between the length, area of the muscle fibers of the anterior abdominal wall, and the volume of the abdominal cavity. It was found that prolonged herniation leads to spastic contraction and shortening of the muscle layer, which may contribute to the development of ACS.

CONCLUSION

The authors suggest using these parameter measurements before and after surgery to prevent the development of ACS and improve treatment outcomes.

Keywords:

CT-abdominometry

abdominal volume

ventral hernia

abdominal compartment syndrome

computer vision

Authors:

Kanakhina L.B.

Peoples’ Friendship University of Russia named after Patrice Lumumba

ORCID: 0000-0003-2677-6272

Protasov A.V.

Peoples’ Friendship University of Russia named after Patrice Lumumba

ORCID: 0000-0001-5439-9262

Mazurova O.I.

Peoples’ Friendship University of Russia named after Patrice Lumumba

ORCID: 0000-0003-2677-6272

Topchiev A.M.

Astrakhan State Medical University

ORCID: 0000-0001-8402-1009

Navid M.N.

Peoples’ Friendship University of Russia named after Patrice Lumumba

ORCID: 0000-0003-1790-1158

Received:

25.01.2024

Accepted:

17.06.2024

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