Closed loop obstruction of small bowel: CT signs predicting successful non-surgical treatment

Kim, Hye Ri; Lee, Yedaun; Kim, Jieun; Baek, Tae Wook; Kim, Hyun-Woong; Son, Jung Hee; Park, Eun Joo; Kim, Seung Ho

doi:10.1016/j.ejrad.2023.110716

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…However, abdominal radiography insights might lack depth in intricate cases. This is where advanced tools, like contrast‐enhanced CT scans, shine by revealing intricate details 25,26 . These insights empower surgeons to customise treatment strategies grounded in comprehensive understanding.…”

Section: Discussionmentioning

confidence: 98%

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Unveiling new patterns: A surgical deep learning model for intestinal obstruction management

Tatar,

Akay,

Tatar

et al. 2024

Robotics Computer Surgery

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BackgroundSwift and accurate decision‐making is pivotal in managing intestinal obstructions. This study aims to integrate deep learning and surgical expertise to enhance decision‐making in intestinal obstruction cases.MethodsWe developed a deep learning model based on the YOLOv8 framework, trained on a dataset of 700 images categorised into operated and non‐operated groups, with surgical outcomes as ground truth. The model's performance was evaluated through standard metrics.ResultsAt a confidence threshold of 0.5, the model demonstrated sensitivity of 83.33%, specificity of 78.26%, precision of 81.7%, recall of 75.1%, and mAP@0.5 of 0.831.ConclusionsThe model exhibited promising outcomes in distinguishing operative and nonoperative management cases. The fusion of deep learning with surgical expertise enriches decision‐making in intestinal obstruction management. The proposed model can assist surgeons in intricate scenarios such as intestinal obstruction management and promotes the synergy between technology and clinical acumen for advancing patient care.

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Section: Discussionmentioning

confidence: 98%

“…This is where advanced tools, like contrast-enhanced CT scans, shine by revealing intricate details. 25,26 These insights empower surgeons to customise treatment strategies grounded in comprehensive understanding. values for these metrics were 0.81747, 0.75188, 0.8306, and 0.45914, respectively.…”

Section: Discussionmentioning

confidence: 99%

Unveiling new patterns: A surgical deep learning model for intestinal obstruction management

Tatar,

Akay,

Tatar

et al. 2024

Robotics Computer Surgery

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“…Small-bowel obstruction (SBO) is one of the most common types of acute abdomen in clinical practice, and accounts for approximately 12–16% of all emergent surgical procedures [ 1 ]. SBO can be divided into simple SBO and small-bowel ischemia and necrosis (SBN), and most simple SBO can be relieved or treated through active conservative treatment such as gastrointestinal decompression [ 2 ]. SBN is a serious complication of SBO.…”

Section: Introductionmentioning

confidence: 99%

“…Full abdominal CT can clearly display changes in the thickness and density of the small-bowel wall, as well as reveal structures such as the small-bowel mesentery and blood vessels, providing significant clues for predicting SBN. Previous studies have shown that CT manifestations of SBN after SBO include thickening of the small-bowel wall, decreased enhancement, small-bowel mesenteric edema, and peritonitis [ 1 , 2 ]. Abdominal MRI scanning has limited diagnostic value for SBN due to respiratory artifacts [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

The target sign: a significant CT sign for predicting small-bowel ischemia and necrosis

Li,

Wu,

Wang

2024

Radiol med

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Objective To investigate the correlation between changes in the thickness and density of diseased small-bowel wall and small-bowel ischemia and necrosis (SBN) on CT imaging when small-bowel obstruction (SBO) occurs. Methods We retrospectively analyzed 186 patients with SBO in our hospital from March 2020 to June 2023. The patients were divided into simple SBO (control group) and SBN (case group) groups. We used logistic regression analysis, the chi-square test, and Fisher's exact test to analyze the correlation between the changes in the thickness and density of the diseased intestinal wall and the SBN. A receiver operating characteristic (ROC) curve was used to calculate the accuracy of the multivariate analysis. Results Of the 186 patients with SBO, 98 (52.7%) had simple SBO, 88 (47.3%) had SBN, and the rate of SBN was 47.3% (88/186). Multivariate regression analysis revealed that six CT findings were significantly correlated with SBN (p < 0.05), namely, thickening of the diseased intestinal wall with the target sign (OR = 21.615), thinning of the diseased intestinal wall (OR = 48.106), increase in the diseased intestinal wall density (OR = 13.696), mesenteric effusion (OR = 21.635), decrease in the diseased intestinal wall enhancement on enhanced scanning (OR = 41.662), and increase in the diseased intestinal wall enhancement on enhanced scanning (OR = 15.488). The AUC of the multivariate analysis reached 0.987 (95% CI 0.974–0.999). Specifically, the target sign was easily recognizable on CT images and was a significant CT finding for predicting SBN. Conclusion We identified 6 CT findings that were significantly associated with SBN, and may be helpful for clinical treatment.

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Prediction of bowel necrosis by reduced bowel wall enhancement in closed-loop small bowel obstruction: Quantitative methods

Li,

Qi,

Yuan

et al. 2024

European Journal of Radiology

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Closed loop obstruction of small bowel: CT signs predicting successful non-surgical treatment

Cited by 5 publications

References 20 publications

Unveiling new patterns: A surgical deep learning model for intestinal obstruction management

Unveiling new patterns: A surgical deep learning model for intestinal obstruction management

The target sign: a significant CT sign for predicting small-bowel ischemia and necrosis

Prediction of bowel necrosis by reduced bowel wall enhancement in closed-loop small bowel obstruction: Quantitative methods

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