30-day Readmission After Operative Management of Adhesive Small Bowel Obstruction

Nurmsoo, Sean; Hajizadeh, Mohammad

doi:10.1097/sla.0000000000002091

Annals of Surgery

2018

DOI: 10.1097/sla.0000000000002091

|View full text |Cite

30-day Readmission After Operative Management of Adhesive Small Bowel Obstruction

Sean Nurmsoo

Mohammad Hajizadeh²

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

Development and validation of deep learning models for bowel obstruction on plain abdominal radiograph

Li,

Zhu,

Wang

et al. 2024

J Int Med Res

View full text Add to dashboard Cite

Objective Artificial intelligence (AI) could help medical practitioners in analyzing radiological images to determine the presence and site of bowel obstruction. This retrospective diagnostic study proposed a series of deep learning (DL) models for diagnosing bowel obstruction on abdominal radiograph. Methods A total of 2082 upright plain abdominal radiographs were retrospectively collected from four hospitals. The images were labeled as normal, small bowel obstruction and large bowel obstruction by three senior radiologists based on comprehensive examinations and interventions within 48 hours after admission. Gradient-weighted class activation mapping was used to visualize the inferential explanation. Results In the validation set, the Xception-backboned model achieved the highest accuracy (0.863), surpassing the VGG16 (0.847) and ResNet models (0.836). In the test set, the Xception model (accuracy: 0.807) outperformed other models and a junior radiologist (0.780) but not a senior radiologist (0.840). In the AI-aided diagnostic framework, the junior and senior radiologists made their judgements while aware of the Xception model predictions. Their accuracy significantly improved to 0.887 and 0.913, respectively. Conclusions We developed and validated DL-based computer vision models for diagnosing bowel obstruction on plain abdominal radiograph. DL-based computer-aided diagnostic systems could reduce medical practitioners’ workloads and improve diagnostic accuracy.

show abstract

Development and validation of deep learning models for bowel obstruction on plain abdominal radiograph

Li,

Zhu,

Wang

et al. 2024

J Int Med Res

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

30-day Readmission After Operative Management of Adhesive Small Bowel Obstruction

Cited by 1 publication

References 1 publication

Development and validation of deep learning models for bowel obstruction on plain abdominal radiograph

Development and validation of deep learning models for bowel obstruction on plain abdominal radiograph

Contact Info

Product

Resources

About