Development and Validation of a CT-based Diagnostic Models Using Artificial Intelligence for Detection of Small Bowel Obstruction

NCT05566158 | Unknown

• 1 other identifier: SMARTLOOP2
• Study Type: observational
• Target: 8,000
• Locations: 1 country
• Sites: 2

Brief Summary

Small bowel obstruction (SBO) is a common non-traumatic surgical emergency. All guidelines recommend computed tomography (CT) as the first-line imaging test for patients with suspected SBO. The objectives of CT are multiple: (i) to confirm or refute the diagnosis of GI obstruction, defined as distension of the digestive tracts greater than 25 mm, and, when SBO is present, (ii) to confirm the mechanism (mechanical vs. functional), (iii) to localize the site of obstruction, i.e., the transition zone (TZ), (iv) to identify the cause, and (v) to look for complications such as strangulation or perforation, influencing management. Given the exponential increase in the number of scans being performed, especially in the setting of emergency management, methods to assist the radiologist would be useful to:

1. 1.Sort the scans performed, allowing prioritization of the analysis of scans with a higher probability of pathology (occlusion in our case)
2. 2.Help the radiologist to diagnose occlusion and its type (functional or mechanical), and to identify signs of severity.
3. 3.To help the emergency physician and the digestive surgeon to make a decision on the management of the disease (surgical or medical).

Conditions

Small Bowel Obstruction

Outcome Measures

Primary Outcomes (1)

• Automated detection of digestive occlusions

  This outcome corresponds to the ability of the model to identify the presence or absence of occlusion: sensitivity, specificity and predictive values.

  Year 1

Secondary Outcomes (4)

• Automatic differentiation of functional vs. mechanical occlusions

  Year 1

• Algorithm for surgical indication

  Year 1

• Analysis via radiomics of junction zones

  Year 1

• Automated detection of junction areas

  Year 1

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patient whose age ≥ 18 years, who has had a CT scan with at least one abdominal-pelvic acquisition performed within the Saint Joseph Hospital Group with a report containing the terms "occlusion" or "occlusive", "vomiting" or "ileus".

You may qualify if:

• Patient whose age ≥ 18 years
• Patient who has had a CT scan with at least one abdominal-pelvic acquisition performed within the Saint Joseph Hospital Group
• Report containing the terms "occlusion" or "occlusive", "vomiting" or "ileus"
• French-speaking patient

You may not qualify if:

• Imaging not usable
• Absence of abdomino-pelvic volume on CT acquisitions
• Patient under guardianship or curatorship
• Patient deprived of liberty
• Patient under court protection
• Patient objecting to the use of his data for this research

Sponsors & Collaborators

• Fondation Hôpital Saint-Joseph: lead

Study Sites (2)

Central for Visual Computing - OPIS Inria group

Gif-sur-Yvette, France

Location

Groupe Hospitalier Paris Saint-Joseph

Paris, 75014, France

Location

Related Publications (8)

• Ten Broek RPG, Krielen P, Di Saverio S, Coccolini F, Biffl WL, Ansaloni L, Velmahos GC, Sartelli M, Fraga GP, Kelly MD, Moore FA, Peitzman AB, Leppaniemi A, Moore EE, Jeekel J, Kluger Y, Sugrue M, Balogh ZJ, Bendinelli C, Civil I, Coimbra R, De Moya M, Ferrada P, Inaba K, Ivatury R, Latifi R, Kashuk JL, Kirkpatrick AW, Maier R, Rizoli S, Sakakushev B, Scalea T, Soreide K, Weber D, Wani I, Abu-Zidan FM, De'Angelis N, Piscioneri F, Galante JM, Catena F, van Goor H. Bologna guidelines for diagnosis and management of adhesive small bowel obstruction (ASBO): 2017 update of the evidence-based guidelines from the world society of emergency surgery ASBO working group. World J Emerg Surg. 2018 Jun 19;13:24. doi: 10.1186/s13017-018-0185-2. eCollection 2018.

  PMID: 29946347 BACKGROUND

• Expert Panel on Gastrointestinal Imaging; Chang KJ, Marin D, Kim DH, Fowler KJ, Camacho MA, Cash BD, Garcia EM, Hatten BW, Kambadakone AR, Levy AD, Liu PS, Moreno C, Peterson CM, Pietryga JA, Siegel A, Weinstein S, Carucci LR. ACR Appropriateness Criteria(R) Suspected Small-Bowel Obstruction. J Am Coll Radiol. 2020 May;17(5S):S305-S314. doi: 10.1016/j.jacr.2020.01.025.

  PMID: 32370974 BACKGROUND

• Frager D, Medwid SW, Baer JW, Mollinelli B, Friedman M. CT of small-bowel obstruction: value in establishing the diagnosis and determining the degree and cause. AJR Am J Roentgenol. 1994 Jan;162(1):37-41. doi: 10.2214/ajr.162.1.8273686.

  PMID: 8273686 BACKGROUND

• Montagnon E, Cerny M, Cadrin-Chenevert A, Hamilton V, Derennes T, Ilinca A, Vandenbroucke-Menu F, Turcotte S, Kadoury S, Tang A. Deep learning workflow in radiology: a primer. Insights Imaging. 2020 Feb 10;11(1):22. doi: 10.1186/s13244-019-0832-5.

  PMID: 32040647 BACKGROUND

• Cheng PM, Tejura TK, Tran KN, Whang G. Detection of high-grade small bowel obstruction on conventional radiography with convolutional neural networks. Abdom Radiol (NY). 2018 May;43(5):1120-1127. doi: 10.1007/s00261-017-1294-1.

  PMID: 28828625 BACKGROUND

• Kim DH, Wit H, Thurston M, Long M, Maskell GF, Strugnell MJ, Shetty D, Smith IM, Hollings NP. An artificial intelligence deep learning model for identification of small bowel obstruction on plain abdominal radiographs. Br J Radiol. 2021 Jun 1;94(1122):20201407. doi: 10.1259/bjr.20201407. Epub 2021 Apr 27.

  PMID: 33904763 BACKGROUND

• Vanderbecq Q, Ardon R, De Reviers A, Ruppli C, Dallongeville A, Boulay-Coletta I, D'Assignies G, Zins M. Adhesion-related small bowel obstruction: deep learning for automatic transition-zone detection by CT. Insights Imaging. 2022 Jan 24;13(1):13. doi: 10.1186/s13244-021-01150-y.

  PMID: 35072813 BACKGROUND

• Hodel J, Zins M, Desmottes L, Boulay-Coletta I, Julles MC, Nakache JP, Rodallec M. Location of the transition zone in CT of small-bowel obstruction: added value of multiplanar reformations. Abdom Imaging. 2009 Jan-Feb;34(1):35-41. doi: 10.1007/s00261-007-9348-4.

  PMID: 18172705 BACKGROUND

Study Officials

• Quentin Vanderbecq, MD

  Fondation Hôpital Saint-Joseph

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: September 30, 2022
• First Posted: October 4, 2022
• Study Start: August 9, 2022
• Primary Completion: September 9, 2022
• Study Completion: December 31, 2023
• Last Updated: October 4, 2022

Record last verified: 2022-09

Locations

FR (2)