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

Small Bowel Obstruction Clinical Trial

— SMARTLOOP2  

Official title:

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

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT05566158
• Other study ID #: SMARTLOOP2
• Status: Active, not recruiting
• Start date: August 9, 2022
• Est. completion date: December 31, 2023

Study information

• Verified date: September 2022
• Source: Groupe Hospitalier Paris Saint Joseph
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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. Sort the scans performed, allowing prioritization of the analysis of scans with a higher probability of pathology (occlusion in our case)

2. Help the radiologist to diagnose occlusion and its type (functional or mechanical), and to identify signs of severity.

3. To help the emergency physician and the digestive surgeon to make a decision on the management of the disease (surgical or medical).

Machine learning has developed rapidly over the last decades, first thanks to the increase in data storage capacities, then thanks to the arrival of parallel processing hardware based on graphic processing units, in the context of radiological diagnostic assistance. Consequently, the number of studies on deep neural networks in medical imaging is increasing rapidly. However, few teams focus on SBO. The only published classification models have been produced for standard abdominal radiographs. No studies have used CT or 3D models, apart from our preliminary study on ZTs, despite the recognized advantages of CT for the diagnosis of SBO and the likely contribution of 3D models, which may be comparable to that of multiplanar reconstruction for the analysis of images in multiple planes of space.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 8000
• Est. completion date: December 31, 2023
• Est. primary completion date: September 9, 2022
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• 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

Exclusion Criteria:

• 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

Related Conditions & MeSH terms

• Intestinal Obstruction
• Small Bowel Obstruction

Locations

Country	Name	City	State
France	Central for Visual Computing - OPIS Inria group	Gif-sur-Yvette
France	Groupe Hospitalier Paris Saint-Joseph	Paris

Sponsors (1)

Lead Sponsor	Collaborator
Groupe Hospitalier Paris Saint Joseph

Country where clinical trial is conducted

France, 

References & Publications (8)

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. — View Citation

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® Suspected Small-Bowel Obstruction. J Am Coll Radiol. 2020 May;17(5S):S305-S314. doi: 10.1016/j.jacr.2020.01.025. — View Citation

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. — View Citation

Hodel J, Zins M, Desmottes L, Boulay-Coletta I, Jullès 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. — View Citation

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. — View Citation

Montagnon E, Cerny M, Cadrin-Chênevert 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. Review. — View Citation

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, Søreide 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. Review. — View Citation

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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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	Automatic differentiation of functional vs. mechanical occlusions	This outcome corresponds to the detection of functional vs. mechanical occlusions.	Year 1
Secondary	Algorithm for surgical indication	This outcome corresponds to the performance of the clinical-radio-biological algorithm for prediction of surgery.	Year 1
Secondary	Analysis via radiomics of junction zones	This outcome corresponds to the analysis via radiomics of the junction zones of mechanical digestive occlusions (the junction zones are the zones where the dilation-flat transition is located, thus the zone where the obstruction is located): Adhesions vs. flanges: new radiological signs?; Improved performance of surgery prediction.	Year 1
Secondary	Automated detection of junction areas	This outcome corresponds to the performance of automatic detection in identifying the junction zones of mechanical digestive obstructions.	Year 1