Machine learning for selecting patients with Crohn's disease for abdominopelvic computed tomography in the emergency
Tom Konikoff1, Idan Goren1, Marianna Yalon2
1Division of Gastroenterology, Rabin Medical Center, Petah Tikva, Israel; Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
Background:
Patients with Crohn's disease (CD) frequently undergo abdominopelvic computed tomography (APCT) in the emergency department (ED). It's essential to diagnose clinically actionable findings (CAF) as they may need immediate intervention, frequently surgical. However, repeated APCT's includes increased ionizing radiation exposure. Guidance regarding APCT performance is mostly clinical and empiric.
Aims:
We used a machine learning (ML) approach for predicting CAF on APCT in the ED.
Methods:
We performed a retrospective cohort study of patients with CD who presented to the ED and underwent APCT. CAF were defined as bowel obstruction, perforation, intra-abdominal abscess or complicated fistula. ML was used to predict the probability of having CAF on APCT, using routine clinical variables.
Results:
Of 101 admissions included, 44 (43.5%) had CAF on APCT. ML successfully identified patients at low (NPV 91.6%, CI-95% 90.6-92.5) and high (PPV 92.8%, CI-95%, 92.3-93.2) risk for CAF (AUROC = 0.774), using beats-per-minute, mean arterial pressure, neutrophil-to-lymphocyte ratio and sex. This allowed the construction of a risk stratification scheme according to patients' probability for CAF on APCT.
Conclusion:
We present a novel artificial intelligence-based approach, utilizing readily available clinical variables to better select patients with CD in the ED for APCT. This might reduce the number of APCTs performed, avoiding related hazards while ensuring high-risk patients undergo APCT.
More Related Videos
07:15Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
Published on: August 16, 2020
07:13Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
Published on: April 18, 2025
Related Concept Videos
01:27Imaging Studies III: Computed Tomography
01:14Imaging Studies I: CT and MRI
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
01:10Computed Tomography
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
01:26Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy
Radionuclide Testing
Radionuclide testing is a sophisticated medical technique for assessing gastrointestinal motility. It focuses on gastric emptying and colonic transit time. Radioactive markers track the movement of food through the digestive system, providing insights into gastrointestinal disorders.
In gastric emptying studies, a meal's liquid and...