Machine learning for selecting patients with Crohn's disease for abdominopelvic computed tomography in the emergency department

Konikoff, Tom; Goren, Idan; Yalon, Marianna; Tamir, Shlomit; Avni-Biron, Irit; Yanai, Henit; Dotan, Iris; Ollech, Jacob

doi:10.1016/j.dld.2021.06.020

Cited by 5 publications

(6 citation statements)

References 25 publications

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“…A recent machine learning–based approach for predicting clinically actionable findings on abdominal CT demonstrated the significance of an elevated NLR and these findings. 32…”

Section: Discussionmentioning

confidence: 99%

“…A recent machine learning-based approach for predicting clinically actionable findings on abdominal CT demonstrated the significance of an elevated NLR and these findings. 32 Patients with CD can present to the ED with anemia, a result of malabsorption and inflammation. Tachycardia, a physiological marker mostly attributed to pain and inflammation, was associated with the presence of an IA in our study (above 97 bpm).…”

Section: Discussionmentioning

confidence: 99%

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Machine learning for prediction of intra-abdominal abscesses in patients with Crohn’s disease visiting the emergency department

Levartovsky

Barash

Ben‐Horin

et al. 2021

Therap Adv Gastroenterol

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Background: Intra-abdominal abscess (IA) is an important clinical complication of Crohn’s disease (CD). A high index of clinical suspicion is needed as imaging is not routinely used during hospital admission. This study aimed to identify clinical predictors of an IA among hospitalized patients with CD using machine learning. Methods: We created an electronic data repository of all patients with CD who visited the emergency department of our tertiary medical center between 2012 and 2018. We searched for the presence of an IA on abdominal imaging within 7 days from visit. Machine learning models were trained to predict the presence of an IA. A logistic regression model was compared with a random forest model. Results: Overall, 309 patients with CD were hospitalized and underwent abdominal imaging within 7 days. Forty patients (12.9%) were diagnosed with an IA. On multivariate analysis, high C-reactive protein (CRP) [above 65 mg/l, adjusted odds ratio (aOR): 16 (95% CI: 5.51–46.18)], leukocytosis [above 10.5 K/μl, aOR: 4.47 (95% CI: 1.91–10.45)], thrombocytosis [above 322.5 K/μl, aOR: 4.1 (95% CI: 2–8.73)], and tachycardia [over 97 beats per minute, aOR: 2.7 (95% CI: 1.37–5.3)] were independently associated with an IA. Random forest model showed an area under the curve of 0.817 ± 0.065 with six features (CRP, hemoglobin, WBC, age, current biologic therapy, and BUN). Conclusion: In our large tertiary center cohort, the machine learning model identified the association of six clinical features (CRP, hemoglobin, WBC, age, BUN, and biologic therapy) with the presentation of an IA. These may assist as a decision support tool in triaging CD patients for imaging to exclude this potentially life-threatening complication.

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“…A recent machine learning–based approach for predicting clinically actionable findings on abdominal CT demonstrated the significance of an elevated NLR and these findings. 32…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Machine learning for prediction of intra-abdominal abscesses in patients with Crohn’s disease visiting the emergency department

Levartovsky

Barash

Ben‐Horin

et al. 2021

Therap Adv Gastroenterol

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“…Artificial intelligence (AI) was introduced in clinical practice several years ago, with the main focus being polyp detection [12,13]; however, AI-based diagnosis has been further explored for polyp characterization [14] and other gastroenterological diseases, such as eosinophilic esophagitis [15] and Crohn's disease [12]. AI has the potential to solve the issue of polyp sizing, and several AI-based concepts have recently been described [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence-based polyp size measurement in gastrointestinal endoscopy using the auxiliary waterjet as a reference

et al. 2023

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Background and study aims Measurement of colorectal polyp size during endoscopy is mainly performed visually. In this work, we propose a novel polyp size measurement system (Poseidon) based on artificial intelligence (AI) using the auxiliary water jet as a measurement reference. Methods Visual estimation, biopsy forceps-based estimation, and Poseidon were compared using a CT-colonography-based silicone model with 28 polyps of defined sizes. Four experienced gastroenterologists estimated polyp sizes visually and with biopsy forceps. Furthermore, the gastroenterologists recorded images of each polyp with the water jet in proximity for the application of Poseidon. Additionally, Poseidon's measurements of 29 colorectal polyps during clinical routine were compared to visual estimates. Results Visual estimation had the largest median percentage error (PE) of 25.2% (95% confidence interval (CI95%): 19.1, 30.4), followed by biopsy forceps-based estimation with median 20% (14.4, 25.6) in the silicone model. Poseidon presented a significantly lower median PE of 7.4% (5, 9.4; p<0.001) than other methods. During routine colonoscopies, Poseidon presented a significantly lower median PE (7.7% 6.1, 9.3) than visual estimation (22.1% 15.1, 26.9; p<0.001). Conclusion In this work, we present a novel AI-based method for measuring colorectal polyp size with significantly higher accuracy than other common sizing methods.

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“…Some research has attempted to predict the need for computed tomography in the evaluation of disease. 21 Selected interventions can be a result of a prediction. 22 The majority of investigational studies are concerned with interpretation of diagnostic tests in relation to final diagnosis and outcome.…”

mentioning

confidence: 99%

“…Investigation and intervention tools can be inputs or outputs of an AI algorithm. Some research has attempted to predict the need for computed tomography in the evaluation of disease [ 21 ]. Selected interventions can be a result of a prediction [ 22 ].…”

mentioning

confidence: 99%

Artificial intelligence decision points in an emergency department

Chang

Chul

2022

Clin Exp Emerg Med

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Machine learning for selecting patients with Crohn's disease for abdominopelvic computed tomography in the emergency department

Cited by 5 publications

References 25 publications

Machine learning for prediction of intra-abdominal abscesses in patients with Crohn’s disease visiting the emergency department

Machine learning for prediction of intra-abdominal abscesses in patients with Crohn’s disease visiting the emergency department

Artificial intelligence-based polyp size measurement in gastrointestinal endoscopy using the auxiliary waterjet as a reference

Artificial intelligence decision points in an emergency department

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