TY - JOUR

T1 - Artificial Intelligence based detection of pneumoperitoneum on CT scans in patients presenting with acute abdominal pain

T2 - A clinical diagnostic test accuracy study

AU - Brejnebøl, Mathias W.

AU - Nielsen, Yousef W.

AU - Taubmann, Oliver

AU - Eibenberger, Eva

AU - Müller, Felix C.

N1 - Publisher Copyright: © 2022 The Author(s)

PY - 2022

Y1 - 2022

N2 - Purpose: The primary aim was to investigate the diagnostic performance of an Artificial Intelligence (AI) algorithm for pneumoperitoneum detection in patients with acute abdominal pain who underwent an abdominal CT scan. Method: This retrospective diagnostic test accuracy study used a consecutive patient cohort from the Acute High-risk Abdominal patient population at Herlev and Gentofte Hospital, Denmark between January 1, 2019 and September 25, 2019. As reference standard, all studies were rated for pneumoperitoneum (subgroups: none, small, medium, and large amounts) by a gastrointestinal radiology consultant. The index test was a novel AI algorithm based on a sliding window approach with a deep recurrent neural network at its core. The primary outcome was the area under the curve (AUC) of the receiver operating characteristic (ROC). Results: Of 331 included patients (median age 68 years (Range 19–100; 180 women)) 31 patients (9%) had pneumoperitoneum (large: 16, moderate: 7, small: 8). The AUC was 0.77 (95% CI 0.66–0.87). At a specificity of 99% (297/300, 95% CI: 97–100%), sensitivity was 52% (16/31, 95% CI 29–65%), and positive likelihood ratio was 52 (95% CI 16–165). When excluding cases with smaller amounts of free air (<0.25 mL) the AUC increased to 0.96 (95% CI 0.89–1.0). At 99% specificity, sensitivity was 81% (13/16) and positive likelihood ratio was 82 (95% CI 27 – 254). Conclusions: An AI algorithm identified pneumoperitoneum on CT scans in a clinical setting with low sensitivity but very high specificity, supporting its role for ruling in pneumoperitoneum.

AB - Purpose: The primary aim was to investigate the diagnostic performance of an Artificial Intelligence (AI) algorithm for pneumoperitoneum detection in patients with acute abdominal pain who underwent an abdominal CT scan. Method: This retrospective diagnostic test accuracy study used a consecutive patient cohort from the Acute High-risk Abdominal patient population at Herlev and Gentofte Hospital, Denmark between January 1, 2019 and September 25, 2019. As reference standard, all studies were rated for pneumoperitoneum (subgroups: none, small, medium, and large amounts) by a gastrointestinal radiology consultant. The index test was a novel AI algorithm based on a sliding window approach with a deep recurrent neural network at its core. The primary outcome was the area under the curve (AUC) of the receiver operating characteristic (ROC). Results: Of 331 included patients (median age 68 years (Range 19–100; 180 women)) 31 patients (9%) had pneumoperitoneum (large: 16, moderate: 7, small: 8). The AUC was 0.77 (95% CI 0.66–0.87). At a specificity of 99% (297/300, 95% CI: 97–100%), sensitivity was 52% (16/31, 95% CI 29–65%), and positive likelihood ratio was 52 (95% CI 16–165). When excluding cases with smaller amounts of free air (<0.25 mL) the AUC increased to 0.96 (95% CI 0.89–1.0). At 99% specificity, sensitivity was 81% (13/16) and positive likelihood ratio was 82 (95% CI 27 – 254). Conclusions: An AI algorithm identified pneumoperitoneum on CT scans in a clinical setting with low sensitivity but very high specificity, supporting its role for ruling in pneumoperitoneum.

KW - Acute Abdomen

KW - Artificial Intelligence

KW - CT

KW - Detection

KW - Diagnostic Test Accuracy

KW - Pneumoperitoneum

U2 - 10.1016/j.ejrad.2022.110216

DO - 10.1016/j.ejrad.2022.110216

M3 - Journal article

C2 - 35259709

AN - SCOPUS:85125643885

VL - 150

JO - European Journal of Radiology

JF - European Journal of Radiology

SN - 0720-048X

M1 - 110216

ER -