Deploying artificial intelligence in the detection of adult appendicular and pelvic fractures in the Singapore emergency department after hours: efficacy, cost savings and non-monetary benefits.
10.4103/singaporemedj.SMJ-2023-170
- Author:
John Jian Xian QUEK
1
;
Oliver James NICKALLS
;
Bak Siew Steven WONG
;
Min On TAN
Author Information
1. Department of Radiology, Sengkang General Hospital, Singapore.
- Publication Type:Journal Article
- Keywords:
Artificial intelligence;
emergency department;
fractures;
radiography;
radiology
- MeSH:
Humans;
Singapore;
Emergency Service, Hospital;
Fractures, Bone/diagnostic imaging*;
Artificial Intelligence;
Retrospective Studies;
Adult;
Male;
Female;
Cost Savings;
Middle Aged;
Pelvic Bones/diagnostic imaging*;
Reproducibility of Results;
Aged;
Sensitivity and Specificity;
Radiography
- From:Singapore medical journal
2025;66(4):202-207
- CountrySingapore
- Language:English
-
Abstract:
INTRODUCTION:Radiology plays an integral role in fracture detection in the emergency department (ED). After hours, when there are fewer reporting radiologists, most radiographs are interpreted by ED physicians. A minority of these interpretations may miss diagnoses, which later require the callback of patients for further management. Artificial intelligence (AI) has been viewed as a potential solution to augment the shortage of radiologists after hours. We explored the efficacy of an AI solution in the detection of appendicular and pelvic fractures for adult radiographs performed after hours at a general hospital ED in Singapore, and estimated the potential monetary and non-monetary benefits.
METHODS:One hundred and fifty anonymised abnormal radiographs were retrospectively collected and fed through an AI fracture detection solution. The radiographs were re-read by two radiologist reviewers and their consensus was established as the reference standard. Cases were stratified based on the concordance between the AI solution and the reviewers' findings. Discordant cases were further analysed based on the nature of the discrepancy into overcall and undercall subgroups. Statistical analysis was performed to evaluate the accuracy, sensitivity and inter-rater reliability of the AI solution.
RESULTS:Ninety-two examinations were included in the final study radiograph set. The AI solution had a sensitivity of 98.9%, an accuracy of 85.9% and an almost perfect agreement with the reference standard.
CONCLUSION:An AI fracture detection solution has similar sensitivity to human radiologists in the detection of fractures on ED appendicular and pelvic radiographs. Its implementation offers significant potential measurable cost, manpower and time savings.
