Congress:
ECR25
Poster Number:
C-21584
Type:
Poster: EPOS Radiologist (scientific)
DOI:
10.26044/ecr2025/C-21584
Authorblock:
W. Pettit1, M. Ryan2, A. Raginis-Zborowska2, E. Compton2, A. Kumar1; 1Berkshire and Surrey/UK, 2Sydney/AU
Disclosures:
William Pettit:
Nothing to disclose
Melissa Ryan:
Employee: Annalise AI
Alicja Raginis-Zborowska:
Employee: Annalise AI
Emma Compton:
Employee: Annalise AI
Amrita Kumar:
Nothing to disclose
Keywords:
Artificial Intelligence, Oncology, Respiratory system, Plain radiographic studies, Computer Applications-Detection, diagnosis, Cancer
A total of 538 patients were included alongside patient demographic data on age, sex and ethnicity to confirm generalisability. Of the 64 remarkable CXRs, the AI device correctly identified 40/64 (62.5%). Of the 474 unremarkable CXRs, the device correctly identified 409 (85.74%). The sensitivity, specificity, Positive Predictive Value and Negative Predictive Value were found to be 62.5%, 86.3%, 38.10%, and 94.5% respectively. ​
Fig 2: Results table