Computer-aided analysis of 64- and 320-slice coronary computed tomography angiography: a comparison with expert human interpretation

The international journal of cardiovascular imaging(2018)

Cited 5|Views2
No score
Abstract
Routine use of CCTA to triage Emergency Department (ED) chest pain can reduce ED length of stay while providing accurate diagnoses. We evaluated the effectiveness of using Computer Aided Diagnosis in the triage of low to intermediate risk emergency chest pain patients with Coronary Computed Tomographic Angiography (CCTA). Using 64 and 320 slice CT scanners, we compared the diagnostic capability of computer aided diagnosis to human readers in 923 ED patients with chest pain. We calculated sensitivity, specificity, Positive Predictive Value and Negative Predictive Value for cases performed on each scanner. We calculated the area under the Receiver Operator Curve (ROC) comparing results for the two scanners to Computer Aided Diagnosis performance as compared to the human reader. We examined index and 30 Day outcomes by diagnosis for each scanner and the human reader. 60% of cases could be triaged by the computer. Sensitivity was approximately 85% for both scanners, with specificity at 50.6% for the 64 slice and at 56.5% for the 320 slice scanner (per person measures). The NPV was 97.8 and 97.1 for the 64 and 320 slice scanners, respectively. Results for the four major vessels were similar with negative predictive values ranging from 97 to 100%. The ROC for Computer Aided Diagnosis for the 64 and 320 Slice Scanners, using the human reader as the gold standard was 0.6794 and 0.7097 respectively. The index and 30 day outcomes were consistent for the human reader and Computer Aided Diagnosis interpretation. Although Computer Aided Diagnosis with CCTA cannot serve completely as a substitute for human reading, it offers excellent potential as a triage tool in busy EDs.
More
Translated text
Key words
Automated results processing,Computer aided diagnosis,Computer coronary tomography angiography,Efficiency,Emergency department,Triage
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined