clinical study

A Prospective Randomized Clinical Trial for Measuring Radiology Study Reporting Time on Artificial Intelligence-Based Detection of Intracranial Hemorrhage in Emergency Care CT

Materials & Methods

A total of 620 consecutive non-contrast head CT scans from CT scanners used for inpatient and emergency room patients at a large academic hospital. Immediately following image acquisition, scans were automatically analyzed for the presence of ICH using commercially available software (Aidoc, Tel Aviv, Israel). Cases Identified as positive for ICH by AI (ICH-AI+) were automatically flagged in the radiologists’ reading worklists, where flagging was randomly switched off with a probability of 50%. Study turnaround time (TAT) was measured automatically as the time difference between study completion and first clinically communicated study reporting, with timestamps for these events automatically retrieved from various radiology IT systems.

Results

TATs for flagged cases (73 ± 143 min.) were significantly lower than TATs for non-flagged (132 ± 193 min.) cases (p<0.05, one-sided t-test), where 105 of the 122 ICH-AI+ cases were true positive reads. Total sensitivity, specificity, and accuracy over all analyzed cases were 95.0%, 96.7%, and 96.4%, respectively.

Conclusions

Automatic identification of ICH reduces study TAT for ICH in emergent care head CT settings, and can improve clinical management of ICH by accelerating clinically indicated therapeutic interventions.

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