Accuracy of detecting critical findings using abbreviated brain MRI scan protocols as a prerequisite for AI-driven
Kaining Sheng1, Silvia Ingala2, Andreas Hjelm Brandt2
1Department of Radiology, Copenhagen University Hospital Rigshospitalet, Copenhagen 2100, Denmark; Department of Clinical Medicine, University of Copenhagen, Copenhagen 2200, Denmark.
Purpose:
To evaluate the performance of an AI tool and relevant radiology professionals in detecting brain infarcts, intracranial hemorrhages, and tumors using abbreviated brain MRI scan protocols as prerequisite for an AI-driven workflow that dynamically selects additional imaging sequences based on real-time imaging findings.
Materials And Methods:
A retrospective, consecutively enriched cohort of routine adult brain MRI scans from four Danish hospitals was constructed. Three consultant neuroradiologists, three radiology residents, three MR technologists, and an AI tool detected brain infarcts, hemorrhages, and tumors using an abbreviated 3-sequence protocol (DWI, SWI/T2*-GRE, T2-FLAIR) or 4-sequence protocol (DWI, SWI/T2*-GRE, T2-FLAIR, T1W) in a non-overlapping three-way split cross-over design. Reference findings were established from radiological reports and independent image reviews.
Results:
A total of 414 patients were included (57 years ± 19, 238 men) with 65 confirmed brain infarcts, 65 hemorrhages, and 65 tumors. The AI tool achieved detection sensitivities of 94 % (61 of 65 scans, 95 %CI:85-98 %), 82 % (53 of 65 scans, 95 %CI:70-90 %), and 74 % (48 of 65 scans, 95 %CI:61-84 %) for brain infarcts, hemorrhages, and tumors, respectively, and corresponding specificities of 86 % (300 of 349 scans, 95 %CI:82-89 %), 84 % (293 of 349 scans, 95 %CI:80-88 %), and 62 % (217 of 349 scans, 95 %CI:57-67 %). The tool achieved sensitivities comparable to those of neuroradiologists using abbreviated scan protocols, and it surpassed MR technologists in tumor detection, though its specificities were lower.
Conclusion:
The initial steps towards automated real-time scan adaptation are feasible and can potentially improve detection sensitivity, balancing reduced specificities compared to the current standard of care.
