Aims and objectives
Radiologists read scans in the first-in-first-out (FIFO) order or based on the 'stat' marker based on clinical indications.
This might be suboptimal because scans with critical findings might be on bottom of the work list.
Artificial intelligence(AI) / deep learining algorithms have been built which can detect scans with emergency findings like hemorrhages.
midline shift or fracture[1,
2] from head CT scans.
If these algorithms are used to preread the head CT scans and prioritize scans with critical findings,
it can lead to an improvement...
Methods and materials
We considered a scan critical if it has any of intracranial hemorrhage,
fracture,
mass effect and/or midline shift.
We ran deep learning algorithms which can detect intracranial hemorrhages,
skull fracture,
midline shift and mass effect on a training dataset of 21095 scans.
Predictions of these algorithms were used as features and we trained a random forest model to predict criticality of scan.
The probability score output from this random forest model is used to assign anew scan a criticality score in 0-1.
To validate thealgorithms,...
Results
CQ200 dataset consisted of 214(41[19%] critical) scans.
Simulated trial showed that radiologists would take significantly lower time to read critical scans in algorithmically reordered queue compared to usual FIFO queue (9 mins vs 32 mins,
p<0.01).
Conclusion
Deep learning can successfully reorder radiologists' work-list and this will significantly decrease time to read critical scans.
References
Chilamkurthy,
Sasank,
et al.
"Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study."The Lancet392.10162 (2018): 2388-2396.
Grewal,
Monika,
et al.
"RADnet: Radiologist level accuracy using deep learning for hemorrhage detection in CT scans."Biomedical Imaging (ISBI 2018),
2018 IEEE 15th International Symposium on.
IEEE,
2018.