RSNA 2024
Classification of scans with and without suspicions at high precision
Productivity gain in reporting scans with "high suspicion"
Cost impact of automating normal scan reporting
| Parameter | Description | Hospital A | Hospital B | Hospital C | Hospital D |
|---|---|---|---|---|---|
| Precision (Normal) | Out of all the cases predicted by Al as Normal, what percentage of them are actually Normal | 99% | 97% | 97% | 96% |
| False Discovery Rate(FDR) | Out of all cases segregated by Al as normal, what percentage of them are actually abnormal | 1% | 3% | 3% | 4% |
| Reduction in workload | Percentage of all scans that can be reported as normal by Al | 63% | 55% | 37% | 45% |
| Error rate (in clinical settings) | Out of all scans predicted by radiologist as normal, what percentage of them were actually abnormal | 8% | 6% | 8% | 11% |
Presented at RSNA 2023 : Segregation of normal chest radiographs from abnormal chest radiographs using DeepTek AI: retrospective and prospective analyses. By Pant, R., Gupte, T., Varma, S., Kulkarni, V., & Kharat, A.
US FDA Cleared
CDSCO Approved
Kenya Board Certified
Thai FDA Approved
Malayasia Board Approval
