Reduce Chest X Ray reporting workload by 30-50%

Features Highlights

AI Triaging

Classification of scans with and without suspicions at high precision

Smart Reporting

Productivity gain in reporting scans with "high suspicion"

Driving the Future of Radiology with

 Responsible AI

C. Responsible AI

Build Trust, Measure RoI on AI, Enable Feedback

Cost Saving and productivity gain

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.

Regulatory Approvals

US FDA Cleared

CDSCO Approved

Kenya Board Certified

Thai FDA Approved

Our Research

SGCR WIRES 2022 - A clinical evaluation study employing a deep learning approach to calculate cardiothoracic ratio from chest radiographs

AOCR & KCR 2022 - Deep learning-based Automatic Detection of Pulmonary Nodules on Chest Radiographs

Evaluation study presented by FIT at The Union 2022 - The study reported that each CAD software (total 3 evaluated including Genki v2) performed similarly and achieved less than 5% loss of TB yields.

Impact Stories

Genki leveraged at Greater Chennai Corporation for TB screening

Augmento becomes National AI platform for Singapore - adopted by Synapxe

Transforming radiology worklow at Maxicare Healthcare - Phillipines