Kidney Tumor Classification AI
TendersLab developed a state-of-the-art End-to-End (E2E) Deep Learning system serving as a high-fidelity Clinical Decision Support System (CDSS). Based on an open database POC, the model identifies and classifies kidney tumors as benign or malignant with exceptional accuracy.
97%
Diagnostic Accuracy
600+
Public CT Scans Used (POC)
E2E
Deep Learning Model
XAI
Interpretable Results
Methodology & Validation
Built as a robust proof-of-concept utilizing a public dataset of 600 CT scans. The E2E architecture bypasses manual segmentation entirely, vastly reducing processing time and human error.
Clinical Scale-Up at Sheba
Designed for national scale. Sheba Medical Center is currently evaluating the implementation and expansion of this project utilizing their extensive internal, proprietary hospital dataset of 2,000+ CT scans.
Reduction of Unnecessary Surgeries
For initiatives like Clalit's "Clinics of the Future", the model acts as a "second pair of eyes", allowing high-accuracy differential diagnosis that prevents costly, invasive surgeries for benign tumors.
Risk Mitigation via Explainable AI
Integrates "Responsible AI" metrics to provide interpreting physicians with fully interpretable results. This directly minimizes diagnostic ambiguity, reducing the risk of medical malpractice claims.