CLINICCARDS: Voxel-Based AI for 3D Dental Diagnostics
Cliniccards AI: Deep Tech Revolution in 3D Dental Diagnostics Radiology accounts for 82% of all diagnostic errors in medicine, with practitioners missing up to 44% of pathologies due to fatigue and physiological limitations. Manual analysis of a single 3D scan (CBCT) takes 15–20 minutes, creating a critical bottleneck amidst a global staff shortage. Furthermore, the current market is compromised by non-GDPR compliant solutions with Russian origins, creating unacceptable data security risks for EU clinics. Cliniccards AI is the first fully GDPR-compliant "Clean AI" cloud platform for automated 3D segmentation and diagnostics. Unlike competitors relying on 2D projections, we utilize a True 3D Voxel-based approach (proprietary 3D CNN/U-Net architectures) to analyze volumetric data. The solution integrates seamlessly into the Cliniccards ERP ecosystem, already used by 4,700+ clinics, ensuring instant market access. Key Innovations: 1. Data Moat: We possess the largest verified private dataset in Eastern Europe (240,000 CT scans), creating an uncopyable advantage for training Deep Learning models. 2. Technological Gap: We solve the "blind spots" of existing AI by implementing Metal Artifact Reduction (MAR) and full segmentation of nerves and sinuses directly in voxel space. 3. Digital Sovereignty: We provide a secure European alternative, ensuring data never leaves safe jurisdictions. Measurable Objectives (TRL 4 → TRL 7) Over 24 months, we aim to achieve: - Diagnostic Excellence: Sensitivity >97.3% for pathologies and >95% segmentation accuracy (IoU) for critical anatomy. - Efficiency: Reducing analysis time from 20 minutes to 2–5 seconds (150x faster). - Commercial Impact: Validating a >20% increase in Case Acceptance Rates via pilot testing and securing MDR Class IIa readiness. Action Plan The project involves finalizing R&D (specifically for artifact reduction), conducting a 3-phase "Living Lab" clinical trial in 50 clinics, and filing 4 patent applications
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Imported from OpenAIRE Ukraine | Grant 101312954.
Open sourceOverview
Grant agreement 101312954 | CLINICCARDS
Open sourceCliniccards AI: Deep Tech Revolution in 3D Dental Diagnostics Radiology accounts for 82% of all diagnostic errors in medicine, with practitioners missing up to 44% of pathologies due to fatigue and physiological limitations. Manual analysis of a single 3D scan (CBCT) takes 15–20 minutes, creating a critical bottleneck amidst a global staff shortage. Furthermore, the current market is compromised by non-GDPR compliant solutions with Russian origins, creating unacceptable data security risks for EU clinics. Cliniccards AI is the first fully GDPR-compliant "Clean AI" cloud platform for automated 3D segmentation and diagnostics. Unlike competitors relying on 2D projections, we utilize a True 3D Voxel-based approach (proprietary 3D CNN/U-Net architectures) to analyze volumetric data. The solution integrates seamlessly into the Cliniccards ERP ecosystem, already used by 4,700+ clinics, ensuring instant market access. Key Innovations: 1. Data Moat: We possess the largest verified private dataset in Eastern Europe (240,000 CT scans), creating an uncopyable advantage for training Deep Learning models. 2. Technological Gap: We solve the "blind spots" of existing AI by implementing Metal Artifact Reduction (MAR) and full segmentation of nerves and sinuses directly in voxel space. 3. Digital Sovereignty: We provide a secure European alternative, ensuring data never leaves safe jurisdictions. Measurable Objectives (TRL 4 → TRL 7) Over 24 months, we aim to achieve: - Diagnostic Excellence: Sensitivity >97.3% for pathologies and >95% segmentation accuracy (IoU) for critical anatomy. - Efficiency: Reducing analysis time from 20 minutes to 2–5 seconds (150x faster). - Commercial Impact: Validating a >20% increase in Case Acceptance Rates via pilot testing and securing MDR Class IIa readiness. Action Plan The project involves finalizing R&D (specifically for artifact reduction), conducting a 3-phase "Living Lab" clinical trial in 50 clinics, and filing 4 patent applications