Predictive AI for Coronary Conditions - Nestack Technologies Pvt Ltd

Table of Contents

Pin Point Accurate

Indetailed Anlaysis

Clear Report Presentation

We created this project to solve the fragmented workflows common in medical imaging programs. Today, hospital consoles capture SFE runs, researchers manually review image quality, clinical teams re-enter procedure notes, regulatory teams search through extensive FDA guidance, and scanner calibration issues often go unnoticed until image quality is affected.

Our solution brings these disconnected processes into a single intelligent workflow, connecting imaging data, clinical documentation, hardware health, and regulatory evidence. From image capture to a 510(k)-ready evidence package and communication back to the capture site, the entire process remains connected, efficient, and traceable.

Regulatory Review

Medical-device teams have to prove things — labeling quality, training-data provenance, traceability for AI/ML SaMD — and the evidence is scattered across eighty-page FDA guidance documents. Regulatory Review is the compliance copilot. In Ask Your Documents, you load the FDA AI/ML SaMD action plan, ask a question in plain English (“What evidence is required for an AI-assisted image-labeling system?”), and get a drafted answer with citations back to the exact guidance sections — plus similar prior Q&A pulled from the Knowledge Base. The Evidence Pack tab is where the flywheel pays off: one click assembles a regulatory-ready pack for the active case, pulling KB frame counts, annotated frames, the Clinical Extractor’s structured fields, and dual-
reviewer QA agreement, then maps them to design controls and a 510(k) narrative against an H2 2026 clearance target. A Live Intelligence Feed tags funding, regulatory, competitive, and partnership news for investor and strategy conversations. Instead of reading the guidance, you ask it a question — and the case builds its own evidence.

Predictive Maintenance

Everything downstream in the flywheel depends on one fragile thing: a 1 mm scanning fiber staying in calibration. When a console drifts, images blur, labels go wrong, and clinical and regulatory evidence quietly weakens — usually noticed only after the bad frames are already in the pipeline. Predictive Maintenance monitors every SFE console like a fleet of machines, tracking calibration drift (mm), piezo temperature (°C), and fiber tension (N) on live charts across all four demo sites. The logic is deliberately simple to explain on stage: the last twenty readings set a normal baseline, and the most recent three are flagged only if they’re all above mean + 1.5σ and still rising. BHL-R&D-01 at the Manufacturing R&D lab is the hero — it trips a CRITICAL alert while the three clinical consoles stay low-risk, so the demo shows the system catching a real problem without lighting up everything red. Run AI diagnostic turns raw sensor numbers into a plain-language ops brief with a recommended action and an “inspect within ~X hours” window anyone can act on.