AI Facial Analysis System Design
Thought for 3 sec
Work done
- Finalized production MPN metadata for CM4 and all generic passives, then verified the updated schematic/BOM properties.
- Created the comprehensive Clinical Imaging Build Document covering PCB-mounted parts, sourcing guidance, integrated imaging modules, calibration protocol, and TGA-oriented controls.
- Updated Pre-Manufacturing Status with the latest validation snapshot and manufacturing export gate.
- ❌ Fully routed / zero DRC errors: blocked; latest review still reports 62 airwire DRC errors.
- ✅ Imaging validation documentation: calibration, module options, fixture controls, and longitudinal capture SOP are documented.
- ✅ Exact parts and sourcing guidance: key ICs/connectors/modules and passives now have MPN guidance.
- ❌ Manufacturing outputs ready: blocked because Gerber/BOM/PnP export should not proceed until airwires are cleared.
The remaining required action is manual/autorouter routing closure for USB, CSI, power, I2C, LED, and connector nets, followed by DRC recheck and manufacturing export generation once airwires are zero.
List airwires
Group DRC errors
Optimize placement
Prioritize routing
Add routing checklist
Refine this doc
Ask about this doc
Clinical Imaging Portal Requirements
Purpose
Develop a controlled imaging portal for AI-driven facial analysis in medspa clinical environments. The system captures standardized client facial photographs and calibrated 3D/depth data to support practitioner-reviewed assessment of skin tone, texture, congestion, pigmentation, and longitudinal facial volume/depth change against 2026 global cosmetic-clinic workflows.
Intended Use and Regulatory Positioning
- Intended use: acquire repeatable facial imaging data in a controlled clinic environment and generate practitioner-reviewed decision-support outputs for aesthetic assessment and longitudinal monitoring.
- The system must not present autonomous diagnosis, treatment selection, or therapeutic claims unless separately validated and cleared in the deployment jurisdiction.
- For TGA-oriented review, claims, labeling, risk management, clinical evaluation, cybersecurity, software lifecycle, and post-market monitoring must be maintained as controlled records.
- AI outputs must be labeled as clinical decision support with practitioner sign-off, confidence/quality flags, limitations, contraindications, and version identifiers.
- Any volume/depth measurement claim must be supported by a locked calibration workflow, verification data, defined accuracy limits, and clinical validation across representative users and skin tones.
Form Factor
- Fixed-position tabletop or wall-mounted facial imaging portal.
- Repeatable client face position using chin/forehead guide or equivalent rigid patient-positioning fixture.
- High-resolution camera system centered on the face with controlled working distance and focus.
- Calibrated stereo/depth imaging geometry with fixed camera baseline, rigid mount, known intrinsics/extrinsics, and field-serviceable calibration checks.
- Multi-zone illumination ring around the camera with diffuse white illumination and optional polarized capture workflow.
- Secure operator interface through embedded compute UI or locked-down clinical workstation.
Hardware Requirements
- Embedded compute module with camera input, local storage, networking, and secure boot capability.
- Primary high-resolution MIPI CSI camera module via FFC connector; target module: Sony IMX477/IMX519-class camera for production.
- Second synchronized MIPI CSI camera connector for calibrated stereo depth capture, wired to the CM4 CAM0 interface.
- Controlled illumination driver with per-zone PWM/current control.
- White LEDs selected for stable color temperature and high CRI; production design must use calibrated LEDs and optical diffuser.
- Secure element for device identity, key storage, signed updates, and encrypted session workflows.
- USB-C service/power input and network connectivity for clinic IT integration.
- Production enclosure must mechanically lock camera baseline, client distance, illumination geometry, and color reference target position.
Controlled Repeat Imaging Protocol
- Verify client consent, operator authentication, device identity, calibration status, and software/model version before capture.
- Enforce fixed geometry: patient positioning fixture engaged, camera-to-face distance in tolerance, face alignment in tolerance, and stereo camera baseline mechanically verified.
- Run illumination calibration before clinical capture: exposure target, LED PWM/current setting, color temperature/CCT, white balance, and diffuser/LED health check.
- Include a color/reference target or calibrated reference region in the capture field when feasible; record whether it passed validation.
- Capture standardized frontal image plus calibrated stereo/depth pair; optional left/right/oblique views may be added in future revisions.
- Perform image-quality checks: focus, exposure, face alignment, motion blur, illumination uniformity, color reference validation, stereo correspondence quality, and depth-map completeness.
- Store raw/original images and raw/depth data separately from AI-derived outputs with audit metadata.
- Session metadata must include operator ID, client ID/pseudonym, date/time, device serial, camera serials, firmware version, model version, working distance, exposure, gain, white balance, CCT/illumination settings, calibration target result, temperature if available, and quality flags.
Facial Volume and Depth Measurement Requirements
- Clinical-grade volume/depth assessment requires dedicated 3D sensing; 2D RGB-only images are insufficient for defensible facial volume-loss measurement.
- Selected method for this prototype: calibrated passive stereo using two matched MIPI CSI camera modules connected to J1 and J3, with fixed baseline and software synchronization/quality gating.
- The depth pipeline must include camera intrinsic calibration, stereo extrinsic calibration, rectification, depth reconstruction, facial landmark registration, longitudinal alignment, and regional volume-change computation.
- Calibration workflow must use a traceable calibration target and store calibration date, operator, target ID, residual reprojection error, baseline, lens configuration, and pass/fail result.
- Accuracy targets must be established in clinical validation before making quantitative volume claims; production requirements should define minimum detectable volume change by facial region and acceptable repeatability limits.
- Depth outputs must be quality-gated and must disclose when texture, hair, occlusion, facial expression, makeup, specular reflection, or poor alignment limits measurement reliability.
AI Clinical Output Categories
- Skin tone: Fitzpatrick-aware tone estimate and uniformity notes.
- Texture: pores, roughness, fine lines, wrinkle/texture gradients.
- Volume loss: calibrated 3D regional contour/volume indicators with practitioner review and confidence bounds.
- Congestion: comedone/blocked-pore and inflammatory congestion indicators.
- Pigmentation: hyperpigmentation, melasma-like patterning, dyschromia, redness/brown spot segmentation.
Clinical/Legal Workflow
- Practitioner review and sign-off are mandatory before treatment recommendations.
- Reports must disclose algorithm version, capture conditions, calibration status, confidence/quality flags, and limitations.
- Validation must include diverse skin tones, ages, facial anatomies, lighting conditions, and cosmetic-clinic populations; benchmark against practitioner-labeled datasets current to 2026 global cosmetic-clinic standards.
- Clinical validation must separately assess RGB appearance categories and 3D/depth volume outputs.
- The system must support traceability from a report back to raw images, depth data, calibration data, software versions, and device configuration used at capture time.
Privacy, Security, and Audit Controls
- Encrypt images, depth data, calibration records, and analysis records at rest and in transit.
- Use secure boot, signed updates, hardware key storage, role-based access, audit logs, retention controls, and clinic-specific access policy.
- Audit logs must record operator authentication, consent capture, acquisition attempts, rejected/accepted captures, calibration events, report generation, export, deletion, and configuration/software changes.
- Support consent capture, data minimization, client access/export/deletion workflows, and clinic-specific retention policy.
- Cybersecurity controls must include unique device identity, vulnerability/update process, credential handling, network hardening, and documented incident response.
Open Validation Items
- Final production camera modules, lenses, lighting parts, and calibration target are not yet locked.
- Stereo measurement accuracy and repeatability targets must be validated before clinical quantitative volume-loss claims.
- DRC/ERC and manufacturing export remain blocked until routing and review convergence are completed.
Welcome 👋
Flux helps you build PCBs faster with an AI teammate!
Create your account to collaborate, stay updated, fork your own version, and get instant answers from our AI agent.