Clinic Management & Cephalometric Analysis

Project ownership & pilot

This project was designed and implemented solely by me (Mohamed Khaled). It was pilot-tested and validated in collaboration with a leading Egyptian dentist who provided clinical feedback and the initial model owner dataset. The pilot confirmed the workflow integration and clinical utility in real clinic settings.

summary : A complete dental clinic management portal integrated with a model that extracts jaw/teeth angle landmarks and derives the clinical information a dentist needs to diagnose the patient from those measurements.

Project gallery

Screenshots, reviewer UI and example outputs.

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Executive summary

We delivered a clinically-focused cephalometric solution that automatically identifies 26+ anatomical landmarks from lateral cephalograms, applies calibrated conversions to clinical units, computes standard cephalometric angles and linear measurements, and produces auditable reports. The system pairs backend model inference with a lightweight, dependency-free frontend reviewer to provide immediate, trustworthy feedback for clinicians and technicians.

Frontend & clinical interaction

The clinician-facing reviewer is implemented in Vanilla JavaScript to remain lightweight, responsive and easy to deploy inside clinical environments. The reviewer provides:

• High-performance image rendering with scalable overlays for landmarks, vectors and quality indicators.
• Interactive landmark editing with immediate recalculation of measurements, enabling clinicians to validate and correct automated outputs without server round-trips.
• User-friendly controls for calibration, measurement display and exporting clinician-approved reports.

Keeping deterministic geometry (measurements and transforms) on the client ensures low-latency feedback during review, while heavy inference workloads remain in the backend where they are fully auditable and versioned.

Mathematical approach (overview)

Measurements are founded on robust geometric procedures and clinically-meaningful calibration. Rather than relying on display pixels, we derive a consistent real-world scale for each image (using DICOM spacing where available or a clinician-supplied calibration marker). All linear distances and angular computations use that calibrated scale so results are reported in millimetres and degrees.

The platform includes reliable methods to convert pixel measurements to clinical units, compute clinically-relevant linear and angular measures, and preserve coordinate fidelity by performing calculations against raw image coordinates. These procedures are thoroughly tested and treated as first-class validation artifacts for clinical acceptance.

Image formats, DICOM and ingestion

The system accepts medical DICOM (preferred) and common image formats (PNG, JPEG, TIFF). For DICOM inputs we read and apply relevant metadata (such as pixel spacing and orientation) to guarantee correct scale and orientation. For non-DICOM images, technicians can supply a calibration marker or known device pixel-size so the same clinical conversions apply.

Ingest pipelines include configurable de-identification, secure handling of PHI, and optional connectors for PACS/EHR integration. Image files and derived artifacts are stored with versioning so every saved measurement can be traced to the underlying image and processing version.

Backend, ML & processing pipeline

Heavy model inference and image pre/post-processing execute on the backend. Key architectural choices include:

• Model inference isolated in versioned worker pools; results carry confidence metadata and model version identifiers.
• Asynchronous job orchestration with queueing for robust scaling and predictable SLAs.
• Post-processing routines that convert model outputs into clinician-ready landmark sets and measurements, with audit logs for all modifications.
• Standardized APIs for upload, job status, results retrieval and clinician review, enabling secure integration with clinic systems.

Security, privacy & regulatory readiness

The product is designed for clinical environments with strict privacy and audit requirements. Highlights:

• Configurable PHI handling and de-identification pipelines at ingest.
• Encrypted storage and transport (TLS + AES-standard encryption for image stores and sensitive fields).
• Role-based access controls and SSO integration supporting clinic user management and audit workflows.
• Immutable audit logs for every measurement and manual adjustment, exportable for QA and regulatory review.

Validation, QA & acceptance

Geometric correctness and reproducibility are treated as core quality gates. Validation procedures include unit tests for geometric primitives, numerical regression checks against a holdout dataset, and clinician acceptance tests (end-to-end sign-off workflows). Each release includes a validation report documenting model version, datasets used and numeric tolerances.

Skills & technologies applied

Standards & operational

• DICOM compatibility and PACS-friendly ingest
• Keycloak (or equivalent) for SSO and RBAC
• Encrypted storage, audit logs and configurable retention policies

Outcomes & next steps

• Approximately 40% reduction in manual landmarking time observed in pilot clinics.
• Improved measurement reproducibility and decreased intra-rater variance.
• Roadmap: federated learning support, 3D cephalometric extensions and formal clinical validation studies.