Retina — مصنف اعتلال الشبكية السكري (MVP)

ملخص المشروع

An educational MVP designed to demonstrate an end-to-end retinal screening workflow. The system provides separate portals for patients and ophthalmologists: patients can book appointments and view their reports, while doctors manage schedules, review images and approve automatically-generated reports. The project included an integrated classification model (converted to an H5 inference file) that accepts retinal images and returns a probability score for diabetic retinopathy.

I built the MVP end-to-end and then supported a group of students by preparing documentation (DB diagrams, table relations, and pipeline notes) and explaining complex operations such as the booking workflow and the image-analysis pipeline.

الميزات الرئيسية

• بوابتان: صفحات حجز وتقارير للمرضى، وإدارة مواعيد ومراجعة صور للأطباء.
• تكامل النموذج: دمج نموذج شبكية خارجي، تحويله إلى H5 للتشغيل، وتوصيله بخط الاستدلال لإرجاع درجات احتمالية DR وبيانات الثقة.
• تقارير ووصفات ديناميكية: إنشاء تقارير معتمدة من الأطباء ووصفات ديناميكية بناءً على نتائج العيادة أو التحليل الآلي للصور.
• دردشة فورية: تواصل بين الطبيب والمريض عبر دردشة تعمل بـ WebSocket (Django Channels) مع غرف خاصة لكل استشارة.
• مخرجات تعليمية: مخططات قواعد البيانات، العلاقات وتوثيق خطوط المعالجة للطلاب والمشرفين.

ملاحظات النموذج والاستدلال

The classification model was provided by a third party and converted to an H5 file for ease of serving. Inference is run server-side and returns a probability score and an optional explainability overlay (saliency/heatmap) that the doctor can inspect before validating results. Low-confidence cases are flagged for manual review and included in the clinician sign-off workflow.

Reports and prescription drafts created from model outputs require clinician approval before being published to the patient's portal.

التعاون الفوري وتجربة المستخدم

Implemented real-time doctor–patient chat and notification channels using WebSockets and Django Channels. Each consultation has a private chat room and event stream for status updates (appointment, report ready, review requested). Frontend interaction is implemented in Vanilla JavaScript for lightweight integration into the portals.

الدعم التعليمي والتوثيق

After delivering the MVP I prepared supporting materials to help students understand and extend the system: database diagrams, table relation explanations, and pipeline walkthroughs (especially for the booking flow and the image-analysis submission/validation cycle).

This material was used as a baseline for student assignments and to onboard new contributors safely and quickly.

المكدس التقني (موجز)

• Model serving: containerized REST endpoints (TF Serving / simple H5 loader)
• Backend: Django & Django REST Framework
• Async & realtime: Redis + Celery, Django Channels for WebSockets
• Frontend: Vanilla JavaScript for lightweight portals
• Data store: PostgreSQL; code & collaboration: GitHub

التأثير والخطوات التالية

• Delivered a working MVP that demonstrates automated retinal screening inside a clinical booking workflow.
• Provided documentation and guidance enabling students to extend the system into production-ready flows.
• Suggested next steps: formal external validation, robust explainability modules, and integration with clinic EHRs for automated reporting.