# Frontend Development Plan

## Overview
The frontend will be a React-based web application that provides a clinical interface for radiologists and medical professionals to interact with the AI tumor detection system.

## Technology Stack
- **Framework**: React with TypeScript
- **UI Library**: Material-UI (MUI) or Ant Design
- **DICOM Viewer**: OHIF Viewer or Cornerstone.js
- **State Management**: React Context API or Redux Toolkit
- **HTTP Client**: Axios
- **Build Tool**: Vite or Create React App

## Key Features

### 1. Authentication & User Management
- User login/logout
- Role-based access control (radiologist, technician, admin)
- Session management

### 2. Patient Management
- Patient list view
- Patient search and filtering
- Patient details view
- Study history

### 3. Study Management
- Study upload interface
- Study list with filtering
- Study viewer with DICOM display
- AI processing status tracking

### 4. AI Results Display
- Tumor detection visualization
- Confidence scores and metrics
- Overlay on DICOM images
- Comparison with previous studies

### 5. Reporting
- Report generation interface
- Template selection
- Report preview and editing
- Export to PDF/DICOM SR

### 6. Dashboard
- Overview of recent studies
- AI processing queue
- System statistics
- Performance metrics

## Component Structure

```
src/
├── components/
│   ├── common/
│   │   ├── Header.tsx
│   │   ├── Sidebar.tsx
│   │   ├── Loading.tsx
│   │   └── ErrorBoundary.tsx
│   ├── patient/
│   │   ├── PatientList.tsx
│   │   ├── PatientDetails.tsx
│   │   └── PatientForm.tsx
│   ├── study/
│   │   ├── StudyList.tsx
│   │   ├── StudyViewer.tsx
│   │   ├── StudyUpload.tsx
│   │   └── AIResults.tsx
│   ├── report/
│   │   ├── ReportGenerator.tsx
│   │   ├── ReportViewer.tsx
│   │   └── ReportTemplates.tsx
│   └── dashboard/
│       ├── Dashboard.tsx
│       ├── Statistics.tsx
│       └── ProcessingQueue.tsx
├── pages/
│   ├── LoginPage.tsx
│   ├── DashboardPage.tsx
│   ├── PatientsPage.tsx
│   ├── StudiesPage.tsx
│   └── ReportsPage.tsx
├── services/
│   ├── api.ts
│   ├── auth.ts
│   ├── patient.ts
│   ├── study.ts
│   └── report.ts
├── hooks/
│   ├── useAuth.ts
│   ├── useApi.ts
│   └── useLocalStorage.ts
├── types/
│   ├── patient.ts
│   ├── study.ts
│   ├── report.ts
│   └── api.ts
└── utils/
    ├── constants.ts
    ├── helpers.ts
    └── dicom.ts
```

## API Integration

The frontend will communicate with the FastAPI backend through RESTful endpoints:

### Authentication
- `POST /api/auth/login` - User login
- `POST /api/auth/logout` - User logout
- `GET /api/auth/me` - Get current user

### Patients
- `GET /api/patients` - List patients
- `POST /api/patients` - Create patient
- `GET /api/patients/{id}` - Get patient details

### Studies
- `GET /api/studies` - List studies
- `POST /api/upload` - Upload DICOM files
- `GET /api/studies/{id}` - Get study details
- `POST /api/predict` - Run AI analysis

### Reports
- `POST /api/reports` - Generate report
- `GET /api/reports/{id}` - Get report
- `GET /api/reports/{id}/pdf` - Download PDF

### Models
- `GET /api/models` - List available AI models

## DICOM Viewer Integration

### Option 1: OHIF Viewer
- Pros: Full-featured, medical-grade, standards compliant
- Cons: Complex integration, larger bundle size
- Implementation: Embed OHIF as iframe or integrate components

### Option 2: Cornerstone.js
- Pros: Lightweight, customizable, good performance
- Cons: Requires more development work
- Implementation: Custom DICOM viewer with overlay capabilities

## Development Phases

### Phase 1: Basic Structure (Week 1)
- [ ] Setup React project with TypeScript
- [ ] Configure routing and basic layout
- [ ] Implement authentication flow
- [ ] Create basic component structure

### Phase 2: Core Features (Week 2)
- [ ] Patient management interface
- [ ] Study upload and listing
- [ ] Basic DICOM viewer integration
- [ ] API integration

### Phase 3: AI Integration (Week 3)
- [ ] AI results display
- [ ] Tumor overlay visualization
- [ ] Processing status tracking
- [ ] Results comparison

### Phase 4: Reporting & Polish (Week 4)
- [ ] Report generation interface
- [ ] Dashboard with statistics
- [ ] UI/UX improvements
- [ ] Testing and bug fixes

## Design Guidelines

### Visual Design
- Clean, medical-professional appearance
- High contrast for medical imaging
- Accessible color schemes
- Responsive design for various screen sizes

### User Experience
- Intuitive navigation
- Fast loading times
- Clear visual feedback
- Keyboard shortcuts for power users

### Performance
- Lazy loading for large datasets
- Image optimization and caching
- Progressive loading for DICOM files
- Efficient state management

## Security Considerations
- HTTPS-only communication
- JWT token-based authentication
- Role-based access control
- Input validation and sanitization
- Secure file upload handling

## Next Steps
1. Setup development environment
2. Create project structure
3. Implement authentication
4. Build core components
5. Integrate DICOM viewer
6. Connect to backend API
7. Implement AI visualization
8. Add reporting features
