Configuring the AI Engine

The Neolens AI engine is highly configurable, allowing you to tailor detection sensitivity, analysis depth, and output verbosity to your clinical or research needs.

Understanding Key Configuration Parameters

Neolens AI models provide several configuration parameters to let you tailor how the system interprets, analyzes, and outputs results. The most frequently adjusted parameters include:

1. Detection Sensitivity (`sensitivity`)

Defines how aggressively the model should flag findings.

low (0.3): Reduces false positives but may miss subtle anomalies
medium (0.5): Balanced behavior (default)
high (0.8): Detects even minor anomalies, with higher risk of noise

{
  "sensitivity": "medium",
  "confidence_threshold": 0.5
}

2. Analysis Depth (`depth`)

Controls how deeply the AI should analyze each image.

shallow: Faster analysis, surface-level insights (~2-5 seconds)
standard: Good tradeoff for most use cases (~5-15 seconds, default)
deep: Slower but richer and more exhaustive evaluation (~15-45 seconds)

3. Output Verbosity (`verbosity`)

Specifies the amount of detail included in the response.

minimal: Essential data only (findings, confidence scores)
standard: Includes explanations and context (default)
verbose: Detailed results including intermediate steps
debug: Full raw output with model internals (development only)

4. Model Selection (`model_version`)

Choose specific AI model versions for different use cases:

Model Version	Specialization	Best For
`v3.2.1`	General radiology	X-rays, CT scans (default)
`v3.1.5`	Neuroimaging	MRI brain scans
`v2.8.3`	Cardiology	Cardiac imaging, ECG
`experimental`	Latest features	R&D, testing only

5. Region of Interest (`roi`)

Focus analysis on specific anatomical regions:

{
  "roi": {
    "region": "thorax",
    "coordinates": [100, 50, 400, 300],
    "auto_detect": true
  }
}

⚡ Performance vs Quality Trade-offs

Configuration	Speed	Accuracy	Resource Usage	Best For
`shallow` + `low`	⚡⚡⚡	⭐⭐	Low	Screening, high-volume
`standard` + `medium`	⚡⚡	⭐⭐⭐	Medium	General clinical use
`deep` + `high`	⚡	⭐⭐⭐⭐	High	Critical cases, research

🎯 Configuration by Use Case

Emergency Department Triage

{
  "sensitivity": "high",
  "depth": "shallow",
  "verbosity": "minimal",
  "priority_filter": "critical_only",
  "response_time_target": "< 5s"
}

Research & Development

{
  "sensitivity": "high",
  "depth": "deep",
  "verbosity": "debug",
  "model_version": "experimental",
  "uncertainty_estimation": true
}

Routine Screening

{
  "sensitivity": "medium",
  "depth": "standard",
  "verbosity": "standard",
  "batch_processing": true,
  "false_positive_tolerance": "low"
}

🛠️ Advanced Configuration Options

Custom Thresholds

{
  "thresholds": {
    "anomaly_detection": 0.7,
    "classification": 0.8,
    "measurement_precision": 0.95
  }
}

{
  "multi_modal": {
    "enabled": true,
    "primary_modality": "ct",
    "secondary_data": ["clinical_notes", "lab_results"],
    "fusion_strategy": "weighted_ensemble"
  }
}

Quality Assurance

{
  "qa_settings": {
    "image_quality_check": true,
    "minimum_resolution": "512x512",
    "artifact_detection": true,
    "preprocessing": "auto"
  }
}

📊 Configuration API

Set Configuration

curl -X PUT "https://api.neolens.ai/v1/config" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "sensitivity": "high",
    "depth": "standard",
    "verbosity": "verbose",
    "model_version": "v3.2.1"
  }'

Get Current Configuration

curl -X GET "https://api.neolens.ai/v1/config" \
  -H "Authorization: Bearer YOUR_API_KEY"

Validate Configuration

curl -X POST "https://api.neolens.ai/v1/config/validate" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "sensitivity": "ultra_high",
    "depth": "experimental"
  }'

⚠️ Configuration Warnings

Performance Impact

deep analysis can increase processing time by 3-5x
debug verbosity generates large response payloads (>10MB possible)
experimental models may have unstable behavior

Clinical Considerations

Higher sensitivity increases false positive rates
Lower sensitivity may miss subtle but critical findings
Always validate configuration changes with clinical workflows

🧪 Testing Your Configuration

A/B Testing Framework

{
  "test_config": {
    "baseline": {"sensitivity": "medium", "depth": "standard"},
    "variant": {"sensitivity": "high", "depth": "deep"},
    "sample_size": 100,
    "metrics": ["accuracy", "false_positive_rate", "processing_time"]
  }
}

Configuration Profiles

Save commonly used configurations:

{
  "profiles": {
    "emergency": {...},
    "research": {...},
    "screening": {...}
  }
}

📈 Monitoring Configuration Impact

Track how configuration changes affect:

Accuracy metrics: Sensitivity, specificity, AUC
Performance: Response time, throughput
Clinical outcomes: False positive/negative rates
User satisfaction: Radiologist feedback, workflow efficiency

Best Practices

Start with default settings and adjust incrementally
Test configuration changes on validation datasets before production
Document configuration rationale for regulatory compliance
Monitor performance metrics after configuration updates
Use profiles to quickly switch between use cases

Understanding Key Configuration Parameters​

1. Detection Sensitivity (sensitivity)​

2. Analysis Depth (depth)​

3. Output Verbosity (verbosity)​

4. Model Selection (model_version)​

5. Region of Interest (roi)​

⚡ Performance vs Quality Trade-offs​

🎯 Configuration by Use Case​

Emergency Department Triage​

Research & Development​

Routine Screening​

🛠️ Advanced Configuration Options​

Custom Thresholds​

Multi-Modal Analysis​

Quality Assurance​

📊 Configuration API​

Set Configuration​

Get Current Configuration​

Validate Configuration​

⚠️ Configuration Warnings​

🧪 Testing Your Configuration​

A/B Testing Framework​

Configuration Profiles​

📈 Monitoring Configuration Impact​

🔗 Related Documentation​