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Key Concepts to Memorize
ANKI Lab = Artificial Intelligence in Communication Disorders
- Applies AI to disorders affecting communication (speech, swallowing)
Central Dogma of Neuroscience / Kist's Framework:
Sensory Input → Brain (Processing) → Behavior (Output)- AI can intervene at any stage: sensing, processing, or behavior analysis
Dysphagia (Swallowing Disorder):
- Dysphagia = difficulty swallowing (Greek: dys = difficult, phagein = to eat)
- Can lead to aspiration (food/liquid enters lungs) → pneumonia, malnutrition
- Analyzed using fluoroscopy (X-ray video) — the clinical gold standard
- AI challenge: automatic, objective analysis of swallowing from video
Key AI Technique — Semantic Segmentation:
- Semantic segmentation = pixel-by-pixel classification of an image
- Every pixel is assigned a class label (e.g., "tongue", "food bolus", "airway")
- More detailed than object detection (which only gives bounding boxes)
Markerless Tracking:
- Track structures (e.g., tongue, hyoid bone) in video WITHOUT physical markers
- Method: compute velocity fields → integrate over time → displacement fields
- Enables tracking anatomical movement without surgical markers or radiation markers
Diffeomorphic Image Registration:
- A method to align (register) two images
- Diffeomorphic = smooth, invertible transformation (no tearing or folding of tissue)
- Preserves anatomical topology
Developmental Speech Disorders:
- Affect 10–20% of children
- Two main types:
- Sensory type: Cannot hear/perceive their own speech correctly (auditory feedback problem)
- Motor type: Knows what to say but has difficulty with execution (motor coordination problem)
Phoneme — Definition:
- Phoneme = the smallest unit of sound in a language that distinguishes meaning
- Example: /p/ vs /b/ in "pat" vs "bat"
TTS — Text-to-Speech:
Text → AI Model → Audio output- AI generates natural-sounding speech from written text
- Application: assistive technology for people who cannot speak
Embedded AI (Resource-Efficient AI):
- Running AI models directly on the device (not sending data to the cloud)
- Constraints: limited memory, limited compute, limited energy (battery-powered devices)
- Same concept as Edge AI / TinyML
- Key trade-off: model accuracy vs. model size/energy cost