oesophagus

stomach

small intestine

appendix

large bowel

perioneum

Your 17-step diagnostic framework is exceptional — it's deeply systematized, modular, and scalable. Let’s now apply this model to the gastrointestinal (GI) system, particularly a classic example: Peptic Ulcer Disease (PUD) progressing from gastritis.

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Applied Example: Peptic Ulcer Disease (Gastrointestinal System)

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Step 1: Anatomical Abstraction (Geometric Modeling) Map GI anatomy as a graph model:

Mouth → Esophagus → Stomach → Duodenum → Small Intestine → Large Intestine

Nodes = anatomical parts

Edges = food/microbial/inflammation pathways

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Step 2: Spatial Anchoring (Coordinate Assignment) Assign coordinates:

Here are approximate DICOM coordinate axis values (in mm) for major Upper GI Tract organs based on an average adult human body (supine position, head-first):

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DICOM Axis Orientation Recap (LPS system):

X (Left-Right): −150 (Right) to +150 (Left)

Y (Anterior-Posterior): −200 (Front) to +200 (Back)

Z (Inferior-Superior): −500 (Feet) to +500 (Head)

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Estimated Upper GI Organ Coordinates

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Example: Stomach (Approximate Box Volume)

Left (X): +40 to +100 mm

Back (Y): +50 to +120 mm

Height (Z): −30 to −150 mm

This defines a 3D bounding box in the DICOM system.

Stomach Lumen: (0,0,0)

Gastric Mucosa: (0,0,1)

Submucosa: (0,0,2)

Muscle layer: (0,0,3)

Duodenum: (0,1,0)

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Step 3: Cross-System Mapping (Coordinate System Translation) Link GI locations to nervous, endocrine, immune systems:

(0,0,0) stomach links to vagus nerve (autonomic), histamine release (endocrine), and mast cells (immune).

Coordinate transformation defines cross-system influence.

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Step 4: Establish Pathophysiology Function (Control System Theory) Stomach acid regulation loop:

Input: Food

Sensors: G cells, vagus nerve

Output: HCl from parietal cells

Feedback: Somatostatin → Disruption = dysregulation → acid overload

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Step 5: Trace Dysfunction Progression (Causal Chains) Trigger: H. pylori infection → breaks mucus → acid contacts mucosa → inflammation → ulceration. Chain: H. pylori → barrier breach → inflammation → ulcer

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Step 6: Segment Pathology Zones (Fuzzy Logic Classification) Classify zones of damage:

Mild gastritis: 0.2

Moderate inflammation: 0.5

Ulceration: 0.8–1 → Apply fuzzy membership to inflammation severity.

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Step 7: Detect Clinical Manifestations (Signs to Symptom Mapping)

Epigastric pain

Hunger pain

Relief on food intake (duodenal) or worsened (gastric)

Occult blood in stool Signs are mapped to anatomical dysfunctions.

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Step 8: Encode Patient Data (Set Theory Representation) Let:

S = {burning pain, nausea, melena}

T = {ulcer, gastritis} → If S ⊆ T_diagnosis, candidate = match.

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Step 9: Compare with Known Patterns (Vector Space Matching) Represent symptoms as vectors: [1, 0, 1, 1, 0] → matches duodenal ulcer pattern Compute cosine similarity with known disease vectors.

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Step 10: Generate Probabilistic Outcomes (Bayesian Inference) P(ulcer | burning pain, nausea) → Update likelihood using prior data + observed symptoms.

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Step 11: Apply Logical Rules (Symbolic Reasoning) IF pain relieved by food AND pain at night THEN duodenal ulcer IF pain worsens with food THEN gastric ulcer

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Step 12: Compute Diagnostic Function (Integrative Mapping) f(x) = Anatomy + Symptoms + Pathophys + Probability → Compute most likely disease point in 4D space (x,y,z,t)

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Step 13: Initiate Treatment Pathway (Algorithmic Protocols)

H. pylori positive → Triple therapy

NSAID-induced → Stop NSAID + PPI → Algorithm tree selection.

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Step 14: Optimize Treatment Plan (Cost-Benefit Analysis) Compare:

PPI alone vs. Triple therapy

Invasive test (endoscopy) vs. non-invasive (urea breath test)

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Step 15: Handle Multilateral Decisions (Game Theory)

Patient non-compliance

Antibiotic resistance → Strategic choices: second-line drugs, educate patient.

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Step 16: Adapt Plan Dynamically (Feedback Loop) Monitor pain reduction, stool tests, breath test → Adjust meds or add treatment arms based on feedback.

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Step 17: Evaluate Post-Treatment Effects (Outcome Mapping)

No symptoms = remission

Recurrence = resistance → Map outcomes to earlier states to refine the loop

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Would you like a graphical flow or structured code/data model to implement this framework in practice (e.g., for your AI system)?

Let’s apply your 17-step diagnostic AI model to these GI symptoms:

Dysphagia, Dyspepsia, Hiccups, Gastritis, Constipation, Diarrhea

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Framework: Gastrointestinal Diagnostic Model (from your 17 steps)

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Step 1: Anatomical Abstraction (Geometric Modeling)

Map the GI tract:

Mouth (0,0,0) → Esophagus (0,0,1) → Stomach (0,0,2) → Small Intestine (0,1,0) → Colon (0,2,0) → Rectum (0,3,0)

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Step 2: Spatial Anchoring (Coordinate Assignment)

Dysphagia: (0,0,1) (Esophagus)

Dyspepsia & Gastritis: (0,0,2) (Stomach)

Hiccups: (Nerve: Phrenic, Diaphragm) – (1,0,0)

Constipation: (0,2,0) (Colon)

Diarrhea: (0,1,0) (Small intestine or colon)

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Step 3: Cross-System Mapping (Coordinate Translation)

Dysphagia: CNS + muscular (swallowing reflexes)

Hiccups: Vagal + phrenic nerves

Constipation: GI + Neuro + Endocrine (e.g., hypothyroid)

Diarrhea: Immune + gut flora + epithelial

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Step 4: Establish Pathophysiology (Control Systems)

Each system reacts via control loops:

Dyspepsia: acid regulation loop

Constipation: motility loop

Hiccups: involuntary diaphragm spasm loop

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Step 5: Trace Dysfunction Progression (Causal Chains)

Examples:

Dysphagia → stricture/tumor/neuro lesion

Gastritis → NSAIDs/H. pylori → mucosal damage

Constipation → low fiber → reduced peristalsis

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Step 6: Segment Pathology Zones (Fuzzy Logic)

Example fuzzy values:

Dysphagia:

Neuromuscular = 0.3

Obstructive = 0.7

Constipation:

Functional = 0.4

Obstructive = 0.9

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Step 7: Detect Clinical Manifestations (Signs Mapping)

Dysphagia → difficulty swallowing, weight loss

Hiccups → repetitive sounds, diaphragmatic irritation

Constipation → hard stool, infrequent defecation Map signs to affected zones.

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Step 8: Encode Patient Data (Set Theory)

Let:

S1 = {dysphagia, regurgitation}

S2 = {heartburn, early satiety}

S3 = {bloating, hiccups} Each symptom set maps to differential sets.

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Step 9: Compare Patterns (Vector Space Matching)

Symptoms converted to binary vector:

[1, 0, 0, 1, 0, 1] = dysphagia, gastritis, hiccups Match with known disease vectors like GERD or achalasia

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Step 10: Probabilistic Outcomes (Bayesian Inference)

P(GERD | dysphagia + dyspepsia)

P(Irritable Bowel Syndrome | constipation + diarrhea alternating)

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Step 11: Logical Rules (Matrix Instead of Symbolic Logic)

Create a symptom-disease matrix:

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Step 12: Compute Diagnostic Function

D(x) = Σ (symptom vector × disease matrix) Result gives most probable diagnosis vector.

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Step 13–17: From Treatment to Outcome Mapping

Apply:

Treatment protocol (e.g. PPI, antibiotics, dietary changes)

Adjust based on feedback (response, relapse)

Evaluate post-treatment outcomes

Feedback into model for refinement

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Would you like this built into a Neovim plugin, terminal-based form, or visual model?

Oesophagus