Medical Simulation TRIZ Cases

This case groups respiratory events by timing, severity, and position to create clustering indices for improved sleep disorder assessment.

Independent models classify dementia types for targeted drug response assessment.

Sensors and AI predict stress-related human errors, then adapt interfaces, assistance, and alerts to support operators.

This assay combines nebulization with 3D reconstructed airways epithelium to measure interleukin mRNA and evaluate treatment efficacy.

An initial model adapts subjective corrective-lens tests to each subject, improving reliability and reducing evaluation time.

A physiological model uses motion and heart-rate data to estimate energy expenditure and improve glucose control during activity.

Map patient-specific vessels to perfused tissue for clearer treatment planning.

This decision support system models patient-state transitions to trigger alerts and recommend treatments amid complex ICU data.

This case uses 3D CT models, computational phantom matching, and time-series data to reduce reading errors in tumor progression tracking.

Patient-specific PK-PD modeling ranks anti-infective therapies amid resistant pathogens.

Known spherical geometry and ergonomic leg positioning reduce calibration error and patient movement during dynamic 2D-to-3D reconstruction.

Compare vessel sizes across physiological states to assess vessel reactivity.

Convert 3D ablation data into a 2D map to check lesion coverage and guide additional points for continuous pulmonary vein rings.

Personalized plans use bone registration and movement data to guide joint implantation.

Recommend neurological disease models using integrated phenotype data.

Location-aware reminders and nearby devices sustain diabetes alerts when the primary smartphone is unavailable or offline.

Machine-learned hand, face, and body landmark models quantify movement biomarkers for more accessible Parkinson's assessment.

Direct pseudoinverse learning reduces data needs for exploring neuromodulation stimuli.

Sensors in two facilities continuously collect and compare biometric data, enabling personalized health programs without wearable-device hassle.

Clinical machine learning estimates complication risk and timing in portal hypertension to support individualized treatment decisions.

Compare current and target dentition models as the system screens treatment options and supplies precise implementation parameters.

A weighted frequency set point uses lung time constant, dead space, and spontaneous breathing activity for smooth ventilation.

Garment-integrated inertial sensors generate musculorientation metrics and multimodal feedback during gait training.

Peer-to-peer infectious-area datasets support predictive indoor-outdoor navigation while reducing privacy exposure and reliance on continuous GPS.

Reader-dependent lesion findings can vary; coordinated lesion detection and anatomical analysis produce consistent diagnosis results.

Node2vec embedding, LASSO selection, and classification help distinguish Alzheimer’s disease and mild cognitive impairment from fMRI brain networks.

Outcome-linked patient data supports personalized surgical plans and implant designs.

TRAIL analyzes clinical variables to flag patients at risk from R-CHOP and support alternative treatment selection.

This case uses multiple glucose models and insulin pharmacokinetics to adjust delivery during abrupt and slow glycemic changes.

A staged CT workflow detects tubular voxels, grows constrained regions, and refines lung lobes despite incomplete fissures.

An implantable bone plate uses primary and reference load sensors for wireless, remote tracking of ossification trends.

Differentiable rendering and neural networks streamline accurate 3D dentition reconstruction for orthodontic planning.

This case integrates visual, anatomical, and physiological assays into composite biomarkers for ocular treatment monitoring.

Machine learning compares simulated spinal forces with reference patients to predict surgical success and recommend implant configurations.

Sensors and AI/ML models forecast stress-related human errors, then adapt HMI content and digital assistance in real time.

This case uses air, fat, and motion-rod intensities to calibrate bone mineral density directly from CT images.

This case uses whole slide image biomarkers to adapt radiotherapy plans for tumor-specific dose accuracy and tissue protection.

AI predicts internal organ deformation from biological signals for precise radiotherapy.

Automatic calibration from bone, air, fat, and a motion rod enables precise BMD measurement from CT images without a physical phantom.

Structured protocols link heterogeneous instruments to complete, reproducible analysis.

Machine learning uses saliva bacteria to stratify neurodegenerative disease risk.

Personalized sensor weights calibrate mobile activity models for more accurate recognition.

Tracked tool poses and voxelized CSG create smooth, view-independent cut visuals while reducing recomputation.

Orphan-point projection aligns current images with a dental model to reduce blank areas and support deformation detection.

This case compares new 3D dental scans with prior treatment models to correct segmentation errors and improve aligner fit.

Simulated dialysis scenarios train operators to manage complex events safely.

Cell-free DNA end motifs and fragment sizes are vectorized for AI diagnosis and cancer-type prediction at low read coverage.

The method derives roughness from oral-model curvature and color-codes surfaces to support more precise dental preparation.

Anonymized patient modeling aligns AML transplant referrals with survival and toxicity trade-offs.

Adaptive multimodal chatbot conversations support accessible monitoring and cognitive training.