Medical Simulation TRIZ Cases

Enlarged atria challenge electrode contact and spacing; compass pairs with simultaneous recordings track rotor paths.

Precomputed vehicle simulations supplement scarce event data to predict occupant body-region injuries and support faster triage.

A Reduced Order Model of the periodontal ligament replaces repeated complex simulations, supporting accurate tooth-displacement predictions for appliance planning.

Intersecting line-segment pairs replace costly tissue-conductivity simulations to select precise TTField transducer locations.

Raw and rich CBC data are compressed with autoencoders before classifier training to detect anomalies and biological traits.

Foundation models generate bioprofiles from patient data and possible missing values to improve disease predictions and trial participant selection.

Dental surgical-guide software determines abutment dimensions from virtual crown and implant placement, then searches a library for a compatible recommendation.

Conventional ultrasound struggles to deliver quantitative tissue maps; paired probes and neural reconstruction recover speed-of-sound or attenuation distributions.

A digital characteristic map correlates stimulation signals with output movements, supporting more controllable timing and location of therapy.

Immunoassays for ten protein markers are combined into an MBDA score to quantify PsA activity and guide personalized therapy.

Biomarker and microbiome data combine with symptoms and lifestyle factors to replace biased gut-health advice with personalized nutrition plans.

Technicians can view real-time movement data in augmented reality to adjust orthopaedic devices during patient movement, improving fitting accuracy.

Graph representations of branched biological structures reduce computational burden while supporting accurate, real-time detection of biological characteristics.

Variations in blood boron can misalign BNCT dose delivery; measured concentration feedback corrects irradiation dose to the target.

Machine learning ranks likely cardiac pacing sites from electrophysiological and pace-mapping data, reducing trial-and-error during arrhythmia localization.

Machine learning analyzes normalized head images to estimate dementia symptom progression without invasive MRI, PET, or lumbar puncture procedures.

Separate CGM, SMBG, and combined-data models predict future hypoglycemic episodes as patient data availability varies.

An encoder-decoder neural network generates subject-specific replacement tooth geometry from existing 3D models for accurate aesthetic matching.

Clinical symptom-based assessment lacks objective laboratory verification; this case uses exosome SERS maps and AI averaging for diagnosis.

An AI deep-learning system links disease, gene, and protein terms across sentences, improving relation accuracy and revealing associations in graph data.

Bilateral eye-image analysis adjusts neural-network predictions to improve sensitivity for early corneal conditions without invasive microscopy.

Static prognosis models miss changing disease factors; time-series patient data and LSTM learning forecast acute exacerbation and death.

Rule-based models and DTI miss myocardial motion; joint optimization of cine-MRI movement and fibre parameters improves heart-model realism.

Voxel-level boron assignment and SUV-based region grouping refine BNCT dose simulation, improving tumor targeting while limiting normal-tissue damage.

A sensor-pump processor loop tracks intracranial pressure, induces controlled fluid changes, and estimates compliance continuously.

Dynamic airway models use catheter position measurements and respiratory phase to reduce tracking errors as lung shape changes during bronchoscopy.

3D voxel fold densities and transformer encoding generate protein sequences for target structures, including incomplete structures and NMR ensembles.

Monte Carlo simulation models light paths through a virtual 3D body part to measure HbA1c and glucose without blood collection.

Facial images and questionnaires assess medication effectiveness through exophthalmos, CAS, and diplopia trends, reducing frequent hospital visits.

Multicollinear feature clustering and representative selection create a transparent composite index for noise-robust remote ALS progression monitoring.

An AI digital twin uses patient and environmental data to automate smart-bed repositioning, reducing caregiver handling and discomfort.

Point-cloud models from X-ray or CT data rapidly reproduce patient-specific deformities for multi-plane planning and spinal-device customization.

Scan-based 3D CFD paired with a lumped cardiovascular model and rhythm generator evaluates AF progression through left atrial wall shear stress.

A predefined current profile and temperature feedback enable rapid tissue ablation while limiting dangerous steam-pop risk.

A trained AI model boosts first-pass dose calculations toward Monte Carlo accuracy while reducing treatment-planning time.

Physiological monitors trigger a rescue-drug bolus when preset criteria and confidence levels are met, with an emergency button for manual dosing.

Variable sensor sensitivities are addressed with adaptive filtering, trend updates, and temperature compensation for accurate glucose monitoring.

Manual prosthesis-target tooth selection slows dental CAD/CAM; margin-line extraction from paired intraoral images automates recognition and selection.

Multispectral eye data simulates prescribed and deviated geographic atrophy treatments, making adherence benefits visible to patients.

Manifold learning reuses common biomarkers across diseases to support accurate diagnosis and treatment prediction from small training datasets.

Multiple-frequency measurements feed a computing model that delivers real-time specimen health and purchase recommendations.

Machine learning identifies B. burgdorferi antigenic peptides to improve Lyme disease detection and reduce false negatives in patient samples.

This case converts 2D spinal images into 3D models to pre-bend rods for thoracic kyphosis despite in vivo deformation.

Voxel-level boron concentration modeling combines medical images with neutron dose simulation to improve tumor targeting while limiting normal tissue damage.

Combining European and East Asian GWAS data, this SNP scoring approach uses APOE and demographics to predict Alzheimer’s and amnestic MCI risk groups.

Multiple AR interfaces exchange coordinate data to align dental instruments while reducing parallax and occlusion during surgery.

Using patient anatomy, valve geometry, and flow metrics, predictive simulation estimates TAVR thrombosis risk before valve selection and implantation.

An organ model estimates post-treatment features and sets display conditions for evaluating heart valve treatment positions.

This case uses a pre-generated auxiliary line to guide mesh generation and create a more natural gingiva-palate interface.

Personalized 3D vessel models simulate exercise blood flow for non-invasive stenosis assessment.