Working group 3: Patient Modeling and Simulation

• Ruzena Bajcsy—UC Berkeley• Scott L. Bartow—Senatra Home Care Services• Amit Bose—Tyco Healthcare• M.Cenk Cavusoglu—Case Western Reserve Univ.• Robert C. Kircher—Dose Safety Company• Douglas Rosendale—VA • Charles Taylor—Stanford Univ.• Russ Taylor—Johns Hopkins• Harvey Rubin—Univ. of Pennsylvania• David Arney–Univ. of Pennsylvania

Why develop patient models?• Improved health care—outcomes, quality

• Better utilization of health care costs – Prevention, intervention, maximal value of EHR

• More efficient device development– Human studies are expensive– Device manufacturers need models– High entry barriers to developing specific models

• More effective procedure execution– Planning, monitoring, and control

• Training/professional certification

• Patient education and guidance in clinical decision making

• Research

Convincing successes in other fields confirm the value of

modelingproduct developmentsafetycost effectivenessregulatory approval

examples:aerospace industrychemical plantsautomotive

Lessons learned

• Lesson 1 • 1 a.Models exist at 5 levels of spatial scale:• Biochemical/genetic• Cell• Organs• Whole body• In society• 1.b Each model evolves on temporal scale • 1. c At each scale the models involve

hetergeneous structures and physical processes

Examples of “tools”

• Biochemistry/Genes/Cells

Physiome project, DARPA BioComp

• Organs/whole bodyITK open source NIH funded image processing toolkit. “digital astronaut” in planning stageDARPA Virtual Soldier

Lesson 1 continued..

1.d Models are incomplete Incomplete or non-existing mathematical models for physiological

processesInsufficient parameters for most biological processesIncomplete data sets: e.g. quantitative postoperative data not collected 1.d Models must be accessible to the community of practioners—large and heterogeneousto the community of investigatorsto the community of device developersto the community of regulators 1.e Models must accommodate “uniqueness” of each patient but

also must permit aggregation of populations

Lesson 2

Convincing preliminary data show that image based modeling is effective

• at procedural level—training, outcomes (seizure focus ablation, arrhythmia focus ablation, interventional radiology-image guided biopsies, radiation therapy mapping)

• clinically cost effective• at commercial level—some systems are already

in use

Lesson 2.a• Convincing preliminary data show that

physiology based modeling is effectivecritical careintra-operativehome care

• Convincing preliminary data show that patient-in-society based modeling is effective

home careinstitutional carevaccine strategies

Mechanisms to share data, models, tools, results are necessary

Challenges:

2.a Interoperability

2.b Institutional barriers to sharing data, tools

2.c Maintenance of Privacy

2.d Academic reward system

2.e Commercial reward system

Lesson 3

Demonstration cases:(2-5 yr*) Create "Knowledge Portal" Build a foundation for open source environment

ontologylinks to available models, data and device sourcesprotocols for validation

Build and distribute anatomical atlases data exists—VA may be best source combine information from multiple patients generate coordinate system to “place” patientsearchable generate statistical analysispredict outcomes based on individual characteristics and

statisticaloutcomesdevice companies can project scales and sizes

Create protocol manual

detailed written descriptions of specific interventions metrics for evaluation

Statistical Atlases of Patient Anatomy

Training Data Sets

Segmentation

Multiple resolution models

Statistical Analysis

Average model + variation modes

Anatomical Labels

Biomechanics

General Surgical Plans

Outcome data

Electronic Anatomical

Atlas

R. Taylor & J. Yao

APPLICATIONS

• Treatment planning, outcomes analysis, basic research, …

One Application: Bootstrapping Atlas

Training Data Sets

Segmentation

Multiple resolution models

Statistical Analysis

Average model + variation modes

Electronic Anatomical

Atlas

R. Taylor & J. Yao

APPLICATIONS

• Treatment planning, outcomes analysis, basic research, …

Atlas-assisted segmentation

Statistical Atlases of Physiology

Training Data Sets

Signal processing

Analytical models

Statistical Analysis

Average model + variation modes

Signal features

Biology info

Lab data

Outcome data

Electronic Atlas

R. Taylor & J. Yao

APPLICATIONS

• Device design, treatment monitoring, planning, outcomes analysis, basic research, …

0.2

0.22

0.24

0.26

0.28

0.3

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0.34

400 600 800 1000 1200 1400 1600 1800 2000

Red/IR

20 per. Mov. Avg. (Red/IR)

),,( ii SXFX ( , , )X F X S

Fused Statistical Atlases

Training Data Sets

Segmentation

Multiple resolution models

Statistical Analysis

Average model + variation modes

Anatomical Labels

Lab data

General Surgical Plans

Outcome data

Fused Atlas

R. Taylor & J. Yao

APPLICATIONS

• Treatment planning, outcomes analysis, basic research, device design, control, …

0.2

0.22

0.24

0.26

0.28

0.3

0.32

0.34

400 600 800 1000 1200 1400 1600 1800 2000

Red/IR

20 per. Mov. Avg. (Red/IR)

),,( ii SXFX ( , , )X F X S

Another Application: Filling in information

Patient-specific model

FusedElectronic

Atlas

R. Taylor & J. Yao

APPLICATIONS

• Treatment planning, outcomes analysis, basic research, …

Atlas-assisted segmentation

Patient-specific images

0.2

0.22

0.24

0.26

0.28

0.3

0.32

0.34

400 600 800 1000 1200 1400 1600 1800 2000

Red/IR

20 per. Mov. Avg. (Red/IR)

),,( ii SXFX

Augmented models

Research needs

• Understand abstraction– domain specific– technical fix

• Improved techniques for assessing clinically relevant variability in measurements

• Experimental validation of models using:ex vivo and bio-mimetic materials

and systemsanimal modelsclinical data

• Policy—privacy, security, legal, regulatory

Specific recommendations

• (2 yr*) common ontologiesdescriptions of blood vessel branching for predicting cardiovascular surgery outcomes

descriptions of activities of daily living for safe performance in the home by the elderly

• (5 yr*) Statistical/analytical tools—“on the fly” analysis of randomized trialsrisk analysis – procedure/outcome, statistical methods for characterizing variability, abnormality, anatomical variance.

Specific recommendation• (2-5 yrs*) Build teams for the production of high confidence medical devices:

work plan- 1) multidisciplinary academic and industry teams develops model2) team does trials to validate model, publishes studies3) FDA approves model for medical device validation4) team maintains model5) device manufacturer uses model for FDA submissions

Example: SRI / Stanford consortium with 7 medical device manufacturersto develop model of femoral artery stent. Consortium does data acquisition and modeling. Consortium publishes work,

can use for certification, companies buy in and get pre-publication data. Data generated a redesign of stent testing methods and FDA using results in regulatory process

Other examples: Diabetes—insulin pump design Chemotherapy-infusion/intralesional design Pacemaker—control and validation Long term oxygen therapy—delivery systems and monitoring Recomendation: FDA, NSF, NIH, NIST, encourage public/private partnerships

academic/industry/government

Example: insulin pump device

Medical Device

Effector

SensorOrgans

Metabolicprocesses

Apps/w

Organmodels

Effector

SensorMedical Device

Apps/w

Effector

Sensor

Organmodel

sOrgan

models

Effector

Sensor

Apps/w

Plant

Bio chemprocesses

Bio chemprocesses

Plant

Clinicaltrials

Softwaredevelopment

phase

Lab testphase

Organmodels

Model-based Medical Device Software development