COMPUTATIONAL MODELING OF THE SHOULDER

1
COMPUTATIONAL MODELING OF THE SHOULDER

• Richard E. Hughes, Ph.D.
• Orthopaedic Surgery

2
OUTLINE

• Shoulder anatomy
• Modeling glenoid inclination
• Support vector machine modeling
• New research directions

3
ROTATOR CUFF DISORDERS
4
EPIDEMIOLOGY
5
SHOULDER ANATOMY
6
GLENOID ANATOMY

• Long head of biceps
• Labrum
• Not oval
• Shallow

7
GLENOID MECHANICS
Rockwood, C.A. and Matsen, F.A. III (1998) The
Shoulder
8
OUTLINE

• Shoulder anatomy
• Modeling glenoid inclination
• Support vector machine modeling
• New research directions

9
NORMAL INCLINATION
Compression
Net force
Shear
10
INCREASED INCLINATION
Net force
Compression
Shear
New inclination
11
GLENOID INCLINATION
GLENOID ANGLE (DEG.)
TEAR
NO TEAR
Hughes et al. (2003) CORR 40786-91
12
GLENOID INCLINATION

• Rotator cuff tear repair of 96 shoulders
• Control group of 30 shoulders
• SGAP 10 deg. larger for cuff tear group

Tetreault et al. (2004) J. Ortho. Res. 22
202-207
13
OBJECTIVE

• To determine if glenoid inclination angle
  affects superior humeral head migration

14
THEORY
Deltoid
Supraspinatus
Subscapularis
Infraspinatus teres minor
mg
15
GLENOHUMERAL STABILITY
Superior migration
No migration
16
EFFECT OF INCLINATION
Normal inclination
Increased inclination
17
MINIMUM DELTOID FORCE
Min. force with normal inclination
Min. force with inclination
18
MODEL ASSUMPTIONS

• Static, planar analysis
• All muscle forces constant except deltoid
  (proportional to EMG and area)
• Frictionless contact
• Humerus has smaller diameter than glenoid

19
DATA SOURCES

• Muscle activation during abduction
• Kronberg, M. et al. (1990) Clin. Orthop. 257
  76-85
• Muscle lines of action
• Poppen, N. and Walker, P. (1978) Clin. Orthop.
  135 165-170.
• Humeral head and glenoid radii
• Iannotti, J. et al. (1992) JBJS 74-A 491-500
• Arm mass
• Chaffin, D.B. and Andersson, G.B.J. (1984)
  Occupational Biomechanics

20
MODEL PREDICTIONS
5000
4000
0 abduction
3000
Deltoid force (N)
30 abduction
2000
60 abduction
1000
0
91
95
92
93
94
96
97
98
Glenoid inclination angle (deg.)
21
METHODS

• 8 cadaver shoulders
• Soft tissues removed, except rotator cuff
• Mounted on text fixture in MTS
• 5, 10, and 15 degree wedges inserted
• Forces applied to rotator cuff tendons via ropes
  and pulleys
• Humerus moved superiorly at a rate of 0.127 mm/sec

22
MECHANIAL EFFECT OF GLENOID INCLINATION ANGLE
Wong et al. (2003) J. Shoulder Elbow Surg. 12
360-364
23
AVERAGE ANALYSIS
F
24
STOCHASTIC MODEL
F
y
x
25
PROBABILITY OF MIGRATION
Probmigration ProbF points out of
glenoid ProbF lies
above n Prob Fy/Fx gt ny/nx
26
STOCHASTIC MODEL

• Muscle forces modeled as Gamma distribution
• Mean muscle force during initiation of abduction
  was product of EMG, PCSA, and specific tension
• Variation in muscle force was 31 of mean
• Monte Carlo simulation

27
GAMMA DISTRIBUTION
PROBABILITY
MUSCLE FORCE
28
RESULTS
29
3D EMG-DRIVEN MODEL

• SIMM
• Delft model anatomy
• EMG normalized to MVC reference contractions

30
3D GLENOHUMERAL JOINT ANALYSIS
31
OUTLINE

• Shoulder anatomy
• Modeling glenoid inclination
• Support vector machine modeling
• New research directions

32
OBJECTIVES

• To develop a metric of shoulder function that
  represents a continuum from pathologic to healthy
• To develop a strength-based test for rotator cuff
  tears that is superior to MRI or ultrasound

33
ISOMETRIC ER STRENGTH
34
NORMATIVE SHOULDER STRENGTH

• Healthy volunteers (n120)
• Age 20-78 years
• Dominant and non-dominant sides
• Isometric strength
• Abduction/adduction
• Internal/external rotation
• Flexion/extension

Hughes, R.E. et al. (1999) AJSM 27651-657
35
ROTATOR CUFF TEAR (RCT) PATIENT STRENGTH

• Same protocol as normative data
• Full-thickness RCT
• Measurements
• Pre-op
• 6 months post-op
• 12 months post-op
• Intraoperative tear size measurement
• n37

36
METHODS

• Use isometric shoulder strength measurements for
  asymptomatic shoulders and symptomatic cuff tear
  shoulders
• Model data using a least-squares support vector
  machine (SVM)

37
2D EXAMPLE
x(x1, x2)
x2
EXTERNAL ROTATION STRENGTH (Nm) _at_ NEUTRAL
x1
ABDUCTION STRENGTH (Nm) _at_ 0o ABDUCTION
38
STRENGTH DATA

• Abduction _at_ 30o, 60o, 90o
• Adduction_at_ 30o, 60o, 90o
• External rotation
• 30o IR and 15o abduction
• 0o IR and 90o abduction
• 0o IR and 15o abduction
• 30o ER and 90o abduction
• Internal rotation
• 0o IR and 15o abduction
• 30o ER and 90o abduction
• 30o ER and 15o abduction
• 60o ER and 90o abduction

39
STRENGTH DATA
x1, x2, x3

• Abduction _at_ 30o, 60o, 90o
• Adduction _at_ 30o, 60o, 90o
• External rotation
• 30o IR and 15o abduction
• 0o IR and 90o abduction
• 0o IR and 15o abduction
• 30o ER and 90o abduction
• Internal rotation
• 0o IR and 15o abduction
• 30o ER and 90o abduction
• 30o ER and 15o abduction
• 60o ER and 90o abduction

x4, x5, x6
x7
x8
x9
x(x1 , , x14)
x10
x11
x12
x13
x14
40
SVM MODEL
(no tear data points)
(tear data points)
41
SVM ADVANTAGES

• Can model highly nonlinear relationships in high
  dimensional spaces
• More intuitive than competing machine learning
  methods (i.e. artificial neural networks)
• Very computationally efficient
• Can rigorously represent expert knowledge in
  model formulation

42
SVM APPLICATIONS IN MEDICINE

• Breast tumor identification from ultrasound
  images
• Microarray gene expression classification
• Nosocomial infection detection
• Bioinformatics
• EEG analysis

43
SVM MODELING STEPS

• Identify and prepare data set
• Predict healthy shoulder strength from regression
  models (gender, age, body mass)
• Train SVM
• Test (evaluate) SVM performance

44
ROC CURVE
45
ROC RESULTS
46
SHOULDER METRIC
(no tear data points)
Distance from hyperplane
(tear data points)
47
RESULTS
48
DISCUSSION

• Developed a simple unifying metric for healthy
  shoulder strength based on healthy-pathologic
  continuum
• Exceeded some but not all US studies of detecting
  cuff tears
• Did not exceed diagnostic ability of MRI to
  detect cuff tears

49
LIMITATIONS

• Asymptomatic people assumed to represent intact
  rotator cuff case
• Data based on Mayo Clinic data normative data
  from rural Minnesota volunteers

50
OUTLINE

• Shoulder anatomy
• Modeling glenoid inclination
• Support vector machine modeling
• New research directions

51
NEW RESEARCH DIRECTIONS

• Decision sciences
• Optimization modeling of rehabilitation
• Optimization modeling of fracture fixation
• Computer-assisted orthopaedic surgery
• Simulation
• Medical simulators for training surgeons

52
OPTIMIZATION MODELING OF REHABILITATION
53
OPTIMIZATION MODELING OF REHABILITATION
Maximize
Such that
54
OPTIMIZATION MODELING OF FRACTURE FIXATION

• Distal humeral fracture
• Multiple plate holes
• Screws interfere
• Decision what holes to put screws through?
• Integer program

55
COMPUTER-ASSISTED ORTHOPAEDIC SURGERY
56
COMPUTER-ASSISTED ORTHOPAEDIC SURGERY
57
MEDICAL SIMULATION
58
ACKNOWLEDGEMENTS

• Aaron Silver
• Matt Lungren
• Oleg Svintsitski
• Shawn ODriscoll, M.D., Ph.D.
• Mike Rock, M.D.
• Kai-Nan An, Ph.D.
• Marj Johnson, P.T.

• Linda Gallo
• Andrew Wong
• Joe Langenderfer
• Amy Mell
• Chris Gatti
• Lisa Case Doro
• James Carpenter, M.D.

59
(No Transcript)
60
THANK YOU
61
CONCAVITY COMPRESSION
Lippitt, S.B. et al. (1993) JSES 227-35