Parametric model of human body shape and ligaments for patient-specific epidural simulation

OBJECTIVE This work is to build upon the concept of matching a person's weight, height and age to their overall body shape to create an adjustable three-dimensional model. A versatile and accurate predictor of body size and shape and ligament thickness is required to improve simulation for medical procedures. A model which is adjustable for any size, shape, body mass, age or height would provide ability to simulate procedures on patients of various body compositions. METHODS Three methods are provided for estimating body circumferences and ligament thicknesses for each patient. The first method is using empirical relations from body shape and size. The second method is to load a dataset from a magnetic resonance imaging (MRI) scan or ultrasound scan containing accurate ligament measurements. The third method is a developed artificial neural network (ANN) which uses MRI dataset as a training set and improves accuracy using error back-propagation, which learns to increase accuracy as more patient data is added. The ANN is trained and tested with clinical data from 23,088 patients. RESULTS The ANN can predict subscapular skinfold thickness within 3.54 mm, waist circumference 3.92 cm, thigh circumference 2.00 cm, arm circumference 1.21 cm, calf circumference 1.40 cm, triceps skinfold thickness 3.43 mm. Alternative regression analysis method gave overall slightly less accurate predictions for subscapular skinfold thickness within 3.75 mm, waist circumference 3.84 cm, thigh circumference 2.16 cm, arm circumference 1.34 cm, calf circumference 1.46 cm, triceps skinfold thickness 3.89 mm. These calculations are used to display a 3D graphics model of the patient's body shape using OpenGL and adjusted by 3D mesh deformations. CONCLUSIONS A patient-specific epidural simulator is presented using the developed body shape model, able to simulate needle insertion procedures on a 3D model of any patient size and shape. The developed ANN gave the most accurate results for body shape, size and ligament thickness. The resulting simulator offers the experience of simulating needle insertions accurately whilst allowing for variation in patient body mass, height or age.

[1]  Slobodan Ilic,et al.  3D Semantic Parameterization for Human Shape Modeling: Application to 3D Animation , 2013, 2013 International Conference on 3D Vision.

[2]  O. Bamgbade,et al.  Obstetric anaesthesia outcome in obese and non-obese parturients undergoing caesarean delivery: an observational study. , 2009, International journal of obstetric anesthesia.

[3]  Katherine M Flegal,et al.  Mean body weight, height, and body mass index, United States 1960-2002. , 2004, Advance data.

[4]  P. Watson,et al.  Total body water volumes for adult males and females estimated from simple anthropometric measurements. , 1980, The American journal of clinical nutrition.

[5]  Kunwoo Lee,et al.  Parametric human body shape modeling framework for human-centered product design , 2012, Comput. Aided Des..

[6]  S. Heymsfield,et al.  Body circumferences: clinical implications emerging from a new geometric model , 2008, Nutrition & metabolism.

[7]  L. Piers,et al.  Indirect estimates of body composition are useful for groups but unreliable in individuals , 2000, International Journal of Obesity.

[8]  G. Bray,et al.  Relation of central adiposity and body mass index to the development of diabetes in the Diabetes Prevention Program. , 2008, The American journal of clinical nutrition.

[9]  Charlie C. L. Wang,et al.  Parameterization and parametric design of mannequins , 2005, Comput. Aided Des..

[10]  G. Chertow,et al.  Development of a population-specific regression equation to estimate total body water in hemodialysis patients. , 1997, Kidney international.

[11]  M. Morgan,et al.  Massive maternal obesity and perioperative cesarean morbidity. , 1994, American journal of obstetrics and gynecology.

[12]  P. Pompei,et al.  Body composition obtained from the body mass index , 2008, European journal of nutrition.

[13]  A. Çimen,et al.  Methods for Body Composition Analysis in Adults , 2011 .

[15]  T. Kurokawa,et al.  An Isomorphic Polygon Model for Describing Human Body Shape , 2009 .

[16]  S. Selvin,et al.  Does the pattern of postpartum weight change differ according to pregravid body size? , 2001, International Journal of Obesity.

[17]  D D Hood,et al.  Anesthetic and Obstetric Outcome in Morbidly Obese Parturients , 1993, Anesthesiology.

[18]  J. Abbas,et al.  Ligamentum Flavum Thickness in Normal and Stenotic Lumbar Spines , 2010, Spine.

[19]  Venketesh N. Dubey,et al.  Haptic Interface on Measured Data for Epidural Simulation , 2012 .

[20]  J. Wang,et al.  Anthropometry in Body Composition: An Overview , 2000, Annals of the New York Academy of Sciences.

[21]  A. R. Behnke.  ANTHROPOMETRIC EVALUATION OF BODY COMPOSITION THROUGHOUT LIFE , 1963, Annals of the New York Academy of Sciences.

[22]  H. Mikami,et al.  Measurements of ligamentum flavum thickening at lumbar spine using MRI , 2009, Archives of Orthopaedic and Trauma Surgery.

[23]  Neil Vaughan,et al.  Towards a realistic in vitro experience of epidural Tuohy needle insertion , 2013, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[24]  R. Hume,et al.  Relationship between total body water and surface area in normal and obese subjects , 1971, Journal of clinical pathology.

[25]  T. Nagaoka,et al.  Development of realistic high-resolution whole-body voxel models of Japanese adult males and females of average height and weight, and application of models to radio-frequency electromagnetic-field dosimetry. , 2004, Physics in medicine and biology.

[26]  Nadia Magnenat-Thalmann,et al.  An automatic modeling of human bodies from sizing parameters , 2003, I3D '03.

[27]  Li Bai,et al.  Fitting 3D garment models onto individual human models , 2010, Comput. Graph..

[28]  Timothy D Wilson,et al.  Explorable three‐dimensional digital model of the female pelvis, pelvic contents, and perineum for anatomical education , 2010, Anatomical sciences education.

[29]  E Zarzur,et al.  Anatomic studies of the human ligamentum flavum. , 1984, Anesthesia and analgesia.

[30]  Chang Shu,et al.  Estimating 3D human shapes from measurements , 2012, Machine Vision and Applications.

[31]  M. Tovée,et al.  Patterns of subcutaneous fat deposition and the relationship between body mass index and waist-to-hip ratio: implications for models of physical attractiveness. , 2009, Journal of theoretical biology.