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Predictive Models for (BCO) Lameness Using Health, Stress, and Leg Health Parameters in Broilers (2023-EU-PO-1342)

ABSTRACT Stress and lameness negatively affect the health, production, and welfare of animals. The following physiological and non-invasive measures of stress and lameness were measured: core body temperature, corticosterone (CORT) concentrations in serum and feathers, surface temperatures of the head (eye and beak) and legs (hock, shank, and foot) regions by infrared thermography (IRT), leg blood and oxygen saturation (leg O2).  JMP Pro 17 Model Screening platform was used to fit several parametric and machine learning models to the binary response variable (Lame=1) of the 256 study birds on the nine health and stress indicators mentioned above.   We selected K Fold Cross-validation with K=5 and repeated the process twice (N Trials Folds=10).  The best models were the Neural Boosted (Mean AUC=.985 and misclassification rate of zero in 50 validation birds) and the Generalized Regression Lasso (Mean AUC=.975 and misclassification rate of 3 in 50 validation birds). The Stepwise Logistic Regression and most interesting for explaining BCO required only seven of the nine indicators and had a similar overall fit performance as the other two.  Both REG models “agreed on the significant predictor effects,” and when applying Model Comparison to compare them further found them nonsignificant to each other (comparing their AUC’s).

 

 

Hello,  everyone,  and  welcome  to  our  presentation.  My  name  is  Dr  Andy Mauromoustakos.  I'm  a  professor  at  the  Agricultural  Statistics  Lab  at  the  University  of  Arkansas.  My  co- presenter  is  Dr  Shawna  Weimer . She's A ssistant  Professor  at  Poultry  Science  Department  at  the  University  of  Arkansas,  and  she's  the  Animal  Welfare  chairperson  at  the  university.

We're  going  to  talk  to  you  today  about  predictive  models  for  BCO  lameness  using  health  and  stress  and  leg  health  parameters  in  broilers.  Our  presentation  is  going  to  be  short.  We're  going  to  discuss  the  models  that  we  are  fitting,  the  champion  models,  the  ones  that  they  get  the  medals.  We're  going  to  evaluate  them,  and  we're  going  to  have  some  conclusions  in  the  end.  Shawna?

All  right.  This  study  compared  physiological  and  non-invasive  measures  of  stress  and  lameness  in  clinically  healthy  and  BCO— or  b acterial chondronecrosis with osteomyelitis—laden  broilers.  BCO  is  a  leading  cause  of  infectious  lameness  in  broiler  chickens,  with  flock  diagnosis  requiring  euthanasia.   Thus,  there's  a  need  for  technological  innovations  to  detect  health  and  lameness  status  in  animals  to  project  the  disease  likelihood  prior  to  clinical  onset.

In  this  study,  birds  were  raised  in  separate  environmental  chambers  with  either  wood  shavings  on  the  floor,  or  the  litter,  and  a  wire  flooring  model  that  is  validated  to  induce  BCO  lameness.  Nine  non-invasive  measures  of  stress  and  lameness  were  collected  from  256  birds,  male  broilers,  over  several  weeks,  which  included  core  body  temperature,  stress  hormone,  surface  temperatures  of  the  head,  the  eye,  and  the  beak,  and  the  legs,  the  hocks,  the  shank,  and  the  feet,  with  infrared  thermography.  Leg  blood  oxygen  saturation  was  also  measured  with  a  pulse  oximeter.

Of  these  measures,  two we  sought  to  validate.  The  first  was  extraction  of  corticosterone  from  the  feathers.   Corticosterone is  the  major  primary  stress  hormone,  and  the  gold  standard  for  measures  is  blood  serum  concentrations,  which  requires  the  capture  and  restraint  of  the  bird  to  collect  it.  So  if  feather  corticosterone  could  be  validated,  then  we  could  simply  clip  a  feather  and  not  put  the  bird  through  that  stress  of  restraint  and  blood  draw.

The  second  was  the  thermal  images,  of  which  each  pixel  has  its  own  temperature  recorded  and  can  be  used  to  quantify  external  changes  in  skin  temperature  related  to  blood  flow,  offering  a   non-invasive  tool  to  measure  health  and  welfare.  During  stress,  peripheral  blood  is  shunted  to  the  core,  and  we  expected  the  average  pixels  of  the  eye  and  the  beak,  or  Eavg  and  Bavg,  to  be  lower  in  lame  than  sound  birds,  which  we  correlated  with  the  serum  corticosterone.

For  the  thermal  images  of  the  legs,  we  expected  the  average  pixel  temperature  of  the  hock,  the  shank,  and  the  foot  to  be  lower  in  the  lame  birds,  both  for  the  stress  reasons  and  also  for  the  colonization  of  the  bacteria  that  slowed  the  blood  flow,  which  we  correlated  with  the  leg  blood  oxygen  saturation.  There  were  marked  differences  between  lame  and  sound  birds.

Our  objectives  is  to  identify  which  of  these  nine  health  and  stress  indicators  are  important  for  lameness.  We  want  to  build  models,  both  for  prediction  purposes,  but  we  are  in  agriculture,  and  we  like  to  publish  papers  that  try  to  explain  how  our  inputs  are affect  the  response.  We  are  hoping  that  some  of  the  models,  the  traditional  regression  models,  will  do  fairly  well  and  we  can  interpret  those.

In  our  methods,  we  talked  about…  It's  a  balanced  study.  It's  a   match paired experiment  where  every  time  a  sound  bird  is  observed,  a  lame  bird  is  also  observed  in  the  same  indicators.  We  have  an  incidence  of  a  disease  of  0.5.  We're  going  to  take  advantage  of  the  Model  Screening  platform  of  JMP  Pro  17.  We're  going  to  select  the  lame  that  has  values  1  and  0  categorical.   1 stands  for,  yes,  it's   lame. 0  is  the  sound .

We  have  our  nine  predictors  in  here.  We're  going  to  do  the  defaults.  We're  going  to   fit all  of  the  machine  learning  models,  that  they  are  checked.  We're  going  to  select  cross validation.  We're  going  to  have   5-fold  cross validation.  With  our  approximate  250  birds,  we're  going  to  have  about  50  birds  per  fold,  and  we're  going  to  repeat  it  twice.  We  selected  the  random  seed  so  we  can  reproduce  the  results.  Notice  that  I  did  not  add  the  quadratic  terms  and  interaction  terms  to  hopefully  have  easier  interpretations.

When  we  select  this,  and  we  click  " Run ,"  JMP  takes  about  a  minute. I t's  going  to  come  up  with  the  ranking  of  the  models  that  we  have  fitted.  This  is  what  we  call  the  beginning  of  the  end.  We  have  10  different  data  sets  that  we  have  tried.  This  is  the  average  fit  criteria,  higher  the  RS quare, and  is  the  ranking  the  best,  second  best,  third  best.

Hopefully  for  us,  we  expected  that  the   Neural Boosted  model  may  do  a  little  bit  better  than  the  traditional  regression  models,  such  as  the  Penalized  Regression  and  the  Logistic  Regression  models,  but  we  were  happy  to  see  that  these  are  our  close  second.

If  we  wanted  to  see  how  our  best  model  did,  we're  going  to  go  and  see  that  the  best  model— the   Neural Boosted,  that  is  the  best  model  for  predicting  purposes —had  a  misclassification  of  0.  You  can  see  here  both  in  the  training  and  the  validation  that  our  receiver  operating  characteristic  curve  reaches  very  soon  the  1  and  stays at  1,  which  is  extremely  good.  We  see  the  confusion  metrics  that  out  of  the   49  birds,  we're  not  really  misclassifying  any  of  them, so  this  is  a  very  good  model  with  a  very  high  generalized  RS quare  and  all  of  the  other  fit  criteria  that  is  produced  in  here.

But  we  are  more  interested  in  the  regression  type of  models.  The  regression  type  of  models,  the  Generalized  Regression,  we  can  see  that  when  we  use  Lasso   as  the  estimation  method,  the  model  included  all  of  the  variables,  and  we  have  couple  of  non -significant  variables  in  the  model  in  here.  We  can  see  these  non -significant  factors,  such  as  the   FCORT,  you  can  see  it  that  it's  not  crossing  the  horizontal  line.  You  can  see  that  the  year  average  is  not  significant,  but  is  included.  We  can  see  that  this  model  had  approximately   three misclassification, a misclassification  rate  of  about  7 %.

Here  is  the  third  model  that   was...  If  we  had  to  give  it  gold,  silver,  and  bronze,  these  two  will  share  the  second  place.  The  Logistic  Regression  model,  when  we  did  the  step wise  procedure,  decided  not  to  include  the  two  highly  non -significant  factors.  We  can  see  in  our  regression  model,  in  the  Logistic  Regression  model,  that   SCORT  is  the  most  important  variable.  This  model  has  similar  misclassification  rate  of  three,  the  same  as  the  Logistic  Regression.

Here  is  how  the  seven  indicator  variables  are  used  to  predict  the  probability  of  lameness.  What  we  like  about  the  regression  type  of  models  is  that  we  can  get  odds  ratios  that  will  help  us  to  interpret.  For  example,  for  our  most  important  variable,  we  can  see  that  the  odds  of  lameness  is  twice  with  one  unit  of  increase  is  serum  cortisol.

Overall,  we  would  like  to  say  that  the  Logistic  Regression  model   only  used  seven  out  of  the  nine  indicators.  The   Neural Boosted  model  and  the  Generalized  Regression,  both  of  them  used  all  nine  indicators  for  lameness.  All  of  the  models  have  area  under  the  curve of  greater  than  0.9. All  of  the  models  have  a  lower…  The regression  type  of models  had  the  7 %  misclassification  on  the  validation  set,  and  the   Neural Boosted  did  not  have  anything.

We  can  compare  our  three  winner  models  using  the  Model  Comparison  platform  in  JM P.  We  can  see  that  in  terms  of  predicting,  strictly  predicting,  the   Neural Boosted  model  is  significantly  better  than  both  of  the  Generalized  Regression  and  the  regression  model.  The  two  regression  models ,  the  one  with  all  nine  variables versus  the  one  with  the  seven  variables,  are  not  significantly  different  from  each  other.  They  all  had  a  very  similar  area  under  the  curve,  and  they  had  similar  misclassification  of  three  birds.

This  is  our  presentation.  We'd  like  to  thank  you  for  your  attention.  We  have  some  references  that  you  can  find  similar  related  material  to  the  techniques  that  we  used  in  JMP  documentation  that  will  help  you  through  this  process.  Thank  you  for  your  attention.

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