Machine learning diagnosis and prediction for cervical myelopathy

• Research type

  Research Study

• Full title

  A point-of-care machine learning algorithm to predict treatment outcomes in patients with cervical myelopathy and machine learning tool for early-stage clinical diagnosis of patients with cervical myelopathy: an ambispective cohort study.

• IRAS ID

  305349

• Contact name

  Santhosh Thavarajasingam

• Contact email

  sgt16@ic.ac.uk

• Sponsor organisation

  Imperial College NHS Healthcare Trust

• Clinicaltrials.gov Identifier

  NA, NA

• Duration of Study in the UK

  1 years, 0 months, 1 days

• Research summary

  The overall aim of this research study is to create a predictive machine learning model that is based on radiological, clinical and biochemical variables, which allows spine surgeons to diagnose CSM earlier and more accurately, as well as allowing them to give patients highly individualised and accurate predictive information regarding treatment outcomes.

  Aims: Early diagnosis of CSM

  1. To identify early clinical predictors of cervical myelopathy diagnosis based on individual patient-reported symptoms by using a retrospective questionnaire, and possibly retrospective analysis of biochemical, radiological and clinical data by means of pattern detection with machine learning, or alternatively multivariate linear regression.
  2. To convert the findings of machine learning model or multivariate regression analysis into a scoring system that can be used clinically to score patients into non-likely, moderately likely or high-likely to have CSM groups to aid early clinical diagnosis and prevent unnecessary use of imaging.

  Aims: Treatment outcome prediction of CSM
  
  1. To prospectively follow a cohort of CSM patients collecting as many relevant clinical, biochemical and radiological independent variables as possible, as well as using the JOACMEQ questionnaire and mJOA before, as outcome variables. The relationship of independent and outcome variables will be analysed by means of conventional methods and machine learning.
  2. To build and train a machine learning model to accurately predict treatment outcome in diagnosed patients.
  3. To use the machine learning model as base to design a scoring tool or point-of-care algorithm that can be used clinically to score patients into non-likely, moderately likely or high-likely to be CSM surgery responders to aid appropriate management of CSM patients
  

• REC name

  Wales REC 2

• REC reference

  21/WA/0353

• Date of REC Opinion

  16 Nov 2021

• REC opinion

  Unfavourable Opinion