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Abstract #1420

A machine learning method for tissue characterisation in the human thigh

Terence Jones1,2, Sarah Wayte3, Abhir Bhalerao4, Nicola Gullick5, and Charles Edward Hutchinson1,2

1Medical School, University of Warwick, Coventry, United Kingdom, 2Radiology, University Hospitals Coventry & Warwickshire NHS Trust, Coventry, United Kingdom, 3Medical Physics, University Hospitals Coventry & Warwickshire NHS Trust, Coventry, United Kingdom, 4Computer Science, University of Warwick, Coventry, United Kingdom, 5Department of Rheumatology, University Hospitals Coventry & Warwickshire NHS Trust, Coventry, United Kingdom

Inflammatory idiopathic myositis is a debilitating inflammatory muscle condition. Diagnosis relies on a battery of tests, but monitoring of disease severity can be challenging. We present a novel machine learning approach to classifying tissues using multi-parametric analysis of routine MRI sequences. A logistic regression model was trained to predict tissue type based on T1 and STIR signal intensity and 10-fold cross-validated. The system attained 93.8% sensitivity and 96.9% specificity overall (ROC area 0.991). Testing of this model showed a low level of ostensible muscle inflammation in 9/11 asymptomatic controls – likely due to misclassification of vessels.

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