Artificial intelligence prediction of the effect of rehabilitation in whiplash associated disorder.

Autor: Fidalgo-Herrera AJ; Biomechanics department, Fisi(ON) Health Group, RetiaTech, Las Rozas, Spain., Martínez-Beltrán MJ; San Juan de Dios School of Nursing and Physical Therapy, Comillas Pontifical University, Madrid, Spain., de la Torre-Montero JC; San Juan de Dios School of Nursing and Physical Therapy, Comillas Pontifical University, Madrid, Spain., Moreno-Ruiz JA; Departament of Computing, University of Almería, Almería, Spain., Barton G; School of Sport and Exercise Sciences, Liverpool John Moores University, Liverpool, United Kingdom.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2020 Dec 17; Vol. 15 (12), pp. e0243816. Date of Electronic Publication: 2020 Dec 17 (Print Publication: 2020).
DOI: 10.1371/journal.pone.0243816
Abstrakt: The active cervical range of motion (aROM) is assessed by clinicians to inform their decision-making. Even with the ability of neck motion to discriminate injured from non-injured subjects, the mechanisms to explain recovery or persistence of WAD remain unclear. There are few studies of ROM examinations with precision tools using kinematics as predictive factors of recovery rate. The present paper will evaluate the performance of an artificial neural network (ANN) using kinematic variables to predict the overall change of aROM after a period of rehabilitation in WAD patients. To achieve this goal the neck kinematics of a cohort of 1082 WAD patients (55.1% females), with mean age 37.68 (SD 12.88) years old, from across Spain were used. Prediction variables were the kinematics recorded by the EBI® 5 in routine biomechanical assessments of these patients. These include normalized ROM, speed to peak and ROM coefficient of variation. The improvement of aROM was represented by the Neck Functional Holistic Analysis Score (NFHAS). A supervised multi-layer feed-forward ANN was created to predict the change in NFHAS. The selected architecture of the ANN showed a mean squared error of 308.07-272.75 confidence interval for a 95% in the Monte Carlo cross validation. The performance of the ANN was tested with a subsample of patients not used in the training. This comparison resulted in a medium correlation with R = 0.5. The trained neural network to predict the expected difference in NFHAS between baseline and follow up showed modest results. While the overall performance is moderately correlated, the error of this prediction is still too large to use the method in clinical practice. The addition of other clinically relevant factors could further improve prediction performance.
Competing Interests: AFH is affiliated with RetiaTech. There are no patents, products in development or marketed products to declare. This does not alter our adherence to PLOS ONE policies on sharing data and materials.
Databáze: MEDLINE
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