ArticleAnnals of biomedical engineering2023
Prediction of Temperature and Loading History Dependent Lumbar Spine Biomechanics Under Cyclic Loading Using Recurrent Neural Networks.
Article in Annals of biomedical engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed.
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7 authors.
Funding
Abstract
Extended-duration cyclic loading of the spine is known to be correlated to lower back pain (LBP). Therefore, it is important to understand how the loading history affects the entire structural behavior of the spine, including the viscoelastic effects. Six human spinal segments (L4L5) were loaded with pure moments up to 7.5 Nm cyclically for half an hour, kept unloaded for 15 min, and loaded with three cycles. This procedure was performed in flexion-extension (FE), axial rotation (AR), and lateral bending (LB) and repeated six times per direction for a total of 18 h of testing per segment. A Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) was trained to predict the change in the biomechanical response under cyclic loading. A strong positive correlation between the total testing time and the ratio of the third cycle to the last cycle of the loading sequence was found (BT: [Formula: see text] = 0.3469, p = 0.0003, RT: [Formula: see text] =0.1988, p = 0.0377). The moment-range of motion (RoM) curves could be very well predicted with an RNN ([Formula: see text]=0.988), including the correlation between testing time and testing temperature as inputs. This study shows successfully the feasibility of using RNNs to predict changing moment-RoM curves under cyclic moment loading.
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