Accurate prediction of scoliotic curve progression is crucial for guiding treatment decisions in adolescent idiopathic scoliosis (AIS). Traditional methods of assessing the likelihood of AIS progression are limited by variability and rely on static measurements. This study developed and validated machine learning models for classifying progressive and non-progressive scoliotic curves based on gait analysis using wearable inertial sensors. Gait data from 38 AIS patients were collected using seven inertial measurement unit (IMU) sensors, and hip–knee (HK) cyclograms representing inter-joint coordination were generated. Various machine learning algorithms, including support vector machine (SVM), random forest (RF), and novel deep convolutional neural network (DCNN) models utilizing multi-plane HK cyclograms, were developed and evaluated using 10-fold cross-validation. The DCNN model incorporating multi-plane HK cyclograms and clinical factors achieved an accuracy of 92% in predicting curve progression, outperforming SVM (55% accuracy) and RF (52% accuracy) models using handcrafted gait features. Gradient-based class activation mapping revealed that the DCNN model focused on the swing phase of the gait cycle to make predictions. This study demonstrates the potential of deep learning techniques, and DCNNs in particular, in accurately classifying scoliotic curve progression using gait data from wearable IMU sensors.

Rationale: Swallowing apraxia is defined as dysfunction in oral phase caused by the deficit in the coordination of tongue, lip, and chin movements, without motor weakness, sensory loss, and cognitive decline and has not been reported yet. Patient concerns: A 69-year-old male with personal medical history of ischemic stroke about 10 years ago newly developed right striatocapular infarction. He had a problem in the oral phase of swallowing after recurrent ischemic strokes. Diagnoses: He was diagnosed as swallowing apraxia via bed side examination and videofluoroscopic swallowing study. Intervention: Videofluoroscopic swallowing study was done in this case. Outcomes: Symptoms and findings of VFSS were not improved after 2 months treatment. Lessons: This case implies that a clinician should be alert to swallowing apraxia as a possible cause when a patient with recurrent strokes complains of oral phase dysfunction of swallowing and considers proper diagnostic option such as videofluoroscopic swallowing study.

Gait impairment after stroke limits independence. Powered orthoses may augment task-specific over-ground practice, although clinical uptake is constrained

Lee HH, et al. Brain Neurorehabil. 2025 Dec;18(3):e7. https://doi.org/10.12786/bn.2025.18.e7