Deep Learning Algorithm Trained on Brain Magnetic Resonance Images and Clinical Data to Predict Motor Outcomes of Patients With Corona Radiata Infarct

Abstract

The early and accurate prediction of the extent of long-term motor recovery is important for establishing specific rehabilitation strategies for stroke patients. Using clinical parameters and brain magnetic resonance images as inputs, we developed a deep learning algorithm to increase the prediction accuracy of long-term motor outcomes in patients with corona radiata (CR) infarct. Using brain magnetic resonance images and clinical data obtained soon after CR infarct, we developed an integrated algorithm to predict hand function and ambulatory outcomes of the patient 6 months after onset. To develop and evaluate the algorithm, we retrospectively recruited 221 patients with CR infarct. The area under the curve of the validation set of the integrated modified Brunnstrom classification prediction model was 0.891 with 95% confidence interval (0.814–0.967) and that of the integrated functional ambulatory category prediction model was 0.919, with 95% confidence interval (0.842–0.995). We demonstrated that an integrated algorithm trained using patients’ clinical data and brain magnetic resonance images obtained soon after CR infarct can promote the accurate prediction of long-term hand function and ambulatory outcomes. Future efforts will be devoted to finding more appropriate input variables to further increase the accuracy of deep learning models in clinical applications.

Identification of L5 vertebra on lumbar spine radiographs using deep learning

“…Compared with these traditional methods for counting vertebra number, our recently developed deep learning model showed high accuracy (93.3%) for detecting the L5 vertebra on anteroposterior lumbar spine radiographs. Deep learning models are characterized by multilayer structures with multiple hidden layers; they thus have a better detection accuracy ability than traditional shallow learning models [8]. We therefore believe that our deep learning model extracts valuable features that differentiate the L5 vertebra from other vertebral levels.…”

“…Deep learning techniques have recently emerged as powerful methods to automatically learn feature representations from data. [8][9][10] In particular, these techniques can substantially improve object detection. 11 Object detection refers to determining whether there are any instances of objects from specified categories in an image; if there are, the spatial location and extent of each object instance are shown via a bounding box.…”

Use of Machine Learning in Stroke Rehabilitation: A Narrative Review

“…Consequently, a regression model that used both low- and high-order functional connectivity predicted the results based on the correlation coefficient (r = 0.54, p = 0.0002). In 2022, Kim et al [ 47 ] used clinical data, including age, sex, modified Brunnstrom classification, functional ambulation score, and Medical Research Council score at an early stage (8–30 days following stroke onset), and MR image data (three images from each patient) of the same 221 corona radiata infarct patients as input to develop the prediction model. This study demonstrated that an integrated algorithm trained using clinical data of patients and brain MR images could improve the accuracy of the prediction of long-term upper extremity function and ambulatory outcomes.…”

Deep Learning in Ischemic Stroke Imaging Analysis: A Comprehensive Review

“…This study demonstrated that the use of deep learning models can accurately predict long-term outcomes and record a significantly higher AUC than the ASTRAL score [ 108 ]. Kim et al developed an integrated modified Brunnstrom algorithm to predict the hand function and the ambulatory outcomes of a patient with corona radiata (CR) at 6 months after onset, using clinical parameters and brain magnetic resonance images as input, with an AUC of 0.891 [ 109 ]. Ding et al employed CNNs to predict the functional outcomes at 3 months poststroke with acute brainstem infarction using clinical features, laboratory features, conventional imaging features (infarct volume and number of infarctions), and DWI neuroimaging features from 1482 patients, which achieved an extremely high AUC of 0.975 [ 110 ].…”