Publications by authors named "Iglna Artha Wiguna"
Article Synopsis
- The study investigated the use of deep learning techniques to assess the severity of cervical spinal cord injuries (SCI) from MRI scans, addressing limitations in the standard assessment method (ASIA Impairment Scale).
- Patients with cervical SCI from 2019 to 2022 had their MRI images labeled by physicians, and a deep convolutional neural network was trained for image segmentation and classification.
- The model demonstrated high accuracy, achieving impressive Dice and IoU scores for spinal cord segmentation and a decent F1 score for classification, indicating potential for more advanced predictive models in future research.
Case: A 48-year-old man fell from a tree and presented to the emergency department with right-sided full hemiplegia and C3 bilateral hypoesthesia. Imaging was remarkable for a C2-C3 fracture-dislocation. The patient was effectively managed surgically with a posterior decompression and 4-level posterior cervical fixation/fusion that included pedicle screws in the axis fixation and lateral mass screws.
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