AI-Driven Segmentation and Automated Analysis of the Whole Sagittal Spine from X-ray Images for Spinopelvic Parameter Evaluation.

Spinal-pelvic parameters are utilized in orthopedics for assessing patients' curvature and body alignment in diagnosing, treating, and planning surgeries for spinal and pelvic disorders. Segmenting and autodetecting the whole spine from lateral radiographs is challenging. Recent efforts have employed deep learning techniques to automate the segmentation and analysis of whole-spine lateral radiographs. This study aims to develop an artificial intelligence (AI)-based deep learning approach for the automated segmentation, alignment, and measurement of spinal-pelvic parameters through whole-spine lateral radiographs. We conducted the study on 932 annotated images from various spinal pathologies. Using a deep learning (DL) model, anatomical landmarks of the cervical, thoracic, lumbar vertebrae, sacrum, and femoral head were automatically distinguished. The algorithm was designed to measure 13 radiographic alignment and spinal-pelvic parameters from the whole-spine lateral radiographs. Training data comprised 748 digital radiographic (DR) X-ray images, while 90 X-ray images were used for validation. Another set of 90 X-ray images served as the test set. Inter-rater reliability between orthopedic spine specialists, orthopedic residents, and the DL model was evaluated using the intraclass correlation coefficient (ICC). The segmentation accuracy for anatomical landmarks was within an acceptable range (median error: 1.7-4.1 mm). The inter-rater reliability between the proposed DL model and individual experts was fair to good for measurements of spinal curvature characteristics (all ICC values > 0.62). The developed DL model in this study demonstrated good levels of inter-rater reliability for predicting anatomical landmark positions and measuring radiographic alignment and spinal-pelvic parameters. Automated segmentation and analysis of whole-spine lateral radiographs using deep learning offers a promising tool to enhance accuracy and efficiency in orthopedic diagnostics and treatments.

Deep Learning for the Automatic Segmentation of Extracranial Venous Malformations of the Head and Neck from MR Images Using 3D U-Net.

BACKGROUND: It is difficult to characterize extracranial venous malformations (VMs) of the head and neck region from magnetic resonance imaging (MRI) manually and one at a time. We attempted to perform the automatic segmentation of lesions from MRI of extracranial VMs using a convolutional neural network as a deep learning tool. METHODS: T2-weighted MRI from 53 patients with extracranial VMs in the head and neck region was used for annotations. Preprocessing management was performed before training. Three-dimensional U-Net was used as a segmentation model. Dice similarity coefficients were evaluated along with other indicators. RESULTS: Dice similarity coefficients in 3D U-Net were found to be 99.75% in the training set and 60.62% in the test set. The models showed overfitting, which can be resolved with a larger number of objects, i.e., MRI VM images. CONCLUSIONS: Our pilot study showed sufficient potential for the automatic segmentation of extracranial VMs through deep learning using MR images from VM patients. The overfitting phenomenon observed will be resolved with a larger number of MRI VM images.

Glymphatic system dysfunction in patients with early chronic kidney disease.

Introduction: It is a recent finding that glymphatic system dysfunction contributes to various neurological problems. The purpose of this research was to assess the function of the glymphatic system in neurologically asymptomatic early chronic kidney disease (CKD) patients and healthy controls, using diffusion tensor image analysis along perivascular space (DTI-ALPS) index. Methods: In a prospective study, we included patients with early CKD who were asymptomatic for neurological issues and obtained clinical and laboratory data. In all participants, brain magnetic resonance imaging (MRI) with diffusion tensor imaging (DTI) was conducted. We used DSI program for DTI preprocessing and DTI-ALPS index estimation. The DTI-ALPS index was compared between patients with early CKD and healthy controls, and the association between clinical characteristics and the DTI-ALPS index was investigated. Results: Eighteen patients with early CKD and 18 healthy controls were included in this study. Patients with early CKD had lower DTI-ALPS index than healthy controls (1.259 ± 0.199 vs. 1.477 ± 0.232, p = 0.004). In the correlation analysis, the DTI-ALPS index had no significant relationship with other clinical factors. Conclusion: We suggest dysfunction of glymphatic system in patients with early chronic kidney disease using the DTI-ALPS index. This may be related to the pathophysiology of neurological problems including impairment of cognition in patients with early CKD.

Glymphatic Dysfunction in Patients With End-Stage Renal Disease.

BACKGROUND: We aimed to compare glymphatic dysfunction between patients with end-stage renal disease (ESRD) and healthy controls and analyze the correlation between the glymphatic function and clinical characteristics using the diffusion tensor image analysis along with the perivascular space (DTI-ALPS) index. METHODS: We prospectively enrolled neurologically asymptomatic 49 patients with ESRD undergoing dialysis and 38 healthy controls. Diffusion tensor image was conducted using the same 3T scanner, and the DTI-ALPS index was calculated. We compared the DTI-ALPS index between the patients with ESRD and healthy controls. In addition, we conducted a correlation analysis between the clinical characteristics and DTI-ALPS index in patients with ESRD. RESULTS: There were significant differences in the DTI-ALPS index between patients with ESRD and healthy controls. The DTI-ALPS index in patients with ESRD was lower than that in healthy controls (1.460 vs. 1.632, p = 0.003). In addition, there was a significant positive correlation between the DTI-ALPS index and serum parathyroid hormone levels (r = 0.357, p = 0.011). CONCLUSION: We demonstrated glymphatic dysfunction in patients with ESRD, as revealed by the DTI-ALPS index. This study also reveals the feasibility of the DTI-ALPS method to determine glymphatic function in patients with ESRD, which could be used in future research studies.