Automated measurements of interscrew angles in vertebral body tethering patients with deep learning.

BACKGROUND CONTEXT: Vertebral body tethering is the most popular nonfusion treatment for adolescent idiopathic scoliosis. The effect of the tether cord on the spine can be segmentally assessed by comparing the angle between two adjacent screws (interscrew angle) over time. Tether breakage has historically been assessed radiographically by a change in adjacent interscrew angle by greater than 5° between two sets of imaging. A threshold for growth modulation has not yet been established in the literature. These angle measurements are time consuming and prone to interobserver variability. PURPOSE: The purpose of this study was to develop an automated deep learning algorithm for measuring the interscrew angle following VBT surgery. STUDY DESIGN/SETTING: Single institution analysis of medical images. PATIENT SAMPLE: We analyzed 229 standing or bending AP or PA radiographs from 100 patients who had undergone VBT at our institution. OUTCOME MEASURES: Physiologic Measures: An image processing algorithm was used to measure interscrew angles. METHODS: A total of 229 standing or bending AP or PA radiographs from 100 VBT patients with vertebral body tethers were identified. Vertebral body screws were segmented by hand for all images and interscrew angles measured manually for 60 of the included images. A U-Net deep learning model was developed to automatically segment the vertebral body screws. Screw label maps were used to develop and tune an image processing algorithm which measures interscrew angles. Finally, the completed model and algorithm pipeline was tested on a 30-image test set. Dice score and absolute error were used to measure performance. RESULTS: Inter- and Intra-rater reliability for manual angle measurements were assessed with ICC and were both 0.99. The segmentation model Dice score against manually segmented ground truth across the 30-image test set was 0.96. The average interscrew angle absolute error between the algorithm and manually measured ground truth was 0.66° and ranged from 0° to 2.67° in non-overlapping screws (N=206). The primary modes of failure for the model were overlapping screws on a right thoracic/left lumbar construct with two screws in one vertebra and overexposed images. An algorithm step which determines whether an overlapping screw was present correctly identified all overlapping screws, with no false positives. CONCLUSION: We developed and validated an algorithm which measures interscrew angles for radiographs of vertebral body tether patients with an accuracy of within 1° for the majority of interscrew angles. The algorithm can process five images per second on a standard computer, leading to substantial time savings. This algorithm may be used for rapid processing of large radiographic databases of tether patients and could enable more rigorous definitions of growth modulation and cord breakage to be established.

Probabilistic tractography of the extracranial branches of the trigeminal nerve using diffusion tensor imaging.

PURPOSE: The peripheral course of the trigeminal nerves is complex and spans multiple bony foramen and tissue compartments throughout the face. Diffusion tensor imaging of these nerves is difficult due to the complex tissue interfaces and relatively low MR signal. The purpose of this work is to develop a method for reliable diffusion tensor imaging-based fiber tracking of the peripheral branches of the trigeminal nerve. METHODS: We prospectively acquired imaging data from six healthy adult participants with a 3.0-Tesla system, including T2-weighted short tau inversion recovery with variable flip angle (T2-STIR-SPACE) and readout segmented echo planar diffusion weighted imaging sequences. Probabilistic tractography of the ophthalmic, infraorbital, lingual, and inferior alveolar nerves was performed manually and assessed by two observers who determined whether the fiber tracts reached defined anatomical landmarks using the T2-STIR-SPACE volume. RESULTS: All nerves in all subjects were tracked beyond the trigeminal ganglion. Tracts in the inferior alveolar and ophthalmic nerve exhibited the strongest signal and most consistently reached the most distal landmark (58% and 67%, respectively). All tracts of the inferior alveolar and ophthalmic nerve extended beyond their respective third benchmarks. Tracts of the infraorbital nerve and lingual nerve were comparably lower-signal and did not consistently reach the furthest benchmarks (9% and 17%, respectively). CONCLUSION: This work demonstrates a method for consistently identifying and tracking the major nerve branches of the trigeminal nerve with diffusion tensor imaging.

A Deep Learning Tool for Automated Landmark Annotation on Hip and Pelvis Radiographs.

BACKGROUND: Automatic methods for labeling and segmenting pelvis structures can improve the efficiency of clinical and research workflows and reduce the variability introduced with manual labeling. The purpose of this study was to develop a single deep learning model to annotate certain anatomical structures and landmarks on antero-posterior (AP) pelvis radiographs. METHODS: A total of 1,100 AP pelvis radiographs were manually annotated by 3 reviewers. These images included a mix of preoperative and postoperative images as well as a mix of AP pelvis and hip images. A convolutional neural network was trained to segment 22 different structures (7 points, 6 lines, and 9 shapes). Dice score, which measures overlap between model output and ground truth, was calculated for the shapes and lines structures. Euclidean distance error was calculated for point structures. RESULTS: Dice score averaged across all images in the test set was 0.88 and 0.80 for the shape and line structures, respectively. For the 7-point structures, average distance between real and automated annotations ranged from 1.9 mm to 5.6 mm, with all averages falling below 3.1 mm except for the structure labeling the center of the sacrococcygeal junction, where performance was low for both human and machine-produced labels. Blinded qualitative evaluation of human and machine produced segmentations did not reveal any drastic decrease in performance of the automatic method. CONCLUSION: We present a deep learning model for automated annotation of pelvis radiographs that flexibly handles a variety of views, contrasts, and operative statuses for 22 structures and landmarks.

Effect of lesion temperature on the durability of percutaneous radiofrequency rhizotomies to treat trigeminal neuralgia.

OBJECTIVE: Percutaneous radiofrequency rhizotomy is a common procedure for trigeminal neuralgia (TN) that creates thermocoagulative lesions in the trigeminal ganglion. Lesioning parameters for the procedure are left to the individual surgeon's discretion, and published guidance is primarily anecdotal. The purpose of this work was to assess the role of lesioning temperature on long-term surgical outcomes. METHODS: This was a retrospective analysis of patients who underwent percutaneous radiofrequency rhizotomy from 2009 to 2020. Patient data, including demographics, disease presentation, surgical treatment, and outcomes, were collected from medical records. The primary endpoint was the recurrence of TN pain. Univariate and multivariate Cox proportional hazards regressions were used to assess the impact of chosen covariates on pain-free survival. RESULTS: A total of 280 patients who had undergone 464 procedures were included in the analysis. Overall, roughly 80% of patients who underwent rhizotomy would have a recurrence within 10 years. Lower lesion temperature was predictive of longer periods without pain recurrence (HR 1.05, p < 0.001). The inclusion of lesion time, postoperative numbness, prior history of radiofrequency rhizotomy, surgeon, and multiple sclerosis as confounding variables did not affect the hazard ratio or the statistical significance of this finding. Postoperative numbness and the absence of multiple sclerosis were significant protective factors in the model. CONCLUSIONS: The study findings suggest that lower lesion temperatures and, separately, postoperative numbness result in improved long-term outcomes for patients with TN who undergo percutaneous radiofrequency rhizotomies. Given the limitations of retrospective analysis, the authors suggest that a prospective multisite clinical trial testing lesion temperatures would provide definitive guidance on this issue with specific recommendations about the number needed to treat and trial design.

The practical limits of high-quality magnetic resonance imaging for the diagnosis and classification of trigeminal neuralgia.

BACKGROUND: Neurovascular compression (NVC) has been the primary hypothesis for the underlying mechanism of classical trigeminal neuralgia (TN). However, a substantial body of literature has emerged highlighting notable exceptions to this hypothesis. The purpose of this study is to assess the reliability and diagnostic accuracy of high resolution, high contrast MRI-determined neurovascular contact for TN. METHODS: We performed a retrospective, randomized, and blinded parallel characterization of neurovascular interaction and diagnosis in a population of TN patients and controls using four expert reviewers. Performance statistics were calculated, as well as assessments for generalizability using shuffled bootstraps. RESULTS: Fair to moderate agreement (ICC: 0.32-0.68) about diagnosis between reviewers was observed using MRIs from 47 TN patients and 47 controls. On average reviewers performed no better than chance when diagnosing participants, with an accuracy of 0.57 (95% CI 0.40, 0.59) per patient. CONCLUSION: While MRI is useful in determining structural causes in secondary TN, expert reviewers do no better to only slightly better than chance with distinguishing TN with MRI, despite moderate agreement. Further, the causal role of NVC for TN is not clear, limiting the applicability of MRI to diagnose or prognosticate treatment of TN.

Identifying symptomatic trigeminal nerves from MRI in a cohort of trigeminal neuralgia patients using radiomics.

INTRODUCTION: Trigeminal neuralgia (TN) is a devastating neuropathic condition. This work tests whether radiomics features derived from MRI of the trigeminal nerve can distinguish between TN-afflicted and pain-free nerves. METHODS: 3D T1- and T2-weighted 1.5-Tesla MRI volumes were retrospectively acquired for patients undergoing stereotactic radiosurgery to treat TN. A convolutional U-net deep learning network was used to segment the trigeminal nerves from the pons to the ganglion. A total of 216 radiomics features consisting of image texture, shape, and intensity were extracted from each nerve. Within a cross-validation scheme, a random forest feature selection method was used, and a shallow neural network was trained using the selected variables to differentiate between TN-affected and non-affected nerves. Average performance over the validation sets was measured to estimate generalizability. RESULTS: A total of 134 patients (i.e., 268 nerves) were included. The top 16 performing features extracted from the masks were selected for the predictive model. The average validation accuracy was 78%. The validation AUC of the model was 0.83, and sensitivity and specificity were 0.82 and 0.76, respectively. CONCLUSION: Overall, this work suggests that radiomics features from MR imaging of the trigeminal nerves correlate with the presence of pain from TN.