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Image-based Quantification of Postural Deviations on Patients with Cervical Dystonia: A Machine Learning Approach Using Synthetic Training Data

Roland Stenger, Sebastian Löns, Nele Brügge, Feline Hamami, Alexander Münchau, Theresa Paulus, Anne Weissbach, Tatiana Usnich, Max Borsche, Martje G. Pauly, Lara M. Lange, Markus A. Hobert, Rebecca Herzog, Ana Luísa de Almeida Marcelino, Tina Mainka, Friederike Schumann, Lukas L. Goede, Johanna Reimer, Julienne Haas, Jos Becktepe, Alexander Baumann, Robin Wolke, Chi Wang Ip, Thorsten Odorfer, Daniel Zeller, Lisa Harder-Rauschenberger, John-Ih Lee, Philipp Albrecht, Tristan Kölsche, Joachim K. Krauss, Johanna M. Nagel, Joachim Runge, Johanna Doll-Lee, Simone Zittel, Kai Grimm, Pawel Tacik, André Lee, Tobias Bäumer, Sebastian Fudickar

Abstract

Cervical dystonia (CD) is the most common form of dystonia, yet current assessment relies on subjective clinical rating scales, such as the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS), which requires expertise, is subjective and faces low inter-rater reliability some items of the score. To address the lack of established objective tools for monitoring disease severity and treatment response, this study validates an automated image-based head pose and shift estimation system for patients with CD. We developed an assessment tool that combines a pretrained head-pose estimation algorithm for rotational symptoms with a deep learning model trained exclusively on ~16,000 synthetic avatar images to evaluate rare translational symptoms, specifically lateral shift. This synthetic data approach overcomes the scarcity of clinical training examples. The system's performance was validated in a multicenter study by comparing its predicted scores against the consensus ratings of 20 clinical experts using a dataset of 100 real patient images and 100 labeled synthetic avatars. The automated system demonstrated strong agreement with expert clinical ratings for rotational symptoms, achieving high correlations for torticollis (r=0.91), laterocollis (r=0.81), and anteroretrocollis (r=0.78). For lateral shift, the tool achieved a moderate correlation (r=0.55) with clinical ratings and demonstrated higher accuracy than human raters in controlled benchmark tests on avatars. By leveraging synthetic training data to bridge the clinical data gap, this model successfully generalizes to real-world patients, providing a validated, objective tool for CD postural assessment that can enable standardized clinical decision-making and trial evaluation.

Image-based Quantification of Postural Deviations on Patients with Cervical Dystonia: A Machine Learning Approach Using Synthetic Training Data

Abstract

Cervical dystonia (CD) is the most common form of dystonia, yet current assessment relies on subjective clinical rating scales, such as the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS), which requires expertise, is subjective and faces low inter-rater reliability some items of the score. To address the lack of established objective tools for monitoring disease severity and treatment response, this study validates an automated image-based head pose and shift estimation system for patients with CD. We developed an assessment tool that combines a pretrained head-pose estimation algorithm for rotational symptoms with a deep learning model trained exclusively on ~16,000 synthetic avatar images to evaluate rare translational symptoms, specifically lateral shift. This synthetic data approach overcomes the scarcity of clinical training examples. The system's performance was validated in a multicenter study by comparing its predicted scores against the consensus ratings of 20 clinical experts using a dataset of 100 real patient images and 100 labeled synthetic avatars. The automated system demonstrated strong agreement with expert clinical ratings for rotational symptoms, achieving high correlations for torticollis (r=0.91), laterocollis (r=0.81), and anteroretrocollis (r=0.78). For lateral shift, the tool achieved a moderate correlation (r=0.55) with clinical ratings and demonstrated higher accuracy than human raters in controlled benchmark tests on avatars. By leveraging synthetic training data to bridge the clinical data gap, this model successfully generalizes to real-world patients, providing a validated, objective tool for CD postural assessment that can enable standardized clinical decision-making and trial evaluation.

Paper Structure

This paper contains 19 sections, 5 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Methods & Materials
  3. Automatic TWSTRS estimation
  4. Head pose estimation for rotational TWSTRS items
  5. Avatar based pre-training for lateral shift
  6. Datasets
  7. Synthetic dataset for algorithmic training
  8. Patient image dataset for algorithm evaluation
  9. Evaluation
  10. Results
  11. Confidence Interval Comparison of Human Estimates on Avatars vs. Real Images
  12. Comparison on avatars: human rating vs. algorithm
  13. Human ratings vs. algorithm on real images
  14. Discussion
  15. Acknowledgment
  16. ...and 4 more sections

Figures (5)

  • Figure 3: Computational pipeline for TWSTRS item estimation. TWSTRS=Toronto Western Spasmodic Torticollis Rating Scale.
  • Figure 4: Synthetic avatar dataset and clinical examples of cervical dystonia. (A) Examples of synthetic avatar images used for model training. The dataset includes frontal renderings with varying camera perspectives, lighting, backgrounds, clothing, and morphologies across a range of symptom severities. (B) Front and side views of two individuals with cervical dystonia. Left: Characterized by mild torticollis (0.5), laterocollis (0.5), and a prominent lateral shift (0.7). Right: A complex syndrome featuring torticollis (2.5), laterocollis (0.9), and minimal retrocollis (0.5). Reported values represent the average of 10 independent clinical ratings using static items of the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS).
  • Figure 5: Performance and clinical correlation of the TWSTRS algorithm. (A) Comparison of algorithm performance (true positive rate and accuracy) against human raters on the avatar benchmark with known ground-truth head deviations. (B) Correlation between TWSTRS estimations and mean clinical ratings on real-world clinical images across five items: torticollis, laterocollis, anterocollis/retrocollis, and lateral shift. Each dot represents a single patient image. TWSTRS = Toronto Western Spasmodic Torticollis Rating Scale.
  • Figure 6: Predictions of our proposed algorithm on each frame of a video, recorded by the Move2Screen app as an illustrative example use case of the algorithm. The tasks (green areas) are chosen to make symptoms accessible, like the maximal possible rotation, or the task to let the head move into the most comfortable position.
  • Figure 7: The annotation platform; A synthesized avatar demonstrating compound head and neck movements along the three principal axes of rotation: yaw (horizontal turning), pitch (vertical nodding), and roll (side-to-side tilting). (B) The Col-Cap evaluation interface, which uses interactive sliders to separately rate upper and lower cervical motion in each rotational plane. (C) Clinical scoring using the TWSTRS motor subscale, with categorical ratings for cervical postures including torticollis, laterocollis, anterocollis, retrocollis, shoulder elevation, and sagittal displacement. In this study, only TWSTRS estimations were used.