Automated structuring of gait data for analysis purposes - A deep learning pilot example.

Clinical gait analysis can help diagnose ambulatory children with cerebral palsy and provide treatment recommendations. This group represents the largest group of children with gait problems. Currently, the workflow for 3D gait analysis involves a complex process of collecting motion capture data and other types of data, analyzing the collected data, and creating an expert knowledge-based assessment. With this in mind, a data pipeline is essential for efficiently and effectively structuring data and reducing the time and effort required for data annotation and organization.A novel data pipeline has been developed to help structure, anonymize and automate parts of the annotation process of the data. In this sense, a pilot experiment was conducted using a simple convolutional neural network to classify between hemi-plegic and diplegic gait. This experiment included preprocessing the data, training the model and testing it.The data pipeline was used to create a semi-automated annotated data set. The neural network was trained on the data set and achieved an accuracy of 0.78 and a median of 1.0 on a holdout test set.

Evaluation and Comparison of Target Registration Error in Active and Passive Optical Tracking Systems.

Optical tracking systems combined with imaging modalities such as computed tomography and magnetic reso-nance imaging are important parts of image guided surgery systems. By determining the location and orientation of sur-gical tools relative to a patient's reference system, tracking systems assist surgeons during the planning and execution of image guided procedures. Therefore, knowledge of the tracking system-induced error is of great importance. To this end, this study compared one passive and two active optical tracking systems in terms of their Target Registration Error. Two experiments were performed to measure the systems' accuracy, testing the impact of factors such as the size of the measuring volume, length of surgical instruments and environmental conditions with orthopedic procedures in mind. According to the performed experiments, the active systems achieved significantly higher accuracy than the tested passive system, reporting an overall accuracy of 0.063 mm (SD = 0.025) and 0.259 mm (SD = 0.152), respectively.

Mixed reality as a novel tool for diagnostic and surgical navigation in orthopaedics.

PURPOSE: This study presents a novel surgical navigation tool developed in mixed reality environment for orthopaedic surgery. Joint and skeletal deformities affect all age groups and greatly reduce the range of motion of the joints. These deformities are notoriously difficult to diagnose and to correct through surgery. METHOD: We have developed a surgical tool which integrates surgical instrument tracking and augmented reality through a head mounted display. This allows the surgeon to visualise bones with the illusion of possessing "X-ray" vision. The studies presented below aim to assess the accuracy of the surgical navigation tool in tracking a location at the tip of the surgical instrument in holographic space. RESULTS: Results show that the average accuracy provided by the navigation tool is around 8 mm, and qualitative assessment by the orthopaedic surgeons provided positive feedback in terms of the capabilities for diagnostic use. CONCLUSIONS: More improvements are necessary for the navigation tool to be accurate enough for surgical applications, however, this new tool has the potential to improve diagnostic accuracy and allow for safer and more precise surgeries, as well as provide for better learning conditions for orthopaedic surgeons in training.

Use of mixed reality for improved spatial understanding of liver anatomy.

Introduction: In liver surgery, medical images from pre-operative computed tomography and magnetic resonance imaging are the basis for the decision-making process. These images are used in surgery planning and guidance, especially for parenchyma-sparing hepatectomies. Though medical images are commonly visualized in two dimensions (2D), surgeons need to mentally reconstruct this information in three dimensions (3D) for a spatial understanding of the anatomy. The aim of this work is to investigate whether the use of a 3D model visualized in mixed reality with Microsoft HoloLens increases the spatial understanding of the liver, compared to the conventional way of using 2D images.Material and methods: In this study, clinicians had to identify liver segments associated to lesions.Results: Twenty-eight clinicians with varying medical experience were recruited for the study. From a total of 150 lesions, 89 were correctly assigned without significant difference between the modalities. The median time for correct identification was 23.5 [4-138] s using the magnetic resonance imaging images and 6.00 [1-35] s using HoloLens (p < 0.001).Conclusions: The use of 3D liver models in mixed reality significantly decreases the time for tasks requiring a spatial understanding of the organ. This may significantly decrease operating time and improve use of resources.

Semi-automated vs. manual 3D reconstruction of central mesenteric vascular models: the surgeon's verdict.

BACKGROUND: 3D vascular anatomy roadmaps are currently being implemented for surgical planning and navigation. Quality of the reconstruction is critical. The aim of this article is to compare anatomical completeness of models produced by manual and semi-automatic segmentation. METHODS: CT-datasets from patients included in an ongoing trial, underwent 3D vascular reconstruction applying two different segmentation methods. This produced manually-segmented models (MSMs) and semi-automatically segmented models (SAMs) which underwent a paired comparison. Datasets were delivered for reconstruction in 4 batches of 6, of which only batch 4 contained patients with abnormal anatomy. Model completeness was assessed quantitatively using alignment and distance error indexes and qualitatively with systematic inspection. MSMs were the gold standard. Assessed vessels were those of interest to the surgeon performing D3-right colectomy. RESULTS: 24 CT-datasets (13 females, age 44-77) were used in a paired comparative analysis of 48 3D-models. Quantitatively, SAMs showed structural improvement from Batch 1 to 3. Batch 4, with abnormal vessels, showed the highest error-index values. Qualitatively, 91.7% of SAMs did not contain all mesenteric branches relevant to the surgeon. In SAMs, 1 (12.5%) right colic artery-RCA scored as a complete vessel. 3 (37.5%) RCAs scored as incomplete and 4 (50%) RCAs were absent. 6 (25%) of 24 middle colic arteries-MCA scored as complete vessels. 11 (45.8%) scored as incomplete while 7 (29.2%) MCAs were absent. 13 (54.2%) of 24 ileocolic arteries-ICA were complete vessels. 11 (45.8%) scored as incomplete. None (0%) were absent. Additionally, it was observed that 10 (41.7%) of SAMs contained all their jejunal arteries, when compared to MSMs. Calibers of "complete" vessels were significantly higher than in "missing" vessels (MCA p < 0.001, RCA p = 0.016, ICA p < 0.001, JAs p < 0.001). CONCLUSION: Despite acceptable results from quantitative analysis, qualitative comparison indicates that semi-automatically generated 3D-models of the central mesenteric vasculature could cause considerable confusion at surgery.