Automated measurement of inter-arytenoid distance on 4D laryngeal CT: A validation study.

Changes to the voice are prevalent and occur early in Parkinson's disease. Correlates of these voice changes on four-dimensional laryngeal computed-tomography imaging, such as the inter-arytenoid distance, are promising biomarkers of the disease's presence and severity. However, manual measurement of the inter-arytenoid distance is a laborious process, limiting its feasibility in large-scale research and clinical settings. Automated methods of measurement provide a solution. Here, we present a machine-learning module which determines the inter-arytenoid distance in an automated manner. We obtained automated inter-arytenoid distance readings on imaging from participants with Parkinson's disease as well as healthy controls, and then validated these against manually derived estimates. On a modified Bland-Altman analysis, we found a mean bias of 1.52 mm (95% limits of agreement -1.7 to 4.7 mm) between the automated and manual techniques, which improves to a mean bias of 0.52 mm (95% limits of agreement -1.9 to 2.9 mm) when variability due to differences in slice selection between the automated and manual methods are removed. Our results demonstrate that estimates of the inter-arytenoid distance with our automated machine-learning module are accurate, and represents a promising tool to be utilized in future work studying the laryngeal changes in Parkinson's disease.

A pilot study of high frequency accelerometry-based sedation and agitation monitoring in critically ill patients.

OBJECTIVE: The degree of sedation or agitation in critically ill patients is typically assessed with the Richmond Agitation and Sedation Scale (RASS). However, this approach is intermittent and subject to unrecognised variation between assessments. High frequency accelerometry may assist in achieving a quantitative and continuous assessment of sedation while heralding imminent agitation. DESIGN: We undertook a prospective, observational pilot study. SETTING: An adult tertiary intensive care unit in Melbourne, Australia. PARTICIPANTS: 20 patients with an admission diagnosis of trauma. MAIN OUTCOME MEASURES: Accelerometers were applied to patients' wrists and used to continuously record patient movement. Video data of patient behaviour were simultaneously collected, and observers blinded to accelerometry data were adjudicated the RASS score every 30 seconds. Exploratory analyses were undertaken. RESULTS: Patients were enrolled for a median duration of 9.7 hours (interquartile range [IQR], 0-22.8) and a total of 160 hours. These patients had a median RASS score of 0 (IQR, -4 to 0). A 2-minute moving window of amplitude variance was seen to reflect contemporaneous fluctuations in motor activity and was proportional to the RASS score. Furthermore, the moving window of amplitude variance was observed to spike immediately before ≥ 2 point increases in the RASS score. CONCLUSIONS: We describe a novel approach to the analysis of wrist accelerometry data in critically ill patients. This technique not only appears to provide novel and continuous information about the depth of sedation or degree of agitation, it is also notable in its aptitude to anticipate impending transitions to higher RASS values.

Arytenoid cartilage feature point detection using laryngeal 3D CT images in Parkinson's disease.

Parkinson's disease is a neurodegenerative disorder that results in progressive degeneration of nerve cells. It is generally associated with the deficiency of dopamine, a neurotransmitter involved in motor control of humans and thus affects the motor system. This results in abnormal vocal fold movements in majority of the Parkinson's patients. Analysis of vocal fold abnormalities may provide useful information to assess the progress of Parkinson's disease. This is accomplished by measuring the distance between the arytenoid cartilages during phonation. In order to automate this process of identifying arytenoid cartilages from CT images, in this work, a rule-based approach is proposed to detect the arytenoid cartilage feature points on either side of the airway. The proposed technique detects feature points by localizing the anterior commissure and analyzing airway boundary pixels to select the optimal feature point based on detected pixels. The proposed approach achieved 83.33% accuracy in estimating clinically-relevant feature points, making the approach suitable for automated feature point detection. To the best of our knowledge, this is the first such approach to detect arytenoid cartilage feature points using laryngeal 3D CT images.