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.

Ledipasvir and Sofosbuvir in the Treatment of Early Hepatitis C Virus Infection in HIV-Infected Men.

BACKGROUND: Treatment of HIV-infected men during early hepatitis C virus (HCV) infection with interferon results in a higher cure rate with a shorter duration of treatment than during chronic HCV infection. We recently demonstrated that this phenomenon applied to interferon-free treatment as well, curing most participants with short-course sofosbuvir and ribavirin. Due to the significantly higher potency of the ledipasvir/sofosbuvir (LDV/SOF) combination, we hypothesized that we would be more successful in curing early HCV infections using a shorter course of LDV/SOF than that used for treating chronic HCV infections. METHODS: We performed a prospective, open-label, consecutive case series study of 8 weeks of LDV/SOF in HIV-infected men with early genotype 1 HCV infection. The primary end point was aviremia at least 12 weeks after completion of treatment. RESULTS: We treated 25 HIV-infected men with early sexually acquired HCV infection with 8 weeks of LDV/SOF, and all 25 (100%) were cured. Twelve (48%) reported sexualized drug use with methamphetamine. CONCLUSIONS: Eight weeks of LDV/SOF cured all 25 HIV-infected men with early HCV infection, including those who were actively using drugs. Based on these results, we recommend treatment of newly HCV-infected men during early infection, regardless of drug use, to both take advantage of this 8-week treatment and to decrease further HCV transmission among this group of men.

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.