Performance evaluation of automated white matter hyperintensity segmentation algorithms in a multicenter cohort on cognitive impairment and dementia

Background: White matter hyperintensities (WMH), a biomarker of small vessel disease, are often found in Alzheimer’s disease (AD) and their advanced detection and quantification can be beneficial for research and clinical applications. To investigate WMH in large-scale multicenter studies on cognitive impairment and AD, appropriate automated WMH segmentation algorithms are required. This study aimed to compare the performance of segmentation tools and provide information on their application in multicenter research. Methods: We used a pseudo-randomly selected dataset (n = 50) from the DZNE-multicenter observational Longitudinal Cognitive Impairment and Dementia Study (DELCODE) that included 3D fluid-attenuated inversion recovery (FLAIR) images from participants across the cognitive continuum. Performances of top-rated algorithms for automated WMH segmentation [Brain Intensity Abnormality Classification Algorithm (BIANCA), lesion segmentation toolbox (LST), lesion growth algorithm (LGA), LST lesion prediction algorithm (LPA), pgs, and sysu_media] were compared to manual reference segmentation (RS). Results: Across tools, segmentation performance was moderate for global WMH volume and number of detected lesions. After retraining on a DELCODE subset, the deep learning algorithm sysu_media showed the highest performances with an average Dice’s coefficient of 0.702 (±0.109 SD) for volume and a mean F1-score of 0.642 (±0.109 SD) for the number of lesions. The intra-class correlation was excellent for all algorithms (>0.9) but BIANCA (0.835). Performance improved with high WMH burden and varied across brain regions. Conclusion: To conclude, the deep learning algorithm, when retrained, performed well in the multicenter context. Nevertheless, the performance was close to traditional methods. We provide methodological recommendations for future studies using automated WMH segmentation to quantify and assess WMH along the continuum of cognitive impairment and AD dementia

Hearing Impairment Affects Dementia Incidence. An Analysis Based on Longitudinal Health Claims Data in Germany

<div><p>Recent research has revealed an association between hearing impairment and dementia. The objective of this study is to determine the effect of hearing impairment on dementia incidence in a longitudinal study, and whether ear, nose, and throat (ENT) specialist care, care level, institutionalization, or depression mediates or moderates this pathway. The present study used a longitudinal sample of 154,783 persons aged 65 and older from claims data of the largest German health insurer; containing 14,602 incident dementia diagnoses between 2006 and 2010. Dementia and hearing impairment diagnoses were defined according to International Classification of Diseases, Tenth Revision, codes. We used a Kaplan Meier estimator and performed Cox proportional hazard models to explore the effect of hearing impairment on dementia incidence, controlling for ENT specialist care, care level, institutionalization, and depression. Gender, age, and comorbidities were controlled for as potential confounders. Patients with bilateral (HR = 1.43, p<0.001) and side-unspecified (HR = 1.20, p<0.001) hearing impairment had higher risks of dementia incidence than patients without hearing impairment. We found no significant effect for unilateral hearing impairment and other diseases of the ear. The effect of hearing impairment was only partly mediated through ENT specialist utilization. Significant interaction between hearing impairment and specialist care, care level, and institutionalization, respectively, indicated moderating effects. We discuss possible explanations for these effects. This study underlines the importance of the association between hearing impairment and dementia. Preserving hearing ability may maintain social participation and may reduce the burden associated with dementia. The particular impact of hearing aid use should be the subject of further investigations, as it offers potential intervention on the pathway to dementia.</p></div

Age-specific prevalence of hearing impairment in health claims data and three epidemiologic studies.

<p>Sources: AOK: health claims data AOK 2006–2010, own calculations; GER—Germany [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0156876#pone.0156876.ref050" target="_blank">50</a>]; IT—Italy [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0156876#pone.0156876.ref051" target="_blank">51</a>]; GB—Great Britain [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0156876#pone.0156876.ref052" target="_blank">52</a>]; dB HL = decibel hearing level.</p

Interaction of hearing impairment and treatment by an ENT doctor.

<p>Interaction of hearing impairment and treatment by an ENT doctor.</p

Interaction of hearing impairment and nursing home.

<p>Interaction of hearing impairment and nursing home.</p

Hazard ratios of incident dementia by hearing impairment and key variables.

<p>Hazard ratios of incident dementia by hearing impairment and key variables.</p

Kaplan-Meier estimator of time to dementia incidence by hearing impairment and depression.

<p>Data source: Claims data AOK 2006–2010; p_{log rank}<0.001.</p

Interaction of hearing impairment and depression.

<p>Interaction of hearing impairment and depression.</p

Kaplan-Meier estimator of time to dementia incidence by hearing impairment and treatment by an ENT doctor.

<p>Data source: Claims data AOK 2006–2010; p_{log rank}<0.001.</p

Kaplan-Meier estimator of time to dementia incidence by hearing impairment and living in a nursing home.

<p>Data source: Claims data AOK 2006–2010; p_{log rank}<0.001.</p