Remote Monitoring of Treatment Response in Parkinson's Disease: The Habit of Typing on a Computer.

OBJECTIVE: The recent advances in technology are opening a new opportunity to remotely evaluate motor features in people with Parkinson's disease (PD). We hypothesized that typing on an electronic device, a habitual behavior facilitated by the nigrostriatal dopaminergic pathway, could allow for objectively and nonobtrusively monitoring parkinsonian features and response to medication in an at-home setting. METHODS: We enrolled 31 participants recently diagnosed with PD who were due to start dopaminergic treatment and 30 age-matched controls. We remotely monitored their typing pattern during a 6-month (24 weeks) follow-up period before and while dopaminergic medications were being titrated. The typing data were used to develop a novel algorithm based on recursive neural networks and detect participants' responses to medication. The latter were defined by the Unified Parkinson's Disease Rating Scale-III (UPDRS-III) minimal clinically important difference. Furthermore, we tested the accuracy of the algorithm to predict the final response to medication as early as 21 weeks prior to the final 6-month clinical outcome. RESULTS: The score on the novel algorithm based on recursive neural networks had an overall moderate kappa agreement and fair area under the receiver operating characteristic (ROC) curve with the time-coincident UPDRS-III minimal clinically important difference. The participants classified as responders at the final visit (based on the UPDRS-III minimal clinically important difference) had higher scores on the novel algorithm based on recursive neural networks when compared with the participants with stable UPDRS-III, from the third week of the study onward. CONCLUSIONS: This preliminary study suggests that remotely gathered unsupervised typing data allows for the accurate detection and prediction of drug response in PD. © 2019 International Parkinson and Movement Disorder Society.

Detection of Motor Impairment in Parkinson's Disease Via Mobile Touchscreen Typing.

Mobile technology is opening a wide range of opportunities for transforming the standard of care for chronic disorders. Using smartphones as tools for longitudinally tracking symptoms could enable personalization of drug regimens and improve patient monitoring. Parkinson's disease (PD) is an ideal candidate for these tools. At present, evaluation of PD signs requires trained experts to quantify motor impairment in the clinic, limiting the frequency and quality of the information available for understanding the status and progression of the disease. Mobile technology can help clinical decision making by completing the information of motor status between hospital visits. This paper presents an algorithm to detect PD by analyzing the typing activity on smartphones independently of the content of the typed text. We propose a set of touchscreen typing features based on a covariance, skewness, and kurtosis analysis of the timing information of the data to capture PD motor signs. We tested these features, both independently and in a multivariate framework, in a population of 21 PD and 23 control subjects, achieving a sensitivity/specificity of 0.81/0.81 for the best performing feature and 0.73/0.84 for the best multivariate method. The results of the alternating finger-tapping, an established motor test, measured in our cohort are 0.75/0.78. This paper contributes to the development of a home-based, high-compliance, and high-frequency PD motor test by analysis of routine typing on touchscreens.

What's in a Name? Accurately Diagnosing Metopic Craniosynostosis Using a Computational Approach.

BACKGROUND: The metopic suture is unlike other cranial sutures in that it normally closes in infancy. Consequently, the diagnosis of metopic synostosis depends primarily on a subjective assessment of cranial shape. The purpose of this study was to create a simple, reproducible radiographic method to quantify forehead shape and distinguish trigonocephaly from normal cranial shape variation. METHODS: Computed tomography scans were acquired for 92 control patients (mean age, 4.2 ± 3.3 months) and 18 patients (mean age, 6.2 ± 3.3 months) with a diagnosis of metopic synostosis. A statistical model of the normal cranial shape was constructed, and deformation fields were calculated for patients with metopic synostosis. Optimal and divergence (simplified) interfrontal angles (IFA) were defined based on the three points of maximum average deformation on the frontal bones and metopic suture, respectively. Statistical analysis was performed to assess the accuracy and reliability of the diagnostic procedure. RESULTS: The optimal interfrontal angle was found to be significantly different between the synostosis (116.5 ± 5.8 degrees; minimum, 106.8 degrees; maximum, 126.6 degrees) and control (136.7 ± 6.2 degrees; minimum, 123.8 degrees; maximum, 169.3 degrees) groups (p < 0.001). Divergence interfrontal angles were also significantly different between groups. Accuracy, in terms of available clinical diagnosis, for the optimal and divergent angles, was 0.981 and 0.954, respectively. CONCLUSIONS: Cranial shape analysis provides an objective and extremely accurate measure by which to diagnose abnormal interfrontal narrowing, the hallmark of metopic synostosis. The simple planar angle measurement proposed is reproducible and accurate, and can eliminate diagnostic subjectivity in this disorder. CLINICAL QUESTION/LEVEL OF EVIDENCE: Diagnostic, IV.

Personalized assessment of craniosynostosis via statistical shape modeling.

We present a technique for the computational analysis of craniosynostosis from CT images. Our fully automatic methodology uses a statistical shape model to produce diagnostic features tailored to the anatomy of the subject. We propose a computational anatomy approach for measuring shape abnormality in terms of the closest case from a multi-atlas of normal cases. Although other authors have tackled malformation characterization for craniosynostosis in the past, our approach involves several novel contributions (automatic labeling of cranial regions via graph cuts, identification of the closest morphology to a subject using a multi-atlas of normal anatomy, detection of suture fusion, registration using masked regions and diagnosis via classification using quantitative measures of local shape and malformation). Using our automatic technique we obtained for each subject an index of cranial suture fusion, and deformation and curvature discrepancy averages across five cranial bones and six suture regions. Significant differences between normal and craniosynostotic cases were obtained using these characteristics. Machine learning achieved a 92.7% sensitivity and 98.9% specificity for diagnosing craniosynostosis automatically, values comparable to those achieved by trained radiologists. The probability of correctly classifying a new subject is 95.7%.

Linearized multidimensional earth-mover's-distance gradient flows.

This paper presents the first framework capable of performing active contour segmentation using Earth Mover's Distance (EMD) to measure dissimilarity between multidimensional feature distributions. EMD is the best known and understood cross-bin histogram distance measure, and as such it allows for meaningful comparisons between distributions, unlike bin-to-bin measures that only account for discrepancies on a bin-to-bin basis. Because EMD is obtained with linear programming techniques, its differential structure with respect to variations in bin weights as the active contour evolves is expressed through sensitivity analysis. Euler-Lagrange equations are then derived from the computed sensitivity at every iteration to produce gradient descent flows. We validate our approach with color image segmentation, in comparison with state-of-the-art Bhattacharyya (bin-to-bin) and 1D EMD (cross-bin) active contours. Some unique advantages of cross-bin comparison are highlighted in our segmentation results: better perceptual value and increased robustness with respect to the initialization.

Distinct quantitative computed tomography emphysema patterns are associated with physiology and function in smokers.

RATIONALE: Emphysema occurs in distinct pathologic patterns, but little is known about the epidemiologic associations of these patterns. Standard quantitative measures of emphysema from computed tomography (CT) do not distinguish between distinct patterns of parenchymal destruction. OBJECTIVES: To study the epidemiologic associations of distinct emphysema patterns with measures of lung-related physiology, function, and health care use in smokers. METHODS: Using a local histogram-based assessment of lung density, we quantified distinct patterns of low attenuation in 9,313 smokers in the COPDGene Study. To determine if such patterns provide novel insights into chronic obstructive pulmonary disease epidemiology, we tested for their association with measures of physiology, function, and health care use. MEASUREMENTS AND MAIN RESULTS: Compared with percentage of low-attenuation area less than -950 Hounsfield units (%LAA-950), local histogram-based measures of distinct CT low-attenuation patterns are more predictive of measures of lung function, dyspnea, quality of life, and health care use. These patterns are strongly associated with a wide array of measures of respiratory physiology and function, and most of these associations remain highly significant (P < 0.005) after adjusting for %LAA-950. In smokers without evidence of chronic obstructive pulmonary disease, the mild centrilobular disease pattern is associated with lower FEV1 and worse functional status (P < 0.005). CONCLUSIONS: Measures of distinct CT emphysema patterns provide novel information about the relationship between emphysema and key measures of physiology, physical function, and health care use. Measures of mild emphysema in smokers with preserved lung function can be extracted from CT scans and are significantly associated with functional measures.

Extraction of airways from CT (EXACT'09).

This paper describes a framework for establishing a reference airway tree segmentation, which was used to quantitatively evaluate fifteen different airway tree extraction algorithms in a standardized manner. Because of the sheer difficulty involved in manually constructing a complete reference standard from scratch, we propose to construct the reference using results from all algorithms that are to be evaluated. We start by subdividing each segmented airway tree into its individual branch segments. Each branch segment is then visually scored by trained observers to determine whether or not it is a correctly segmented part of the airway tree. Finally, the reference airway trees are constructed by taking the union of all correctly extracted branch segments. Fifteen airway tree extraction algorithms from different research groups are evaluated on a diverse set of twenty chest computed tomography (CT) scans of subjects ranging from healthy volunteers to patients with severe pathologies, scanned at different sites, with different CT scanner brands, models, and scanning protocols. Three performance measures covering different aspects of segmentation quality were computed for all participating algorithms. Results from the evaluation showed that no single algorithm could extract more than an average of 74% of the total length of all branches in the reference standard, indicating substantial differences between the algorithms. A fusion scheme that obtained superior results is presented, demonstrating that there is complementary information provided by the different algorithms and there is still room for further improvements in airway segmentation algorithms.