Testing a phantom eye under various signal-to-noise ratio conditions using eleven different OCT devices

We compared eleven OCT devices in their ability to quantify retinal layer thicknesses under different signal-strength conditions, using a commercially available phantom eye. We analyzed a medium-intensity 50 µm layer in an identical manner for all devices, using the provided log-scale images and a reconstructed linear-scale tissue reflectivity metric. Thickness measurements were highly comparable when the data were analyzed in an identical manner. With optimal signal strength, the thickness of the 50 µm layer was overestimated by a mean of 4.3 µm in the log-scale images and of 2.7 µm in the linear-scale images

Matching value propositions with varied customer needs:the role of service modularity

Abstract Organizations seek to manage varied customer segments using varied value propositions. The ability of a knowledge‐intensive business service (KIBS) provider to formulate value propositions into attractive offerings to varied customers becomes a competitive advantage. In this specific business based on often highly abstract service offerings, this requires the provider to have a clear overview of its knowledge and resources and how these can be configured to obtain the desired customization of services. Hence, the purpose of this paper is to investigate how a KIBS provider can match value propositions with varied customer needs utilizing service modularity. To accomplish this purpose, a qualitative multiple case study is organized around 5 projects allowing within‐case and cross‐case comparisons. Our findings describe how through the configuration of knowledge and resources a sustainable competitive advantage is created through creating the right kind of value propositions for varied customers with the help of modularity. Understanding gained through this research helps KIBS organizations in their efforts to increase organizational effectiveness through modular services

Identifying de novo Parkinson's disease with optical coherence tomography of the retina: A machine learning classification approach

Objective To identify and classify de novo Parkinson’s disease patients compared to healthy controls (HC). Background PD patients experience visual symptoms and retinal degeneration. Studies using spectral-domain optical coherence tomography (SD-OCT) have shown retinal thinning in PD, even at the beginning of disease. This study investigated the utility of these retinal changes in de novo Ldopa-naive PD patients, to evaluate if the profile of retinal thinning could serve as a classification biomarker. Methods SD-OCT data were collected in de novo Ldopa-naive PD patients at the University Medical Center Groningen. 5x5 mm macular scans of right and left eyes were made. These were compared to age-matched HC scans. Good quality scans (≥4) were segmented by Iowa Reference Algorithms [1]; each retina was segmented into 10 individual cell layers. Results 121 PD, 110 HC were included. A random forest classification of all cell layers, across both eyes was run. Data was split into 102 training, 26 validation and 31 testing. Total test accuracy was 0.74, Out of the box accuracy 0.64. True positive rate: area under the curve receiver operating characteristic (AUROC) of 0.82, to classify PD compared to HC. Conclusions Retinal cell layer changes could play an important role in a model of classifying PD; presenting with significant differences in medication naive, newly diagnosed patients, being able to provide a high true positive classification, with automatically segmented retinal cell layer data, from straightforward SD-OCT retinal imaging

Identifying de novo Parkinson's disease with optical coherence tomography of the retina:A machine learning classification approach

Objective To identify and classify de novo Parkinson’s disease patients compared to healthy controls (HC). Background PD patients experience visual symptoms and retinal degeneration. Studies using spectral-domain optical coherence tomography (SD-OCT) have shown retinal thinning in PD, even at the beginning of disease. This study investigated the utility of these retinal changes in de novo Ldopa-naive PD patients, to evaluate if the profile of retinal thinning could serve as a classification biomarker. Methods SD-OCT data were collected in de novo Ldopa-naive PD patients at the University Medical Center Groningen. 5x5 mm macular scans of right and left eyes were made. These were compared to age-matched HC scans. Good quality scans (≥4) were segmented by Iowa Reference Algorithms [1]; each retina was segmented into 10 individual cell layers. Results 121 PD, 110 HC were included. A random forest classification of all cell layers, across both eyes was run. Data was split into 102 training, 26 validation and 31 testing. Total test accuracy was 0.74, Out of the box accuracy 0.64. True positive rate: area under the curve receiver operating characteristic (AUROC) of 0.82, to classify PD compared to HC. Conclusions Retinal cell layer changes could play an important role in a model of classifying PD; presenting with significant differences in medication naive, newly diagnosed patients, being able to provide a high true positive classification, with automatically segmented retinal cell layer data, from straightforward SD-OCT retinal imaging