Understanding the propensity to undergo genetic testing in patients affected by inherited retinal diseases: a twelve-item questionnaire.

BACKGROUND: The diagnosis of inherited retinal diseases (IRDs) can only be confirmed through genetic testing. The aim of our study is to investigate the propensity of Italian patients affected by IRDs to undergo genetic testing. MATERIALS AND METHODS: One hundred and thirty-two patients diagnosed with IRDs referred to Italian Retina Onlus were enrolled from 1st January 2021 to 31th December 2021 in this cross-sectional study to answer to a twelve-item questionnaire. RESULTS: One hundred and four patients were aware of the possibility of taking a genetic test, and 94 of them did. Most of genetically tested patients (93.6%) had been informed about advantages and limitations of genetic investigations. The most common reason for undergoing genetic testing was to gather information for their relatives, while the most frequent reason for patients not taking the test was lack of someone who encourages them to do so. Most of genetically tested patients believed that the results could aid medical research in the search for a treatment for IRDs, while who did not undergo DNA testing often did not have a clear opinion on the topic. Almost all patients (98.9%) performed the test through the Italian National Health System. CONCLUSIONS: Our study investigated the tendency of Italian patients affected by IRDs to undergo genetic testing, highlighting the importance of educating both patients and healthcare professionals on this topic.

JustPoint: Identifying Colors with a Natural User Interface.

People with severe visual impairments usually have no way of identifying the colors of objects in their environment. While existing smartphone apps can recognize colors and speak them aloud, they require the user to center the object of interest in the camera's field of view, which is challenging for many users. We developed a smartphone app to address this problem that reads aloud the color of the object pointed to by the user's fingertip, without confusion from background colors. We evaluated the app with nine people who are blind, demonstrating the app's effectiveness and suggesting directions for improvements in the future.

Mind your crossings: Mining GIS imagery for crosswalk localization.

For blind travelers, finding crosswalks and remaining within their borders while traversing them is a crucial part of any trip involving street crossings. While standard Orientation & Mobility (O&M) techniques allow blind travelers to safely negotiate street crossings, additional information about crosswalks and other important features at intersections would be helpful in many situations, resulting in greater safety and/or comfort during independent travel. For instance, in planning a trip a blind pedestrian may wish to be informed of the presence of all marked crossings near a desired route. We have conducted a survey of several O&M experts from the United States and Italy to determine the role that crosswalks play in travel by blind pedestrians. The results show stark differences between survey respondents from the U.S. compared with Italy: the former group emphasized the importance of following standard O&M techniques at all legal crossings (marked or unmarked), while the latter group strongly recommended crossing at marked crossings whenever possible. These contrasting opinions reflect differences in the traffic regulations of the two countries and highlight the diversity of needs that travelers in different regions may have. To address the challenges faced by blind pedestrians in negotiating street crossings, we devised a computer vision-based technique that mines existing spatial image databases for discovery of zebra crosswalks in urban settings. Our algorithm first searches for zebra crosswalks in satellite images; all candidates thus found are validated against spatially registered Google Street View images. This cascaded approach enables fast and reliable discovery and localization of zebra crosswalks in large image datasets. While fully automatic, our algorithm can be improved by a final crowdsourcing validation. To this end, we developed a Pedestrian Crossing Human Validation (PCHV) web service, which supports crowdsourcing to rule out false positives and identify false negatives.

Zebra Crossing Spotter: Automatic Population of Spatial Databases for Increased Safety of Blind Travelers.

In this paper we propose a computer vision-based technique that mines existing spatial image databases for discovery of zebra crosswalks in urban settings. Knowing the location of crosswalks is critical for a blind person planning a trip that includes street crossing. By augmenting existing spatial databases (such as Google Maps or OpenStreetMap) with this information, a blind traveler may make more informed routing decisions, resulting in greater safety during independent travel. Our algorithm first searches for zebra crosswalks in satellite images; all candidates thus found are validated against spatially registered Google Street View images. This cascaded approach enables fast and reliable discovery and localization of zebra crosswalks in large image datasets. While fully automatic, our algorithm could also be complemented by a final crowdsourcing validation stage for increased accuracy.