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Photothermal Lasers
Published in Anita Prasad, Laser Techniques in Ophthalmology, 2022
NAVILAS uses computer-guided eye tracking (retinal navigation), integrated with fundus camera-based delivery (not slit lamp-contact lens delivery). The 532 nm (green) laser integrates fundus imaging (FFA or red-free images can be overlaid on a fundal photo to precisely map and target mA, and plan treatment areas). Eye tracking enhances safety, precision, and accuracy of laser delivery, which is monitored on a screen. The field of view is 50° for FLT, and 80° for PRP, and both treatments can be performed using retinal eye tracking, with 96% accuracy in FLT, using grid patterns (TREX-DME trial). A newer model uses 577nm wavelength (yellow) laser and combines navigation with subthreshold (MPL) laser delivery.
Blinking and Looking: An Eye-Tracking Approach to Studying Cognitive Processing Differences in Individuals with Speech, Language, and Communication Disorders
Published in Stavros Hatzopoulos, Andrea Ciorba, Mark Krumm, Advances in Audiology and Hearing Science, 2020
Jennifer M. Roche, Schea N. Fissel
In infant populations (birth through one year), control of the oculomotor system (much like other motor systems such as reaching) develops exponentially through 4–6 months of age. Infants have relatively less volitional control over their bodies, allowing for data collection to occur in a stabilized environment (e.g., in a car seat or carrier). However, they are still developing and learning about their environment and may lack richer representations that older children learn and develop over time. Traditionally, overt, endogenous infant eye movement indices (fixations, scanning patterns, saccade latencies) are the focus of measurement; however, exogenous indices (e.g., blinking, pupilometry) may provide complementary information on how infants are visually processing their world. In general, by four months of age, typically developing infants are able to flexibly disengage and shift visual attention, as well as predict stimulus location based on visual cues or statistical regularities (Johnson et al., 2003). Conversely, infants with developmental disorders show more variable patterns of oculomotor control, with particular difficulty disengaging attention and predicting or anticipating visual events (e.g., Landry and Bryson, 2004). A critical focus of eye-tracking research in infancy is the development of paradigms used for early identification and differential diagnosis of developmental disorders. While these paradigms are not yet diagnostically valid, many published studies warrant further exploration of eye tracking as a diagnostic tool.
Technology Acceptance, Adoption, and Usability: Arriving at Consistent Terminologies and Measurement Approaches
Published in Christopher M. Hayre, Dave J. Muller, Marcia J. Scherer, Everyday Technologies in Healthcare, 2019
Lili Liu, Antonio Miguel Cruz, Adriana Maria Rios Rincon
Eye-Tracking. This is a direct method of testing usability. In this method, an eye tracking device and a software are configured to measure where users’ eyes are focused while performing tasks or interact naturally with the technology being tested. This method is particularly suited for measuring usability of websites, software applications, physical products or environments (e.g., smart homes) (Rohrer, 2014). The software generates data about users’ actions in the form of heat maps (i.e. colour scale moving from blue to red) or saccade pathways (i.e. a combination of circles and lines, a red circle is the area of focus, the red line indicates the flight) (Usability, 2018).
How Children with Congenital Limb Deficiencies Visually Attend to Their Limbs and Prostheses: Eye Tracking of Displayed Still Images and Visuospatial Body Knowledge
Published in Developmental Neurorehabilitation, 2021
Hiroshi Mano, Sayaka Fujiwara, Nobuhiko Haga
Eye tracking is a technique used to measure gaze direction.10 Some examples related to medical treatment include studies by Trojano et al.,11 who used eye tracking for the cognitive rehabilitation of a brain-injury patient with locked-in syndrome, and by Spataro et al.,12 who used an eye tracking computer device for communication in amyotrophic lateral sclerosis patients. Related to body recognition, facial recognition has been researched in individuals with child and adult autism spectrum disorder, and it has been reported that they looked at eyes, noses, and mouths less than the controls.13–16 To the best of our knowledge, no research has been conducted using eye tracking in children with limb deficiencies or deficiencies in other body parts. As visuospatial body knowledge is derived from visual information, measuring visual attention with eye tracking allows us to evaluate the relationship between visuospatial body knowledge and visual attention.
Machine Learning Applications in Pediatric Ophthalmology
Published in Seminars in Ophthalmology, 2021
Several other applications of ML in conditions indirectly managed by pediatric ophthalmologists were identified in our review. Dyslexia is a condition estimated to affect between 5 and 10% of the population.85 Eye tracking combined with ML can be used to develop fast, objective and accurate screening models useful for identifying school children at risk of dyslexia.85 ML applied to eye tracking has also been studied extensively as a potential tool to assist in the diagnosis of autism spectrum disorders.86–90 Eye tracking has been used to characterize other neurologic conditions, such as a way to perform high throughput classification of patients with attention deficit hyperactivity disorder and fetal alcohol spectrum disorder. ML has been used to explore visual development in facial recognition. Vogelsang et al. showed that CNN-based image recognition algorithms trained initially with blurred images with gradually improving clarity demonstrated increased performance compared to models generated without gradual improvement.91 This insight, though requiring further investigation, may have applications in guiding optimal refractive correction in patients with a history of childhood cataract surgery who have been shown to exhibit diminished facial processing abilities.92,93 ML algorithms have also been applied to assisted device fitting in low vision patients to predict which devices patients would benefit most from based on parameters defined from functional needs.94
Comparison between joystick- and gaze-controlled electric wheelchair during narrow doorway crossing: Feasibility study and movement analysis
Published in Assistive Technology, 2021
Manel Letaief, Nasser Rezzoug, Philippe Gorce
Among the proposed alternatives, it is believed that an electric wheelchair controlled through eye movements has the potential to provide such persons with effective ways to alleviate the impact of their motor limitations. To date, eye tracking has been used mainly to study subjects’ behavior in various fields such as cognitive and behavioral therapy (Grillon, Riquier, Herbelin, & Thalmann, 2006), marketing/advertising (Rayner, Miller, & Rotello, 2008), neurosciences (Snodderly, Kagan, & Gur, 2001), psychology (Rayner, 1998) but also for Human–Computer Interaction (HCI) (Goldberg, Stimson, Lewenstein, Scott, & Wichansky, 2002) and for eye typing (Majaranta & Räihä, 2002). Also, as suggested by recent studies, the application of eye tracking to steer and maneuver an electric wheelchair is very promising (Eid, Giakoumidis, & Saddik, 2016; Ktena, Abbott, & Faisal, 2015; Maule et al., 2016).