Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Structure and Function in Early Glaucoma

Glaucoma is a general term that includes an array of ocular conditions that cause a specific neuropathy of the optic nerve (Greenfield, Bagga, et al. 2003) of which abnormalities associated with this disorder are localized at the level of the retinal ganglion cell layer (Epstein 1997; Quigley & Broman 2006). This structure-function relationship is not clear as it relies on several factors such as variability from the structural and functional tests, differences in measurement scales between the two modalities (Greaney et al. 2002; Katz 1999; Drance 1985; Hood et al. 2007) and physiological variation amongst individuals (Pan & Swanson 2006). The global aim of this thesis was to relate visual function of the retinal ganglion cells to structure of the optic nerve head and retinal nerve fiber layer with respect to the following perimetry techniques: i) standard automated perimetry (SAP), ii) frequency doubling technology (FDT), iii) flicker defined form (FDF), and iv) the motion detection test (MDT), and the following imaging instruments: i) confocal scanning laser ophthalmoscopy (HRT), ii) optical coherence tomography (OCT), and iii) scanning laser polarimetry (GDx VCC). The specific purpose of this study was to i) compare the test-retest characteristics of the perimetry techniques, ii) determine which may be more sensitive for early detection, iii) evaluate the structure-function relationship between measures of retinal nerve fiber layer and visual function, and iv) perform a preliminary study to determine which techniques may be most suitable to monitor progression, in patients with early stage glaucoma. MDT showed little change in the 1-year follow-up study thus being unsuitable for monitoring change. FDT and FDF gave a similar performance and are likely optimal for the detection of early functional damage. Poor diagnostic agreement was seen between the HRT and each perimetry technique. Because no one perimetry test showed both high sensitivity and high specificity, it is recommended that a combination of FDF with either SAP, FDT or MDT be used as the functional component in the diagnosis and follow-up of patients with glaucoma. The strongest global structure-function correlations for OCT were seen with SAP, FDT and MDT; for GDx, the strongest association was seen with FDF. These results suggest that FDF and GDx used in combination are best to detect early glaucomatous changes

The Importance of Contrast Sensitivity, Color Vision, and Electrophysiological Testing In Clinical and Occupational Settings

Visual acuity (VA) is universally accepted as the gold standard metric for ocular vision and function. Contrast sensitivity (CS), color vision, and electrophysiological testing for clinical and occupational settings are warranted despite being deemed ancillary and minimally utilized by clinicians. These assessments provide essential information to subjectively and objectively quantify and obtain optimal functional vision. They are useful for baseline data and monitoring hereditary and progressive ocular conditions and cognitive function. The studies in this dissertation highlight the value of contrast sensitivity, color vision, and cone specific electrophysiological testing, as well as the novel metrics obtained with potential practical clinical applications for visual function and perception evaluation in patients in various settings. The first study aimed to design a clinically expedient method to combine color CS and color naming (CN) into a single, multi-metric test of color vision, the Color Contrast Naming Test (CCNT). This was accomplished by comparing and validating it with the standardized computerized Cone Contrast Test (CCT; Innova Systems, Inc.). Color vision deficient (CVD) and color vision normal (CVN) findings showed a strong correlation between the CCNT CS and the standard CCT. Furthermore, CCT CS showed distinct scores in 50% of CVDs, while the CCNT composite score (mean of CS and CN) showed distinct scores in 70% of CVDs, showing better potential discrimination of CVD color abilities. This novel metric has potential applications for identifying hereditary or progressive CVD severity and capabilities. The second study focused on electrophysiological diagnostics, specifically cone specific visual evoked potentials (VEPs), to objectively measure long-term neural adaptive responses to color-correcting lenses (CCLs). Dr. Werner and colleagues determined that extended wear (for 12 days) of color-correcting lenses improved red-green color perception in hereditary CVD even without wearing CCLs. Furthermore, Dr. Rabin and colleagues were able to objectively measure both immediate short-term (baseline, 4, 8, 12 days) and long-term (3, 6, 12 months) improvements of color perception status post-CCL removal with cone specific VEPs – something that has never been done before. The novel findings from both studies support the notion that neural adaptive changes can occur over short- and longer-term periods despite minimal daily wear time. More importantly, this further supports the value of suprathreshold cone VEPs to objectively assess color vision function in both clinical and occupational settings. Most dry eye studies use measures of tear quality and volume coupled with standard clinical tests such as high contrast visual acuity (VA), while fewer studies have investigated the effects of dry eyes on low contrast vision. The final study was designed to determine the impact of Meibomian Gland Dysfunction (MGD) dry eye on high and low-contrast vision, including both black/white (luminance) and cone specific color vision. A primary intent was to determine if these novel metrics improved following minimal meibomian gland (MG) expression. The computerized CCNT and CCT (cone and black/white) tests used in this study confirmed that minimal MG expression improved low contrast performance for long (L cone) and short (S cone) wavelength-sensitive cones. These improvements were most significant using throughput (CS/response time) and CCNT composite scores, both novel metrics for potential use in dry eye diagnosis, treatment, and management. Physical optics, including decreased destructive interference in the stroma, most detrimental with red light, and increased scattering by subtle epithelial, endothelial, and/or tear film defects, most detrimental for blue light, could each decrease retinal image contrast most evident with L and S cone CS. Contrast sensitivity, color vision, and cone specific electrophysiological testing are non-optimally and infrequently utilized in basic, clinical, applied, and translational research or occupational settings. These studies showed provocative results within their respective categories and confirmed their validity and importance for identifying and monitoring ocular conditions and neural adaptive or cognitive functions. Furthermore, novel metrics such as throughput and CCNT composite scores serve as potential tangible and practical visual function and perception assessment standards

Perspectives and Best Practices for Artificial Intelligence and Continuously Learning Systems in Healthcare

Goals of this paper Healthcare is often a late adopter when it comes to new techniques and technologies; this works to our advantage in the development of this paper as we relied on lessons learned from CLS in other industries to help guide the content of this paper. Appendix V includes a number of example use cases of AI in Healthcare and other industries. This paper focuses on identifying unique attributes, constraints and potential best practices towards what might represent “good” development for Continuously Learning Systems (CLS) AI systems with applications ranging from pharmaceutical applications for new drug development and research to AI enabled smart medical devices. It should be noted that although the emphasis of this paper is on CLS, some of these issues are common to all AI products in healthcare. Additionally, there are certain topics that should be considered when developing CLS for healthcare, but they are outside of the scope of this paper. These topics will be briefly touched upon, but will not be explored in depth. Some examples include: Human Factors – this is a concern in the development of any product – what are the unique usability challenges that arise when collecting data and presenting the results? Previous efforts at generating automated alerts have often created problems (e.g. alert fatigue.) CyberSecurity and Privacy – holding a massive amount of patient data is an attractive target for hackers, what steps should be taken to protect data from misuse? How does the European Union’s General Data Protection Regulation (GDPR) impact the use of patient data? Legal liability – if a CLS system recommends action that is then reviewed and approved by a doctor, where does the liability lie if the patient is negatively affected? Regulatory considerations – medical devices are subject to regulatory oversight around the world; in fact, if a product is considered a medical device depends on what country you are in. AI provides an interesting challenge to traditional regulatory models. Additionally, some organizations like the FTC regulate non-medical devices. This paper is not intended to be a standard, nor is this paper trying to advocate for one and only one method of developing, verifying, and validating CLS systems – this paper highlights best practices from other industries and suggests adaptation of those processes for healthcare. This paper is also not intended to evaluate existing or developing regulatory, legal, ethical, or social consequences of CLS systems. This is a rapidly evolving subject with many companies, and now some countries, establishing their own AI Principles or Code of Conduct which emphasize legal and ethical considerations including goals and principles of fairness, reliability and safety, transparency around how the results of these learning systems are explained to the people using those systems5 . The intended audience of this paper are Developers, Researchers, Quality Assurance and Validation personnel, Business Managers and Regulators across both Medical Device and Pharmaceutical industries that would like to learn more about CLS best practices, and CLS practitioners wanting to learn more about medical device software development

Age-Related Macular Degeneration and Diabetic Retinopathy

This reprint includes contributions from leaders in the field of personalized medicine in ophthalmology. The contributions are diverse and cover pre-clinical and clinical topics. We hope you enjoy reading the articles

Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases

Cardiothoracic and pulmonary diseases are a significant cause of mortality and morbidity worldwide. The COVID-19 pandemic has highlighted the lack of access to clinical care, the overburdened medical system, and the potential of artificial intelligence (AI) in improving medicine. There are a variety of diseases affecting the cardiopulmonary system including lung cancers, heart disease, tuberculosis (TB), etc., in addition to COVID-19-related diseases. Screening, diagnosis, and management of cardiopulmonary diseases has become difficult owing to the limited availability of diagnostic tools and experts, particularly in resource-limited regions. Early screening, accurate diagnosis and staging of these diseases could play a crucial role in treatment and care, and potentially aid in reducing mortality. Radiographic imaging methods such as computed tomography (CT), chest X-rays (CXRs), and echo ultrasound (US) are widely used in screening and diagnosis. Research on using image-based AI and machine learning (ML) methods can help in rapid assessment, serve as surrogates for expert assessment, and reduce variability in human performance. In this Special Issue, “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases”, we have highlighted exemplary primary research studies and literature reviews focusing on novel AI/ML methods and their application in image-based screening, diagnosis, and clinical management of cardiopulmonary diseases. We hope that these articles will help establish the advancements in AI