Accessible tourism futures: the world we dream to live in and the opportunities we hope to have

© 2015, Eleni Michopoulou, Simon Darcy, Ivor Ambrose and Dimitros Buhalis. PurposeAccessible tourism is evolving as a field of academic research and industry practice, set within a dynamic social context. The field is interdisciplinary, multidisciplinary and transdisciplinary. The purpose of this paper is to examine key concepts and global initiatives that will shape accessible tourism futures. Design/methodology/approachThree of the authors have extensive academic experience in the area and the fourth author is the Managing Director of the pre-eminent European Network for Accessible Tourism. In taking a limited Delphi approach to canvassing key areas likely to shape accessible tourism futures, the following concepts and policy initiatives were examined: motivations, dreams and aspirations of people with disability; demography; UN Convention on the Rights of Persons with Disabilities; destination competitiveness; universal design (UD); and the UN Sustainable Development Goals for 2030. FindingsA discussion of each of the above areas was placed in context to accessible tourism futures and to contextualise the papers that were selected for the special issue. The latter part of the paper outlines the contribution of each empirical paper to the issue discussing the approach, findings and implications. Stakeholder collaboration was identified as the key common theme of the papers and the factor for developing accessible tourism solutions, recognising the value of the market and capitalising on it. A collaborative approach is required to recognise the complementary nature of the different paradigms; to re-shape and transform the future of the accessible tourism industry. To assist in the development of accessible tourism futures, UD principles should provide a foundation to enhance the future competitiveness of tourism destinations and organisations. Originality/valueThe paper’s examination of the concepts and global policy considerations provides a strong academic and practitioner foundation for considering accessible tourism futures. In doing so, accessible tourism futures are shown to be affected by key concepts related to core tourism considerations and major policy initiatives on accessibility and sustainability. Yet, accessible tourism futures also have the potential to create their own momentum and contribute unique learnings on the diversity of tourism markets that will shape tourism concepts and global policy initiatives in their own right

Computer assisted characterization of cervical intervertebral disc degeneration in MRI

A texture-based pattern recognition system is proposed for the automatic characterization of cervical intervertebral disc degeneration from saggital magnetic resonance images of the spine. A case sample of 50 manually segmented ROIs, corresponding to 25 normal and 25 degenerated discs, was analyzed and textural features were generated from each disc-ROI. Student's t-test verified the existence of statistically significant differences between textural feature values generated from normal and degenerated discs. This finding is indicative of disc image texture differentiation due to the degeneration of the disc. The generated features were employed in the design of a pattern recognition system based on the Least Squares Minimum Distance classifier. The system achieved a classification accuracy of 94{%} and it may be of value to physicians for the assessment of cervical intervertebral disc degeneration in MRI

Investigating the role of SPECT/CT in dynamic sentinel lymph node biopsy for penile cancers

PURPOSE: Currently, most centres use 2-D planar lymphoscintigraphy when performing dynamic sentinel lymph node biopsy in penile cancer patients with clinically impalpable inguinal nodes. This study aimed to investigate the role of SPECT/CT following 2-D planar lymphoscintigraphy (dynamic and static) in the detection and localization of sentinel lymph nodes in the groin. METHODS: A qualitative (visual) review was performed on planar followed by SPECT/CT lymphoscintigraphy in 115 consecutive patients (age 28-86 years) who underwent injection of (99m)Tc-nanocolloid followed by immediate acquisition of dynamic (20 min) and early static scans (5 min) initially and further delayed static (5 min) images at 120 min followed by SPECT/CT imaging. The lymph nodes detected in each groin on planar lymphoscintigraphy and SPECT/CT were compared. RESULTS: A total of 440 and 467 nodes were identified on planar scintigraphy and SPECT/CT, respectively. Overall, SPECT/CT confirmed the findings of planar imaging in 28/115 cases (24%). In the remaining 87 cases (76%), gross discrepancies were observed between planar and SPECT/CT images. SPECT/CT identified 17 instances of skin contamination (16 patients, 13%) and 36 instances of in-transit lymphatic tract activity (24 patients, 20%) that had been interpreted as tracer-avid lymph nodes on planar imaging. In addition, SPECT/CT identified 53 tracer-avid nodes in 48 patients (42%) that were not visualized on planar imaging and led to reclassification of the drainage basins (pelvic/inguinal) of 27 tracer-avid nodes. CONCLUSIONS: The addition of SPECT/CT improved the rate of detection of true tracer-avid lymph nodes and delineated their precise (3-D) anatomic localization in drainage basins

Simultaneous PET-MRI Studies of the Concordance of Atrophy and Hypometabolism in Syndromic Variants of Alzheimer's Disease and Frontotemporal Dementia: An Extended Case Series

Background: Simultaneous PET-MRI is used to compare patterns of cerebral hypometabolism and atrophy in six different dementia syndromes. Objectives: The primary objective was to conduct an initial exploratory study regarding the concordance of atrophy and hypometabolism in syndromic variants of Alzheimer’s disease (AD) and frontotemporal dementia (FTD). The secondary objective was to determine the effect of image analysis methods on determination of atrophy and hypometabolism. Method: PET and MRI data were acquired simultaneously on 24 subjects with six variants of AD and FTD (n = 4 per group). Atrophy was rated visually and also quantified with measures of cortical thickness. Hypometabolism was rated visually and also quantified using atlas- and SPM-based approaches. Concordance was measured using weighted Cohen’s kappa. Results: Atrophy-hypometabolism concordance differed markedly between patient groups; kappa scores ranged from 0.13 (nonfluent/agrammatic variant of primary progressive aphasia, nfvPPA) to 0.49 (posterior cortical variant of AD, PCA). Heterogeneity was also observed within groups; the confidence intervals of kappa scores ranging from 0–0.25 for PCA to 0.29–0.61 for nfvPPA. More widespread MRI and PET changes were identified using quantitative methods than on visual rating. Conclusion: The marked differences in concordance identified in this initial study may reflect differences in the molecular pathologies underlying AD and FTD syndromic variants but also operational differences in the methods used to diagnose these syndromes. The superior ability of quantitative methodologies to detect changes on PET and MRI, if confirmed on larger cohorts, may favor their usage over qualitative visual inspection in future clinical diagnostic practic

Pitfalls and artifacts using the D-SPECT dedicated cardiac camera

Myocardial perfusion imaging is a well-established and widely used imaging technique for the assessment of patients with known or suspected coronary artery disease. Pitfalls and artifacts associated with conventional gamma cameras are well known, and the ways to avoid and correct them have been described. In recent years solid-state detector dedicated cardiac cameras were introduced and have been shown to offer improved accuracy in addition to new imaging protocols and novel applications. The purpose of this manuscript is to familiarize the readers with the causes and effects of technical, patient-related, and operator-related pitfalls and artifacts associated with the D-SPECT dedicated cardiac camera with solid-state detectors. The manuscript offers guidance on how to avoid these factors, how to detect them, and how to correct better for them, providing high-quality diagnostic images

Spatiotemporal distribution and speciation of silver nanoparticles in the healing wound

Funding: This research was supported by funds from the MIUR-FIRB project number RBFR08M6W8. Acknowledgments: ELGA LabWater is acknowledged for providing the PURELAB Option-Q and Ultra Analytic systems, which produced the ultra-pure water used for Ag determinations. Adam Douglas and Dhinesh Asogan are acknowledged for their technical support during LA-ICP-MS analysis at the University of Venice, and the authors gratefully acknowledge Bill Spence and Teledyne Cetac Technologies for the loan of the laser ablation instrumentation. Laura Molin and ISTM-CNR are acknowledged for MALDI-TOF-MS analysis. The synchrotron experiments were performed on beamline ID21 at the European Synchrotron Radiation Facility (ESRF), Grenoble, France (proposal #CH4121).Peer reviewedPostprin

Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: a systematic review

Introduction Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia. Methods We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases. Results A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort. Discussion The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice. Highlights There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bia

Image analysis for the diagnosis of MR images of the lumbar spine

Intervertebral disc degeneration is related to chronic back pain and functional incapacity. Magnetic Resonance Imaging (MRI) is the modality of choice for diagnosing this condition, providing both morphological and biochemical information for the disc tissue. In clinical practice, grading schemes based on qualitative descriptions of disc image features such as the signal intensity and disc height are commonly used for disc degeneration severity evaluation. However, these grading schemes have a limited number of degeneration severity classes which impairs the detection of small changes. Additionally, this grading is susceptible to inter and intra observer variabilities. To deal with these issues, this study introduces a system for the automated quantification and computer aided diagnosis of disc degeneration severity from spine MRI. The proposed system consists of a segmentation method, a quantification process, and a classification scheme. An atlas-based segmentation approach, combining prior anatomical knowledge provided by means of a probabilistic disc atlas with fuzzy clustering techniques, was designed for extracting the disc region from the images. In the quantification process, texture and shape descriptors are calculated from the segmented disc region aiming to capture structural and biochemical alterations of the tissue related to degeneration. Finally, the classification scheme exploits this information for differentiating between degeneration severity grades. The system is tested on a case sample of 255 discs from conventional T2-weighted MR images acquired by a 3 Tesla scanner. Results indicate that the atlas-based method provides accurate disc segmentation, texture descriptors measuring intensity inhomogeneity can serve the quantification of degeneration severity, and the computer aided diagnosis scheme achieves high agreement to clinical diagnosis. Concluding, the proposed system could be a valuable tool in hands of physicians to support clinical diagnosis of disc degeneration, track the evolution of disease progress and monitor the response to treatment in a simple, precise and repeatable manner