OBJECTIVES: Recently, deep learning medical image analysis in orthopedics has become highly active. However, progress has been restricted by the absence of large-scale and standardized ground-truth images. To the best of our knowledge, this study is the first to propose an innovative solution, namely a deep few-shot image augmentation pipeline, that addresses this challenge by synthetically generating knee radiographs for training downstream tasks, with a specific focus on knee osteoarthritis Kellgren-Lawrence (KL) grading. MATERIALS AND METHODS: This study leverages a deep few-shot image augmentation pipeline to generate synthetic knee radiographs. Despite the limited availability of training samples, we demonstrate the capability of our proposed computational strategy to produce high-fidelity plain knee radiographs and use them to successfully train a KL grade classifier. RESULTS: Our experimental results showcase the effectiveness of the proposed computational pipeline. The generated synthetic radiographs exhibit remarkable fidelity, evidenced by the achieved average Frechet Inception Distance (FID) score of 26.33 for KL grading and 22.538 for bilateral knee radiographs. For KL grading classification, the classifier achieved a test Cohen's Kappa and accuracy of 0.451 and 0.727, respectively. Our computational strategy also resulted in a publicly and freely available imaging dataset of 86 000 synthetic knee radiographs. CONCLUSIONS: Our approach demonstrates the capability to produce top-notch synthetic knee radiographs and use them for KL grading classification, even when working with a constrained training dataset. The results obtained emphasize the effectiveness of the pipeline in augmenting datasets for knee osteoarthritis research, opening doors for broader applications in orthopedics, medical image analysis, and AI-powered diagnosis.

Sentence simplification aims to reduce the complexity of a sentence while retaining its original meaning.Current models for sentence simplification adopted ideas from machine translation studies and implicitly learned simplification mapping rules from normalsimple sentence pairs.In this paper, we explore a novel model based on a multi-layer and multi-head attention architecture and we propose two innovative approaches to integrate the Simple PPDB (A Paraphrase Database for Simplification), an external paraphrase knowledge base for simplification that covers a wide range of real-world simplification rules.The experiments show that the integration provides two major benefits: (1) the integrated model outperforms multiple stateof-the-art baseline models for sentence simplification in the literature (2) through analysis of the rule utilization, the model seeks to select more accurate simplification rules.The code and models used in the paper are available at https://github.com/ Sanqiang/text_simplification.

A novel mobile health (mHealth) system to support self-care and adherence to self-care regimens for patients with chronic disease, called iMHere (interactive mobile health and rehabilitation), has been developed at the University of Pittsburgh. However, the existing design of the iMHere system was initially not suitable for users with a myriad of dexterity impairments. The goal of this study was to design and transform an iMHere system that is usable and accessible for this particular group of users. We approached the accessibility from two essential components of user interface design and development: physical presentation and navigation. Six participants with dexterity impairments were included in this study. User satisfaction significantly improved when using revised apps (P<0.01). The importance of personalization for accessibility and the identified strategies to approach personalization for the mHealth system are discussed.

Websites were retrospectively analysed to study the effects that technological advances in web design have had on accessibility for persons with disabilities. A random sample of general websites and a convenience sample of US government websites were studied and compared for the years 1997 – 2002. Web accessibility barrier (WAB) and complexity scores were calculated. Analysis of Variance (ANOVA) and Tukey's HSD were used to determine differences among years for general sites. Repeated measures of ANOVA were used to analyse trends in US government sites, and Pearson's correlation coefficient (r) was computed to evaluate the relationship between accessibility and complexity. Random websites become progressively inaccessible as complexity increases. US government websites remain accessible while increasing in complexity. Increasing complexity, often caused by adding complex components to a Webpage, doesn't have to contribute to increasing barriers to accessibility: US government websites remain accessible despite increasing complexity by limiting the number of scripts used in Webpage design.

Abstract A novel metric for quantitatively measuring the content accessibility of the Web for persons with disabilities is proposed. The metric is based on the Web Content Accessibility Guidelines (WCAG) checkpoints, an internationally accepted standard, that can be automatically tested using computer programs. Problems with current accessibility evaluation and the need for a good Web accessibility metric are discussed. The proposed metric is intended to overcome the deficiencies of the current measurements used in Web accessibility studies. The proposed metric meets the requirements as a measurement for scientific research. Examples of large‐scale Web accessibility evaluations using the metric are given. The examples cover a comparison of Web accessibility of top medical journal Web sites and a longitudinal study of a Web site over time. The validity of the metric was tested using a large number of Web sites with different levels of compliance (rating categories) to the standard WCAG. The metric, which uses a predetermined simple weighting scheme, compares well to the more complex C5.0 machine learning algorithm in separating Web sites into different rating categories.

Using Internet Archive's Wayback Machine, a random sample of websites from 1997-2002 were retrospectively analyzed for effects that technology has on accessibility for persons with disabilities and compared to government websites. Analysis of Variance (ANOVA) and Tukey's HSD were used to determine differences among years. Random websites become progressively inaccessible through the years (p<0.0001) [as shown by increasing Web Accessibility Barrier (WAB) scores], while complexity of the websites increased through the years (p<0.0001). Pearson's correlation (r) was performed to correlate accessibility and complexity: r=0.463 (p<0.01). Government websites remain accessible while increasing in complexity: r=0.14 (p<0.041). It is concluded that increasing complexity, oftentimes caused by adding new technology to a Web page, inadvertently contributes to increasing barriers to accessibility for persons with disabilities.

Algorithms for reducing variance in neural network prediction using committee and resampling techniques bootstrap and cross-validation are presented. Their effectiveness is tested on data sets with different levels of noise and on medical diagnosis data sets. The methods are most effective when the noise level in the data is high or the size of the learning set is small, which consequently produces high variance. The algorithms will not be of much help in cases where the error of prediction is mainly due to bias. An increase in bias is observed due to smaller effective learning size in the bootstrap and crossvalidation committee. The impact of increased bias on the performance ranges from negligible to completely undermining the benefit of reducing the variance.