Endothelial damage after planned extracapsular cataract extraction and phacoemulsification of hard cataracts

PURPOSE: To evaluate and compare the endothelial damage after planned extracapsular cataract extraction (ECCE) and phacoemulsification of very hard cataracts. METHODS: In this prospective, randomized study, 41 patients with age-related and very hard cataract were divided into two groups: in group 1 (21 patients) an extracapsular cataract extraction was performed and in group 2 (20 patients), phacoemulsification. In both groups, intraocular lenses were implanted in the capsular bag. Preoperatively and 1, 3 and 6 months postoperatively, a complete ophthalmological examination, endothelial specular microscopy, and ultrasonic pachymetry were done. Endothelial cell loss, pleomorphism, polymegathism and corneal thickness were studied. RESULTS: Both groups presented an endothelial cell loss in the postoperative time, as compared with the preoperative values, but there were no significant differences among the postoperative values (1, 3 and 6 months). Six months after surgery, mean cell loss was 28.50% in group 1 and 34.77% in group 2. There were no differences among the indexes of pachymetry, polymegathism and pleomorphism between the two groups. CONCLUSIONS: Endothelial response was not statistically different between the two studied groups.OBJETIVO: Avaliar o dano endotelial em cirurgias de catarata com núcleo duro, comparando as técnicas da extração extracapsular planejada da catarata e a facoemulsificação. MÉTODOS: Estudo prospectivo, incluindo 41 pacientes com catarata senil e núcleo muito duro, que foram divididos aleatoriamente em dois grupos: grupo 1 (21 pacientes) foi submetido à extração extracapsular da catarata (EECC) e grupo 2 (20 pacientes) à facoemulsificação (FACO); em todos casos foram implantadas lentes intra-oculares no saco capsular. Exame oftalmológico completo, microscopia especular e paquimetria foram realizados no pré-operatório e com um, três e seis meses de pós-operatório. Perda endotelial, pleomorfismo, polimegatismo e paquimetria foram aspectos estudados. RESULTADOS: Nos dois grupos, ocorreu perda endotelial significativa, comparando os valores pré e pós-operatórios, mas não houve diferença entre os tempos pós-operatórios (um, três e seis meses). Não houve diferença estatística da perda endotelial entre os grupos da extração extracapsular da catarata e facoemulsificação, em todos tempos estudados. A média de perda endotelial com seis meses de cirurgia no grupo 1 (EECC) foi de 28,50% e no grupo 2 (FACO), de 34,77%. Não houve diferença significativa nas medidas da paquimetria, polimegatismo e pleomorfismo, entre os dois grupos. CONCLUSÕES: As diferenças percentuais da densidade endotelial, polimegatismo, pleomorfismo e paquimetria não foram estatisticamente significantes entre o grupo da extração extracapsular da catarata e da facoemulsificação, em todos tempos estudados.Universidade Federal de São Paulo (UNIFESP) Departamento de OftalmologiaUNIFESP Departamento de OftalmologiaUniversidade Metropolitana de Santos Departamento de OftalmologiaEye Clinic Day HospitalUNIFESP, Depto. de OftalmologiaUNIFESP, Depto. de OftalmologiaSciEL

An Explanatory Model Steering System for Collaboration between Domain Experts and AI

With the increasing adoption of Artificial Intelligence (AI) systems in high-stake domains, such as healthcare, effective collaboration between domain experts and AI is imperative. To facilitate effective collaboration between domain experts and AI systems, we introduce an Explanatory Model Steering system that allows domain experts to steer prediction models using their domain knowledge. The system includes an explanation dashboard that combines different types of data-centric and model-centric explanations and allows prediction models to be steered through manual and automated data configuration approaches. It allows domain experts to apply their prior knowledge for configuring the underlying training data and refining prediction models. Additionally, our model steering system has been evaluated for a healthcare-focused scenario with 174 healthcare experts through three extensive user studies. Our findings highlight the importance of involving domain experts during model steering, ultimately leading to improved human-AI collaboration.Comment: Demo paper accepted for ACM UMAP 202

Representation Debiasing of Generated Data Involving Domain Experts

Biases in Artificial Intelligence (AI) or Machine Learning (ML) systems due to skewed datasets problematise the application of prediction models in practice. Representation bias is a prevalent form of bias found in the majority of datasets. This bias arises when training data inadequately represents certain segments of the data space, resulting in poor generalisation of prediction models. Despite AI practitioners employing various methods to mitigate representation bias, their effectiveness is often limited due to a lack of thorough domain knowledge. To address this limitation, this paper introduces human-in-the-loop interaction approaches for representation debiasing of generated data involving domain experts. Our work advocates for a controlled data generation process involving domain experts to effectively mitigate the effects of representation bias. We argue that domain experts can leverage their expertise to assess how representation bias affects prediction models. Moreover, our interaction approaches can facilitate domain experts in steering data augmentation algorithms to produce debiased augmented data and validate or refine the generated samples to reduce representation bias. We also discuss how these approaches can be leveraged for designing and developing user-centred AI systems to mitigate the impact of representation bias through effective collaboration between domain experts and AI.Comment: Pre-print of a paper accepted for ACM UMAP 202

GenderMag: A Method for Evaluating Software’s Gender Inclusiveness

In recent years, research into gender differences has established that individual differences in how people problem-solve often cluster by gender. Research also shows that these differences have direct implications for software that aims to support users’ problem-solving activities, and that much of this software is more supportive of problem-solving processes favored (statistically) more by males than by females. However, there is almost no work considering how software practitioners—such as User Experience (UX) professionals or software developers—can find gender-inclusiveness issues like these in their software. To address this gap, we devised the GenderMag method for evaluating problem-solving software from a gender-inclusiveness perspective. The method includes a set of faceted personas that bring five facets of gender difference research to life, and embeds use of the personas into a concrete process through a gender-specialized Cognitive Walkthrough. Our empirical results show that a variety of practitioners who design software—without needing any background in gender research—were able to use the GenderMag method to find gender-inclusiveness issues in problem-solving software. Our results also show that the issues the practitioners found were real and fixable. This work is the first systematic method to find gender-inclusiveness issues in software, so that practitioners can design and produce problem-solving software that is more usable by everyone

Monitoring Quality of Life Indicators at Home from Sparse and Low-Cost Sensor Data.

Supporting older people, many of whom live with chronic conditions or cognitive and physical impairments, to live independently at home is of increasing importance due to ageing demographics. To aid independent living at home, much effort is being directed at reliably detecting activities from sensor data to monitor people’s quality of life or to enhance self-management of their own health. Current efforts typically leverage smart homes which have large numbers of sensors installed to overcome challenges in the accurate detection of activities. In this work, we report on the results of machine learning models based on data collected with a small number of low-cost, off-the-shelf passive sensors that were retrofitted in real homes, some with more than a single occupant. Models were developed from the sensor data collected to recognize activities of daily living, such as eating and dressing as well as meaningful activities, such as reading a book and socializing. We evaluated five algorithms and found that a Recurrent Neural Network was most accurate in recognizing activities. However, many activities remain difficult to detect, in particular meaningful activities, which are characterized by high levels of individual personalization. Our work contributes to applying smart healthcare technology in real-world home settings

Lay user involvement in developing human-centric responsible AI systems:When and how?

Artificial Intelligence (AI) is increasingly used in mainstream applications to make decisions that affect a large number of people. While research has focused on involving machine learning and domain experts during the development of responsible AI systems, the input of lay users has too often been ignored. By exploring the involvement of lay users, our work seeks to advance human-centric responsible AI development processes. To reflect on lay users’ views, we conducted an online survey of 1,121 people in the United Kingdom. We found that respondents had concerns about fairness and transparency of AI systems, which requires more education around AI to underpin lay user involvement. They saw a need for having their views reflected at all stages of the AI development lifecycle. Lay users mainly charged internal stakeholders to oversee the development process but supported by an ethics committee and input from an external regulatory body. We also probed for possible techniques for involving lay users more directly. Our work has implications for creating processes that ensure the development of responsible AI systems that take lay user perspectives into account

User Characteristics in Explainable AI: The Rabbit Hole of Personalization?

As Artificial Intelligence (AI) becomes ubiquitous, the need for Explainable AI (XAI) has become critical for transparency and trust among users. A significant challenge in XAI is catering to diverse users, such as data scientists, domain experts, and end-users. Recent research has started to investigate how users' characteristics impact interactions with and user experience of explanations, with a view to personalizing XAI. However, are we heading down a rabbit hole by focusing on unimportant details? Our research aimed to investigate how user characteristics are related to using, understanding, and trusting an AI system that provides explanations. Our empirical study with 149 participants who interacted with an XAI system that flagged inappropriate comments showed that very few user characteristics mattered; only age and the personality trait openness influenced actual understanding. Our work provides evidence to reorient user-focused XAI research and question the pursuit of personalized XAI based on fine-grained user characteristics.Comment: 20 pages, 4 tables, 2 figure

End-user feature labeling: Supervised and semi-supervised approaches based on locally-weighted logistic regression

When intelligent interfaces, such as intelligent desktop assistants, email classifiers, and recommender systems, customize themselves to a particular end user, such customizations can decrease productivity and increase frustration due to inaccurate predictions — especially in early stages when training data is limited. The end user ca improve the learning algorithm by tediously labeling a substantial amount of additional training data, but this takes time and is too ad hoc to target a particular area of inaccuracy. To solve this problem, we propose new supervised and semi-supervised learning algorithms based on locally weighted logistic regression for feature labeling by end users, enabling them to point out which features are important for a class, rather than provide new training instances. We first evaluate our algorithms against other feature labeling algorithms under idealized conditions using feature labels generated by an oracle. In addition, another of our contributions is an evaluation of feature labeling algorithms under real world conditions using feature labels harvested from actual end users in our user study. Our user study is the first statistical user study for feature labeling involving a large number of end users (43 participants), all of whom have no background in machine learning. Our supervised and semi-supervised algorithms were among the best performers when compared to other feature labeling algorithms in the idealized setting and they are also robust to poor quality feature labels provided by ordinary end users in our study. We also perform an analysis to investigate the relative gains of incorporating the different sources of knowledge available in the labeled training set, the feature labels and the unlabeled data. Together, our results strongly suggest that feature labeling by end users is both viable and effective for allowing end users to improve the learning algorithm behind their customized applications

Monitoring meaningful activities using small low-cost devices in a smart home

A challenge associated with an ageing population is increased demand on health and social care, creating a greater need to enable persons to live independently in their own homes. Ambient assistant living technology aims to address this by monitoring occupants’ ‘activities of daily living’ using smart home sensors to alert caregivers to abnormalities in routine tasks and deteriorations in a person’s ability to care for themselves. However, there has been less focus on using sensing technology to monitor a broader scope of so-called ‘meaningful activities’, which promote a person’s emotional, creative, intellectual, and spiritual needs. In this paper, we describe the development of a toolkit comprised of off-the-shelf, affordable sensors to allow persons with dementia and Parkinson’s disease to monitor meaningful activities as well as activities of daily living in order to self-manage their life and well-being. We describe two evaluations of the toolkit, firstly a lab-based study to test the installation of the system including the acuity and placement of sensors and secondly, an in-the-wild study where subjects who were not target users of the toolkit, but who identified as technology enthusiasts evaluated the feasibility of the toolkit to monitor activities in and around real homes. Subjects from the in-the-wild study reported minimal obstructions to installation and were able to carry out and enjoy activities without obstruction from the sensors, revealing that meaningful activities may be monitored remotely using affordable, passive sensors. We propose that our toolkit may enhance assistive living systems by monitoring a wider range of activities than activities of daily living

Evaluating an interactive tool that reasons about quality of life to support life planning by older people

Objectives: In response to the lack of digital support for older people to plan their lives for quality of life, research was undertaken to co-design and then evaluate a new digital tool that combined interactive guidance for life planning with a computerised model of quality of life. Method: First, a workshop-based process for co-designing the SCAMPI tool with older people is reported. A first version of this tool was then evaluated over eight consecutive weeks by nine older people living in their own homes. Four of these people were living with Parkinson's disease, one with early-stage dementia, and four without any diagnosed chronic condition. Regular semi-structured interviews were undertaken with each individual older person and, where wanted, their life partner. A more in-depth exit interview was conducted at the end of the period of tool use. Themes arising from analyses of content from these interviews were combined with first-hand data collected from the tool's use to develop a description of how each older person used the tool over the 8 weeks. Results: The findings provided the first evidence that the co-designed tool, and in particular the computerised model, could offer some value to older people. Although some struggled to use the tool as it was designed, which led to limited uptake of the tool's suggestions, the older people reported factoring these suggestions into their longer-term planning, as health and/or circumstances might change. Conclusions: The article contributes to the evolving discussion about how to deploy such digital technologies to support quality of life more effectively