Protocol for the crowdsourced image-based morbidity hotspot surveillance for neglected tropical diseases (CIMS-NTDs).

INTRODUCTION: Efficient NTDs elimination strategies require effective surveillance and targeted interventions. Traditional methods are costly and time-consuming, often failing to cover entire populations in case of movement restrictions. To address these challenges, a morbidity image-based surveillance system is being developed. This innovative approach which leverages the smartphone technology aims at simultaneous surveillance of multiple NTDs, enhancing cost-efficiency, reliability, and community involvement, particularly in areas with movement constraints. Moreover, it holds promise for post-elimination surveillance. METHODOLOGY: The pilot of this method will be conducted across three states in southern Nigeria. It will target people affected by Neglected Tropical Diseases and members of their communities. The new surveillance method will be introduced to target communities in the selected states through community stakeholder's advocacy meetings and awareness campaigns. The pilot which is set to span eighteen months, entails sensitizing NTDs-affected individuals and community members using signposts, posters, and handbills, to capture photos of NTDs manifestations upon notice using smartphones. These images, along with pertinent demographic information, will be transmitted to a dedicated server through WhatsApp or Telegram accounts. The received images will be reviewed and organized at backend and then forwarded to a panel of experts for identification and annotation to specific NTDs. Data generated, along with geocoordinate information, will be used to create NTDs morbidity hotspot maps using ArcGIS. Accompanying metadata will be used to generate geographic and demographic distributions of various NTDs identified. To protect privacy, people will be encouraged to send manifestation photos of the affected body part only without any identifiable features. EVALUATION PROTOCOL: NTDs prevalence data obtained using conventional surveillance methods from both the pilot and selected control states during the pilot period will be compared with data from the CIMS-NTDs method to determine its effectiveness. EXPECTED RESULTS AND CONCLUSION: It is expected that an effective, privacy-conscious, population inclusive new method for NTDs surveillance, with the potential to yield real-time data for the identification of morbidity hotspots and distribution patterns of NTDs will be established. The results will provide insights into the effectiveness of the new surveillance method in comparison to traditional approaches, potentially advancing NTDs elimination strategies.

Charitable crowdfunding donation-intention estimation depending on emotional project images using fNIRS-based functional connectivity.

Charitable fundraising increasingly relies on online crowdfunding platforms. Project images of charitable crowdfunding use emotional appeals to promote helping behavior. Negative emotions are commonly used to motivate helping behavior because the image of a happy child may not motivate donors to donate as willingly. However, some research has found that happy images can be more beneficial. These contradictory results suggest that the emotional valence of project imagery and how fundraisers frame project images effectively remain debatable. Thus, we compared and analyzed brain activation differences in the prefrontal cortex governing human emotions depending on donation decisions using functional near-infrared spectroscopy, a neuroimaging device. We advance existing theory on charitable behavior by demonstrating that little correlation exists in donation intentions and brain activity between negative and positive project images, which is consistent with survey results on donation intentions by victim image. We also discovered quantitative brain hemodynamic signal variations between donors and nondonors, which can predict and detect donor mental brain functioning using functional connectivity, that is, the statistical dependence between the time series of electrophysiological activity and oxygenated hemodynamic levels in the prefrontal cortex. These findings are critical in developing future marketing strategies for online charitable crowdfunding platforms, especially project images.

Enabling Health Information Recommendation Using Crowdsourced Refinement in Web-Based Health Information Applications: User-Centered Design Approach and EndoZone Informatics Case Study.

BACKGROUND: In the digital age, search engines and social media platforms are primary sources for health information, yet their commercial interests-focused algorithms often prioritize irrelevant content. Web-based health applications by reputable sources offer a solution to circumvent these biased algorithms. Despite this advantage, there remains a significant gap in research on the effective integration of content-ranking algorithms within these specialized health applications to ensure the delivery of personalized and relevant health information. OBJECTIVE: This study introduces a generic methodology designed to facilitate the development and implementation of health information recommendation features within web-based health applications. METHODS: We detail our proposed methodology, covering conceptual foundation and practical considerations through the stages of design, development, operation, review, and optimization in the software development life cycle. Using a case study, we demonstrate the practical application of the proposed methodology through the implementation of recommendation functionalities in the EndoZone platform, a platform dedicated to providing targeted health information on endometriosis. RESULTS: Application of the proposed methodology in the EndoZone platform led to the creation of a tailored health information recommendation system known as EndoZone Informatics. Feedback from EndoZone stakeholders as well as insights from the implementation process validate the methodology's utility in enabling advanced recommendation features in health information applications. Preliminary assessments indicate that the system successfully delivers personalized content, adeptly incorporates user feedback, and exhibits considerable flexibility in adjusting its recommendation logic. While certain project-specific design flaws were not caught in the initial stages, these issues were subsequently identified and rectified in the review and optimization stages. CONCLUSIONS: We propose a generic methodology to guide the design and implementation of health information recommendation functionality within web-based health information applications. By harnessing user characteristics and feedback for content ranking, this methodology enables the creation of personalized recommendations that align with individual user needs within trusted health applications. The successful application of our methodology in the development of EndoZone Informatics marks a significant progress toward personalized health information delivery at scale, tailored to the specific needs of users.

Quantifying the impact of misinformation and vaccine-skeptical content on Facebook.

Low uptake of the COVID-19 vaccine in the US has been widely attributed to social media misinformation. To evaluate this claim, we introduce a framework combining lab experiments (total N = 18,725), crowdsourcing, and machine learning to estimate the causal effect of 13,206 vaccine-related URLs on the vaccination intentions of US Facebook users (N ≈ 233 million). We estimate that the impact of unflagged content that nonetheless encouraged vaccine skepticism was 46-fold greater than that of misinformation flagged by fact-checkers. Although misinformation reduced predicted vaccination intentions significantly more than unflagged vaccine content when viewed, Facebook users' exposure to flagged content was limited. In contrast, unflagged stories highlighting rare deaths after vaccination were among Facebook's most-viewed stories. Our work emphasizes the need to scrutinize factually accurate but potentially misleading content in addition to outright falsehoods.

Boosting wisdom of the crowd for medical image annotation using training performance and task features.

A crucial bottleneck in medical artificial intelligence (AI) is high-quality labeled medical datasets. In this paper, we test a large variety of wisdom of the crowd algorithms to label medical images that were initially classified by individuals recruited through an app-based platform. Individuals classified skin lesions from the International Skin Lesion Challenge 2018 into 7 different categories. There was a large dispersion in the geographical location, experience, training, and performance of the recruited individuals. We tested several wisdom of the crowd algorithms of varying complexity from a simple unweighted average to more complex Bayesian models that account for individual patterns of errors. Using a switchboard analysis, we observe that the best-performing algorithms rely on selecting top performers, weighting decisions by training accuracy, and take into account the task environment. These algorithms far exceed expert performance. We conclude by discussing the implications of these approaches for the development of medical AI.

The Wallet Biopsy: Medical Crowdfunding for Heart Transplantation.

Financial considerations continue to impact access to heart transplantation. Transplant recipients face various costs, including, but not limited to, the index hospitalization, immunosuppressive medications, and lodging and travel to appointments. In this study, we sought to describe the state of crowdfunding for individuals being evaluated for heart transplantation. Using the search term heart transplant, 1000 GoFundMe campaigns were reviewed. After exclusions, 634 (63.4%) campaigns were included. Most campaigns were in support of white individuals (57.8%), males (63.1%) and adults (76.7%). Approximately 15% of campaigns had not raised any funds. The remaining campaigns fundraised a median of $53.24 dollars per day. Of the patients, 44% were admitted at the time of the fundraising. Within the campaigns in the United States, the greatest proportions were in the Southeast United States in non-Medicaid expansion states. These findings highlight the significant financial toxicities associated with heart transplantation and the need for advocacy at the governmental and payer levels to improve equitable access and coverage for all.

A Perspective on Crowdsourcing and Human-in-the-Loop Workflows in Precision Health.

Modern machine learning approaches have led to performant diagnostic models for a variety of health conditions. Several machine learning approaches, such as decision trees and deep neural networks, can, in principle, approximate any function. However, this power can be considered to be both a gift and a curse, as the propensity toward overfitting is magnified when the input data are heterogeneous and high dimensional and the output class is highly nonlinear. This issue can especially plague diagnostic systems that predict behavioral and psychiatric conditions that are diagnosed with subjective criteria. An emerging solution to this issue is crowdsourcing, where crowd workers are paid to annotate complex behavioral features in return for monetary compensation or a gamified experience. These labels can then be used to derive a diagnosis, either directly or by using the labels as inputs to a diagnostic machine learning model. This viewpoint describes existing work in this emerging field and discusses ongoing challenges and opportunities with crowd-powered diagnostic systems, a nascent field of study. With the correct considerations, the addition of crowdsourcing to human-in-the-loop machine learning workflows for the prediction of complex and nuanced health conditions can accelerate screening, diagnostics, and ultimately access to care.

Crowdsourcing for mining new fungal sources for addressing the need for novel antibiotics against multidrug resistant pathogens.

There are a limited number of new antibiotics to manage the health crisis caused by the evolution and spread of antimicrobial resistant (AMR) bacteria including multidrug resistant (MDR), extensively drug-resistant (XDR) and pan-drug-resistant (PDR) ones. Bioprospecting fungi of less studied and extreme environments using new and less used older approaches could reveal novel antibiotics to manage MDR pathogens. Furthermore, I posit a crowdsourcing model which could substantially increase the chances of discovering novel antibiotics as well as new chemotypes for other therapeutic areas and considerably reduce the cost and time of this exercise.

The Impact of Health Literacy on Trust in Online Plastic Surgery Content: A Nationwide Analysis.

INTRODUCTION: On most online platforms, just about anyone can disseminate plastic surgery (PS) content regardless of their educational or professional background. This study examines the general public's perceptions of the accuracy of online PS content and the factors that contribute to the discernment of credible information. METHODS: The Amazon Mechanical Turk crowdsourcing platform was used to survey adults in the United States. The survey assessed respondent demographics, health literacy (HL), and perceptions of online PS content accuracy. T-tests, Chi-square tests, and post hoc analyses with Bonferroni corrections assessed differences between HL groups. Multivariate linear regressions assessed associations between sociodemographic variables and perceptions of online content. RESULTS: In total, 428 (92.0%) of 465 complete responses were analyzed. The median age of respondents was 32 y (interquartile range: 29-40). Online sources were predominantly perceived to have a high degree of accuracy, with mean scores of various platforms ranging from 3.8 to 4.5 (1 = not accurate at all; 5 = extremely accurate). The low HL group perceived social media sites and review sites to be more accurate than the high HL respondents, particularly for Reddit (P = 0.004), Pinterest (P = 0.040), and Snapchat (P = 0.002). CONCLUSIONS: There is a concerning relationship between low HL and the perceptions of the accuracy of online PS sources. This study underscores the need for education campaigns, the development of trustworthy online resources, and initiatives to improve HL. By fostering a more informed public, individuals seeking PS can make better informed decisions.

Development of a crowdsourcing- and gamification-based mobile application to collect epidemiological information and promote healthy lifestyles in Mexico.

We developed a mobile application to promote healthy lifestyles and collect non-communicable disease (NCD) data in Mexico. Its theoretical foundations are supported by a framework-guided literature review. With design sprints, Scrum, Model-View-Controller, and Representational State Transfer architecture, we operationalized evidence-based nutrition/physical activity information into a crowdsourcing- and gamification-based application. The application was piloted for three months to monitor the response of 520 adults. Potential improvements were characterized, considering benchmarking, expert guidance, and standards. Salud Activa (English: Active Health) has two crowdsourcing modules: Nutritional scanner, scanning products' bar codes, providing nutritional data, and allowing new product registry feeding our databases; Surveys, comprising gradually-released NCD questions. Three intervention modules were generated: Drinks diary, a beverage assessment component to receive hydration recommendations; Step counter, monitoring users' steps via Google Fit/Health-iOS; Metabolic Avatar, interconnecting modules and changing as a function of beverage and step records. The 3-month median of Salud Activa use was seven days (IQR = 3-12), up to 35% of participants completed a Survey section, and 157 food products were registered through Nutritional scanner. Better customization might benefit usability and user engagement. Quantitative and qualitative data will enhance Salud Activa's design, user uptake, and efficacy in interventions delivered through this platform.

Developing Crowdsourced Clinical Registry Studies.

ABSTRACT: Crowdsourced registries have been used to quickly gather information, especially during emerging public health concerns. Registries that began during the COVID-19 pandemic were used to rapidly answer key questions on coinfections, experimental treatments, and morbidity and mortality outcomes. Registries are also used more frequently to support clinical trials and track long-term outcomes in patient populations. This article reviews registry methodology, including the collection of data from crowdsourcing and real-world sources, that can be applied to nurse researcher and clinical research nurse skill sets. The authors illustrate a recently reported crowdsourced COVID-19 and cryptococcal disease registry that followed project management strategies and the Agency for Healthcare Research and Quality registry guidelines for planning, execution, and analysis of registries and other research methods.

Crowdsourcing citizens for statewide mapping of per- and polyfluoroalkyl substances (PFAS) in Florida drinking water.

Per- and polyfluoroalkyl substances (PFAS) are a class of persistent chemicals that have been associated with a diverse array of adverse environmental and human health related effects. In addition to a growing list of health concerns, PFAS are also ubiquitously used and pervasive in our natural and built environments, and they have an innate ability to be highly mobile once released into the environment with an unmatched ability to resist degradation. As such, PFAS have been detected in a wide variety of environmental matrices, including soil, water, and biota; however, the matrix that largely dictates human exposure to PFAS is drinking water, in large part due to their abundance in water sources and our reliance on drinking water. As Florida is heavily reliant upon water and its varying sources, the primary objective of this study was to survey the presence of PFAS in drinking water collected from taps from the state of Florida (United States). In this study, 448 drinking water samples were collected by networking with trained citizen scientists, with at least one sample collected from each of the 67 counties in Florida. Well water, tap water, and bottled water, all sourced from Florida, were extracted and analyzed (31 PFAS) using isotope dilution and ultra-high-performance liquid chromatography - tandem mass spectrometry (UHPLC-MS/MS). Overall, when examining ∑PFAS: the minimum, maximum, median, and mean were ND, 219, 2.90, and 14.06 ng/L, respectively. The data herein allowed for a comparison of PFAS in drinking water geographically within the state of Florida, providing vital baseline concentrations for prospective monitoring and highlighting hotspots that require additional testing and mitigation. By incorporating citizen scientists into the study, we aimed to educate impacted communities regarding water quality issues and solutions.

Development and validation of VaxConcerns: A taxonomy of vaccine concerns and misinformation with Crowdsource-Viability.

We present VaxConcerns, a taxonomy for vaccine concerns and misinformation. VaxConcerns is an easy-to-teach taxonomy of concerns and misinformation commonly found among online anti-vaccination media and is evaluated to produce high-quality data annotations among crowdsource workers, opening the potential adoption of the framework far beyond just academic or medical communities. The taxonomy shows high agreement among experts and outperforms existing taxonomies for vaccine concerns and misinformation when presented to non-expert users. Our proof-of-concept study on the changes in anti-vaccination content during the COVID-19 pandemic indicate impactful future use cases, such as longitudinal studies of the shift in vaccine concerns over time.

A smartphone-based crowd-sourced real-time surveillance platform (apple snail inspector) for the invasive snails: a design and development study.

BACKGROUND: The large amphibious freshwater apple snail is an important invasive species in China, but there is currently no method available for their surveillance. The development and popularization of smartphones provide a new platform for research on surveillance technologies for the early detection and effective control of invasive species. METHODS: The ASI surveillance system was developed based on the infrastructure of the WeChat platform and Amap. The user can directly enter the game interface through the WeChat port on their mobile phone, and the system automatically obtains their location. The user can then report the location of apple snails. The administrator can audit the reported information, and all information can be exported to Microsoft Excel version 2016 for analysis. The map was generated by ArcGIS 10.2 and was used to characterize the spatial and temporal distribution of apple snails in Jiangsu Province. RESULTS: The architecture of ASI consists of three parts: a mobile terminal, a server terminal and a desktop terminal. We published more than 10 tweets on the official WeChat account of the system to announce it to the public, and a total of 207 users in 2020 and 2021 correctly reported sightings of apple snails. We identified 550 apple snails breeding sites in 2020 and 2021, featuring ponds (81%), parks (17%) and farmland (2%). In addition, most of the locations contained snail eggs, and the reporting times mainly occurred between May and September. CONCLUSIONS: The ASI is an effective surveillance system that can be used to identify the breeding locations of apple snails and provides the basis of prevention and control for its dispersal. Its successful development and operation provide new potential avenues for surveillance of other public health issues.

A critical analysis of trans-visibility through online medical crowdfunding.

In contexts where many people face barriers to accessing gender-affirming care through public systems, some turn to online crowdfunding to fundraise for private care pathways. Crowdfunding platforms invite people to share personal information, stories, and photos publicly, in order to elicit donations. In this article we draw on empirical data from a multimethodological three-year study of medical crowdfunding in Aotearoa New Zealand, with a focus on people crowdfunding for medical transition services. We apply a lens of 'visibility' to analysis of focus groups, interviews, case studies, and campaign pages, presenting findings on who was present and absent (with a focus on binary gender, and whiteness), and who was the assumed or expected audience (with a focus on cis publics). We describe how campaigns were defined by efforts to make trans bodies legible, and campaign requests competitive, through reference to narrow and medicalised frames of dysphoria, suffering, and transformation via medical intervention. We contribute to more comparative work in the literature on crowdfunding by highlighting how these globalised digital technologies are situated in the particular (demographic, cultural, and structural) contexts of Aotearoa New Zealand. We call attention to crowdfunding as a relational practice, in which the public marketisation of the self can have both individual consequences related to privacy and outing, and social consequences, in the reinforcing of trans-normativities. Overall we argue that although crowdfunding represents an adaptive strategy for trans people trying meet their own needs, it ultimately contributes to a type of trans-visibility which is both risky and limiting.

The HI V O pen Call on I nformed C onsent and E thics in Research (VOICE) for Adolescents and Young Adults: A Digital Crowdsourcing Open Call in Low- and Middle-Income Countries.

BACKGROUND: Many adolescents and young adults (AYAs; 10-24 years old) are excluded from HIV research because of social, ethical, and legal challenges with informed consent, resulting in limited AYA-focused data. We use a participatory approach to identify strategies for improving AYA consent processes in HIV research in low- and middle-income countries (LMICs). METHODS: We conducted a digital crowdsourcing open call for ideas to improve AYA consent to HIV research in LMICs. Crowdsourcing involves engaging a group of people in problem-solving, then sharing emergent solutions. Submissions were evaluated by 3 independent judges using predefined criteria, with exceptional strategies receiving prizes. Demographic data were collected, and textual data were qualitatively analyzed for emergent themes in barriers and facilitators for improving AYA consent in HIV research, guided by a socioecological model. RESULTS: We received 110 strategies total; 65 were eligible for evaluation, 25 of which were identified as finalists. Fifty-eight participants from 10 LMICs submitted the 65 eligible submissions, of which 30 (52%) were 18 to 24 years old. Thematic analysis identified 10 barriers to AYA consent, including HIV stigma, limited education, and legal/regulatory barriers. Strategies for improving AYA consent processes revealed 7 potential facilitators: enhancing AYA engagement in research, involving parents/guardians, improving education/awareness, improving institutional practices/policy, making research participation more AYA-friendly, enhancing engagement of other key communities of interest, and empowering AYA. CONCLUSIONS: Diverse communities of interest in LMICs developed compelling strategies to enhance informed consent that may improve AYA inclusion in HIV research. These data will be used to develop practical guidance on improving AYA consent processes.

Defining Preferred Esthetics of the Ideal Phallus via Crowdsource Survey.

OBJECTIVE: To describe phalloplasty subunits and determine the preferred crowdsourced esthetics. Esthetic ideals are often used to guide reconstruction, and there has been an increase in the number of gender-affirming surgeries and reconstructive phalloplasties performed. However, there is a paucity of literature describing ideal phalloplasty esthetics. METHODS: Phallus esthetic subunits were defined, and a split testing-based survey was used. Subjects were solicited via Craigslist, Amazon Mechanical Turk, and Reddit and distributed among health care co-workers. Computer-generated images with variable ratios of glans, corona, and shaft were provided and respondents were asked to select the most esthetically pleasing photo. Demographic information was gathered. Univariate and multivariate regression were performed. RESULTS: A total of 1029 people responded to the survey request and 909 people (88.3%) completed the entire survey. There were 440 respondents who self-identified as male, 334 female, 92 transgender male, and 25 transgender female. The health care field was the profession for 55.4%. Health care providers had 65.3% higher odds of preferring the longer shaft length-to-width ratio, 30.3% less odds of preferring a bilateral taper of the glans, and 48.4% less odds of preferring an angulated shaft compared to non-health care providers (P = .006, P = .021, P <.001, respectively). When compared to males, transgender females were more than 13 times likely to prefer an angulated glans corona junction (P = .008). CONCLUSION: The ideal phallic esthetic varies by individual, and there were statistically significant preferences across age, education, health care status, gender, and sexual orientation. This study can serve as a guide on phalloplasties for patients and gender-affirming surgeons.

Data Liberation and Crowdsourcing in Medical Research: The Intersection of Collective and Artificial Intelligence.

In spite of an exponential increase in the volume of medical data produced globally, much of these data are inaccessible to those who might best use them to develop improved health care solutions through the application of advanced analytics such as artificial intelligence. Data liberation and crowdsourcing represent two distinct but interrelated approaches to bridging existing data silos and accelerating the pace of innovation internationally. In this article, we examine these concepts in the context of medical artificial intelligence research, summarizing their potential benefits, identifying potential pitfalls, and ultimately making a case for their expanded use going forward. A practical example of a crowdsourced competition using an international medical imaging dataset is provided. Keywords: Artificial Intelligence, Data Liberation, Crowdsourcing © RSNA, 2023.

Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge.

Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex organ topology. There exists a high missed detection rate and incomplete removal of colonic polyps. To assist in clinical procedures and reduce missed rates, automated methods for detecting and segmenting polyps using machine learning have been achieved in past years. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets from different centres, populations, modalities, and acquisition systems. To test this hypothesis rigorously, we, together with expert gastroenterologists, curated a multi-centre and multi-population dataset acquired from six different colonoscopy systems and challenged the computational expert teams to develop robust automated detection and segmentation methods in a crowd-sourcing Endoscopic computer vision challenge. This work put forward rigorous generalisability tests and assesses the usability of devised deep learning methods in dynamic and actual clinical colonoscopy procedures. We analyse the results of four top performing teams for the detection task and five top performing teams for the segmentation task. Our analyses demonstrate that the top-ranking teams concentrated mainly on accuracy over the real-time performance required for clinical applicability. We further dissect the devised methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets and routine clinical procedures.

Addressing Current Deficits in Patient Education Materials Through Crowdsourcing.

BACKGROUND: Patient education materials are commonly reported to be difficult to understand. OBJECTIVES: We aimed to use crowdsourcing to improve patient education materials at our institution. METHODS: This was a department-wide quality improvement project to increase organizational health literacy. There are 6 phases of this pilot study: (1) evaluating preexisting patient education materials, (2) evaluating online patient education materials at the society (the American Society of Plastic Surgeon) and government level (Medline Plus), (3) redesigning our patient education material and reevaluating the education material, (4) crowdsourcing to evaluate understandability of the new patient education material, (5) data analysis, and (6) incorporating crowdsourcing suggestions to the patient education material. RESULTS: Breast-related patient education materials are not easy to read at the institution level, the society level, and the government level. Our new implant-based breast reconstruction patient education material is easy to read as demonstrated by the crowdsourcing evaluation. More than 90% of the participants reported our material is "very easy to understand" or "easy to understand." The crowdsourcing process took 1.5 days, with 700 workers responding to the survey. The total cost was $9. After incorporating participants' feedback into the finalized material, the readability of the material is at the recommended reading level. The material also had the recommended length (between 400 and 800 words). DISCUSSION: Our study demonstrated a pathway for clinicians to efficiently obtain a large amount of feedback to improve patient education materials. Crowdsourcing is an effective tool to improve organizational health literacy.