CD-CODE: crowdsourcing condensate database and encyclopedia.

The discovery of biomolecular condensates transformed our understanding of intracellular compartmentalization of molecules. To integrate interdisciplinary scientific knowledge about the function and composition of biomolecular condensates, we developed the crowdsourcing condensate database and encyclopedia ( cd-code.org ). CD-CODE is a community-editable platform, which includes a database of biomolecular condensates based on the literature, an encyclopedia of relevant scientific terms and a crowdsourcing web application. Our platform will accelerate the discovery and validation of biomolecular condensates, and facilitate efforts to understand their role in disease and as therapeutic targets.

Automating hybrid collective intelligence in open-ended medical diagnostics.

Collective intelligence has emerged as a powerful mechanism to boost decision accuracy across many domains, such as geopolitical forecasting, investment, and medical diagnostics. However, collective intelligence has been mostly applied to relatively simple decision tasks (e.g., binary classifications). Applications in more open-ended tasks with a much larger problem space, such as emergency management or general medical diagnostics, are largely lacking, due to the challenge of integrating unstandardized inputs from different crowd members. Here, we present a fully automated approach for harnessing collective intelligence in the domain of general medical diagnostics. Our approach leverages semantic knowledge graphs, natural language processing, and the SNOMED CT medical ontology to overcome a major hurdle to collective intelligence in open-ended medical diagnostics, namely to identify the intended diagnosis from unstructured text. We tested our method on 1,333 medical cases diagnosed on a medical crowdsourcing platform: The Human Diagnosis Project. Each case was independently rated by ten diagnosticians. Comparing the diagnostic accuracy of single diagnosticians with the collective diagnosis of differently sized groups, we find that our method substantially increases diagnostic accuracy: While single diagnosticians achieved 46% accuracy, pooling the decisions of ten diagnosticians increased this to 76%. Improvements occurred across medical specialties, chief complaints, and diagnosticians' tenure levels. Our results show the life-saving potential of tapping into the collective intelligence of the global medical community to reduce diagnostic errors and increase patient safety.

Assessing residential PM2.5 concentrations and infiltration factors with high spatiotemporal resolution using crowdsourced sensors.

Building conditions, outdoor climate, and human behavior influence residential concentrations of fine particulate matter (PM2.5). To study PM2.5 spatiotemporal variability in residences, we acquired paired indoor and outdoor PM2.5 measurements at 3,977 residences across the United States totaling >10,000 monitor-years of time-resolved data (10-min resolution) from the PurpleAir network. Time-series analysis and statistical modeling apportioned residential PM2.5 concentrations to outdoor sources (median residential contribution = 52% of total, coefficient of variation = 69%), episodic indoor emission events such as cooking (28%, CV = 210%) and persistent indoor sources (20%, CV = 112%). Residences in the temperate marine climate zone experienced higher infiltration factors, consistent with expectations for more time with open windows in milder climates. Likewise, for all climate zones, infiltration factors were highest in summer and lowest in winter, decreasing by approximately half in most climate zones. Large outdoor-indoor temperature differences were associated with lower infiltration factors, suggesting particle losses from active filtration occurred during heating and cooling. Absolute contributions from both outdoor and indoor sources increased during wildfire events. Infiltration factors decreased during periods of high outdoor PM2.5, such as during wildfires, reducing potential exposures from outdoor-origin particles but increasing potential exposures to indoor-origin particles. Time-of-day analysis reveals that episodic emission events are most frequent during mealtimes as well as on holidays (Thanksgiving and Christmas), indicating that cooking-related activities are a strong episodic emission source of indoor PM2.5 in monitored residences.

Expanding the Immunology Toolbox: Embracing Public-Data Reuse and Crowdsourcing.

New technologies have been propelling dramatic increases in the volume and diversity of large-scale public data, which can potentially be reused to answer questions beyond those originally envisioned. However, this often requires computational and statistical skills beyond the reach of most bench scientists. The development of educational and accessible computational tools is thus critical, as are crowdsourcing efforts that utilize the community's expertise to curate public data for hypothesis generation and testing. Here we review the history of public-data reuse and argue for greater incorporation of computational and statistical sciences into the biomedical education curriculum and the development of biologist-friendly crowdsourcing tools. Finally, we provide a resource list for the reuse of public data and highlight an illustrative crowdsourcing exercise to explore public gene-expression data of human autoimmune diseases and corresponding mouse models. Through education, tool development, and community engagement, immunologists will be poised to transform public data into biological insights.

Crowdsourced RNA design discovers diverse, reversible, efficient, self-contained molecular switches.

Internet-based scientific communities promise a means to apply distributed, diverse human intelligence toward previously intractable scientific problems. However, current implementations have not allowed communities to propose experiments to test all emerging hypotheses at scale or to modify hypotheses in response to experiments. We report high-throughput methods for molecular characterization of nucleic acids that enable the large-scale video game­based crowdsourcing of RNA sensor design, followed by high-throughput functional characterization. Iterative design testing of thousands of crowdsourced RNA sensor designs produced near­thermodynamically optimal and reversible RNA switches that act as self-contained molecular sensors and couple five distinct small molecule inputs to three distinct protein binding and fluorogenic outputs. This work suggests a paradigm for widely distributed experimental bioscience.

Repeat it without me: Crowdsourcing the T1 mapping common ground via the ISMRM reproducibility challenge.

PURPOSE: T1 mapping is a widely used quantitative MRI technique, but its tissue-specific values remain inconsistent across protocols, sites, and vendors. The ISMRM Reproducible Research and Quantitative MR study groups jointly launched a challenge to assess the reproducibility of a well-established inversion-recovery T1 mapping technique, using acquisition details from a seminal T1 mapping paper on a standardized phantom and in human brains. METHODS: The challenge used the acquisition protocol from Barral et al. (2010). Researchers collected T1 mapping data on the ISMRM/NIST phantom and/or in human brains. Data submission, pipeline development, and analysis were conducted using open-source platforms. Intersubmission and intrasubmission comparisons were performed. RESULTS: Eighteen submissions (39 phantom and 56 human datasets) on scanners by three MRI vendors were collected at 3 T (except one, at 0.35 T). The mean coefficient of variation was 6.1% for intersubmission phantom measurements, and 2.9% for intrasubmission measurements. For humans, the intersubmission/intrasubmission coefficient of variation was 5.9/3.2% in the genu and 16/6.9% in the cortex. An interactive dashboard for data visualization was also developed: https://rrsg2020.dashboards.neurolibre.org. CONCLUSION: The T1 intersubmission variability was twice as high as the intrasubmission variability in both phantoms and human brains, indicating that the acquisition details in the original paper were insufficient to reproduce a quantitative MRI protocol. This study reports the inherent uncertainty in T1 measures across independent research groups, bringing us one step closer to a practical clinical baseline of T1 variations in vivo.

Crowdsourcing to increase hepatitis B and C testing and reduce hepatitis stigma among medical students in Bangladesh.

This study addresses the pervasive challenges of low hepatitis B (HBV) and hepatitis C (HCV) testing rates coupled with the stigma associated with these diseases in low- and middle-income countries (LMICs) with a special focus on Bangladesh. This study aims to introduce an innovative crowdsourcing intervention that involves medical students, a crucial cohort with the potential to shape healthcare attitudes. Through a structured crowdsourcing approach, the study designs and implements a digital intervention to counter stigma and promote testing among medical students in Dhaka, Bangladesh. Participants submitted brief videos or texts aiming to encourage hepatitis testing and reduce stigma. The call, advertised through meetings, emails, and social media, welcomed entries in English or Bengali over 3 weeks. A panel of six judges evaluated each entry based on clarity, impact potential, innovation, feasibility, and sustainability, awarding prizes to students behind the highest-rated submissions. Seventeen videos and four text messages received an average score of 5.5 among 440 surveyed medical students, predominantly 22 years old (16%) and in their fourth year (21%). After viewing, 360 students underwent screening, identifying two previously undiagnosed HBV cases referred for care; no HCV infections were found. Notably, 41% expressed concerns about individuals with HBV working in hospitals or having a doctor living with HBV. In conclusion, this pilot showcases the power of medical students in spearheading campaigns to counter hepatitis stigma and encourage testing. By utilizing crowdsourcing, the study introduces an innovative approach to a persistent issue in LMICs specially in Bangladesh, offering a model that could potentially be adapted by other regions grappling with similar challenges.

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.

Using crowdsourcing at an academic conference to develop STI testing messaging for public dissemination.

OBJECTIVES: We provide a guide to conducting a crowdsourcing activity at an international sexually transmitted infection (STI) conference to design public messaging about STI testing and disseminating that messaging via social media. METHODS: A speaker gave a presentation at a conference plenary session on the concepts of cocreation, crowdsourcing and designathons, and the application of these participatory approaches in public health research. To illustrate one of these approaches (crowdsourcing), attendees in the audience were asked to take part in a voluntary participatory activity, in which they would pair up with a fellow attendee sitting nearby and write down an idea on a blank notecard. Dyads were given 10 min to create an entry responding to the prompt, 'Write something that inspires gonorrhoea and/or chlamydia testing (eg, picture, jingle, rhyme)'. Each entry was judged by at least four independent judges on a scale of 0 (lowest quality) to 10 (highest quality) based on their innovation and potential to promote chlamydia/gonorrhoea testing. Scores were averaged to determine the finalist entries. RESULTS: We received 32 entries. The average score was 6.41 and scores ranged from 4.5 to 8 (median 6.63, IQR 5.75, 7.06). Half of entries (n=16) were slogans, 15.6% (n=5) were poems/rhymes, 12.5% (n=4) were memes/images, 9.4% (n=3) were programme implementation ideas, 3.1% (n=1) was a song verse, and 3.1% (n=1) was a video idea. One finalist entry was a meme and received 720 impressions, 120 engagements, 27 detail expands, 19 likes, 6 reposts and 1 response on Twitter. The second finalist entry was a slogan and received 242 impressions, 16 engagements, 6 detail expands, 4 likes and 2 reposts. CONCLUSIONS: Conducting crowdsourcing activities at future conferences may be an innovative, feasible way to develop and disseminate engaging and important STI and other health messaging to the public in a short period of time.

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.

A crowdsourcing database for the copy-number variation of the Spanish population.

BACKGROUND: Despite being a very common type of genetic variation, the distribution of copy-number variations (CNVs) in the population is still poorly understood. The knowledge of the genetic variability, especially at the level of the local population, is a critical factor for distinguishing pathogenic from non-pathogenic variation in the discovery of new disease variants. RESULTS: Here, we present the SPAnish Copy Number Alterations Collaborative Server (SPACNACS), which currently contains copy number variation profiles obtained from more than 400 genomes and exomes of unrelated Spanish individuals. By means of a collaborative crowdsourcing effort whole genome and whole exome sequencing data, produced by local genomic projects and for other purposes, is continuously collected. Once checked both, the Spanish ancestry and the lack of kinship with other individuals in the SPACNACS, the CNVs are inferred for these sequences and they are used to populate the database. A web interface allows querying the database with different filters that include ICD10 upper categories. This allows discarding samples from the disease under study and obtaining pseudo-control CNV profiles from the local population. We also show here additional studies on the local impact of CNVs in some phenotypes and on pharmacogenomic variants. SPACNACS can be accessed at: http://csvs.clinbioinfosspa.es/spacnacs/ . CONCLUSION: SPACNACS facilitates disease gene discovery by providing detailed information of the local variability of the population and exemplifies how to reuse genomic data produced for other purposes to build a local reference database.

A crowdsourcing open platform for literature curation in UniProt.

The UniProt knowledgebase is a public database for protein sequence and function, covering the tree of life and over 220 million protein entries. Now, the whole community can use a new crowdsourcing annotation system to help scale up UniProt curation and receive proper attribution for their biocuration work.

Unmet financial needs among patients crowdfunding to support gynecologic cancer care.

BACKGROUND: Patients may use crowdfunding to solicit donations, typically from multiple small donors using internet-based means, to offset the financial toxicity of cancer care. OBJECTIVE: To describe crowdfunding campaigns by gynecologic cancer patients and to compare campaign characteristics and needs expressed between patients with cervical, uterine, and ovarian cancer. STUDY DESIGN: We queried the public crowdfunding forum GoFundMe.com for "cervical cancer," "uterine cancer," and "ovarian cancer." The first 200 consecutive posts for each cancer type fundraising within the United States were analyzed. Data on campaign goals and needs expressed were manually extracted. Descriptive statistics and bivariate analyses were performed. RESULTS: Among the 600 fundraising pages, the median campaign goal was $10,000 [IQR $5000-$23,000]. Campaigns raised a median of 28.6% of their goal with only 8.7% of campaigns reaching their goal after a median of 54 days online. On average, ovarian cancer campaigns had higher monetary goals, more donors, and larger donation amounts than cervical cancer campaigns and raised more money than both cervical and uterine cancer campaigns. Campaigns were fundraising to support medical costs (80-85%) followed by lost wages (36-56%) or living expenses (27-41%). Cervical cancer campaigns reported need for non-medical costs more frequently than uterine or ovarian cancer campaigns. States without Medicaid expansions (31% of the national population) were over-represented among cervical cancer and uterine cancer, but not ovarian cancer campaigns. CONCLUSIONS: Crowdfunding pages reveal patients fundraising for out-of-pocket costs in the thousands of dollars and a wide range of unmet financial needs based on cancer type.

Fourteen quick tips for crowdsourcing geographically linked data for public health advocacy.

This article presents 14 quick tips to build a team to crowdsource data for public health advocacy. It includes tips around team building and logistics, infrastructure setup, media and industry outreach, and project wrap-up and archival for posterity.

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.

Burden of atopic dermatitis in Europe: A population-centred approach leveraging web search data in 21 European countries.

BACKGROUND: The complexity, high prevalence, and substantial personal and socioeconomic burden collectively render atopic dermatitis (AD) a major public health concern. Using crowdsourced Internet data has the potential to provide unique insights into this concern, as demonstrated by several previous studies. However, a comprehensive comparison across European countries remains lacking. OBJECTIVES: The study aimed to investigate AD-related web searches across Europe to assess spatiotemporal variations and associations between disease-related and external factors. METHODS: AD-related web search data were extracted for 21 European countries between February 2019 and January 2023. Descriptive analysis and autocorrelation functions were performed to examine spatiotemporal patterns. Correlations (r) were used to evaluate the associations between web searches and disease-related, socioeconomic and meteorological data. RESULTS: Over 241 million AD-related web searches were identified, with search volume varying substantially among European countries (p < 0.001) and correlating with AD prevalence and disease burden (both r = 0.51, p = 0.019). Search volume increased between 2019 and 2023 in all countries and seasonally peaked in January and March. Negative correlations with median population age (r = -0.46, p = 0.039), number of general practitioners (r = -0.29, p = 0.226) and specialists (r = -0.27, p = 0.270) were observed. Moderate to strong correlations were found between search volume and cold, humid and windy weather with fewer sunshine hours, while higher online interest typically occurred 1-3 months after such weather conditions. CONCLUSION: The study highlights the great potential of online crowdsourced data analysis, for example, to investigate the impact of climate change or to identify unmet needs at a population level. Furthermore, the growing online interest in AD and the corresponding seasonal peaks emphasize the necessity of adapting treatment plans, intensifying public health campaigns, and disseminating reliable online information by governments and healthcare providers, especially during these periods.

Wildfire smoke impacts on indoor air quality assessed using crowdsourced data in California.

Wildfires have become an important source of particulate matter (PM2.5 < 2.5-µm diameter), leading to unhealthy air quality index occurrences in the western United States. Since people mainly shelter indoors during wildfire smoke events, the infiltration of wildfire PM2.5 into indoor environments is a key determinant of human exposure and is potentially controllable with appropriate awareness, infrastructure investment, and public education. Using time-resolved observations outside and inside more than 1,400 buildings from the crowdsourced PurpleAir sensor network in California, we found that the geometric mean infiltration ratios (indoor PM2.5 of outdoor origin/outdoor PM2.5) were reduced from 0.4 during non-fire days to 0.2 during wildfire days. Even with reduced infiltration, the mean indoor concentration of PM2.5 nearly tripled during wildfire events, with a lower infiltration in newer buildings and those utilizing air conditioning or filtration.

Serial reproduction reveals the geometry of visuospatial representations.

An essential function of the human visual system is to locate objects in space and navigate the environment. Due to limited resources, the visual system achieves this by combining imperfect sensory information with a belief state about locations in a scene, resulting in systematic distortions and biases. These biases can be captured by a Bayesian model in which internal beliefs are expressed in a prior probability distribution over locations in a scene. We introduce a paradigm that enables us to measure these priors by iterating a simple memory task where the response of one participant becomes the stimulus for the next. This approach reveals an unprecedented richness and level of detail in these priors, suggesting a different way to think about biases in spatial memory. A prior distribution on locations in a visual scene can reflect the selective allocation of coding resources to different visual regions during encoding ("efficient encoding"). This selective allocation predicts that locations in the scene will be encoded with variable precision, in contrast to previous work that has assumed fixed encoding precision regardless of location. We demonstrate that perceptual biases covary with variations in discrimination accuracy, a finding that is aligned with simulations of our efficient encoding model but not the traditional fixed encoding view. This work demonstrates the promise of using nonparametric data-driven approaches that combine crowdsourcing with the careful curation of information transmission within social networks to reveal the hidden structure of shared visual representations.

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.

Gamified Crowdsourcing as a Novel Approach to Lung Ultrasound Data Set Labeling: Prospective Analysis.

BACKGROUND: Machine learning (ML) models can yield faster and more accurate medical diagnoses; however, developing ML models is limited by a lack of high-quality labeled training data. Crowdsourced labeling is a potential solution but can be constrained by concerns about label quality. OBJECTIVE: This study aims to examine whether a gamified crowdsourcing platform with continuous performance assessment, user feedback, and performance-based incentives could produce expert-quality labels on medical imaging data. METHODS: In this diagnostic comparison study, 2384 lung ultrasound clips were retrospectively collected from 203 emergency department patients. A total of 6 lung ultrasound experts classified 393 of these clips as having no B-lines, one or more discrete B-lines, or confluent B-lines to create 2 sets of reference standard data sets (195 training clips and 198 test clips). Sets were respectively used to (1) train users on a gamified crowdsourcing platform and (2) compare the concordance of the resulting crowd labels to the concordance of individual experts to reference standards. Crowd opinions were sourced from DiagnosUs (Centaur Labs) iOS app users over 8 days, filtered based on past performance, aggregated using majority rule, and analyzed for label concordance compared with a hold-out test set of expert-labeled clips. The primary outcome was comparing the labeling concordance of collated crowd opinions to trained experts in classifying B-lines on lung ultrasound clips. RESULTS: Our clinical data set included patients with a mean age of 60.0 (SD 19.0) years; 105 (51.7%) patients were female and 114 (56.1%) patients were White. Over the 195 training clips, the expert-consensus label distribution was 114 (58%) no B-lines, 56 (29%) discrete B-lines, and 25 (13%) confluent B-lines. Over the 198 test clips, expert-consensus label distribution was 138 (70%) no B-lines, 36 (18%) discrete B-lines, and 24 (12%) confluent B-lines. In total, 99,238 opinions were collected from 426 unique users. On a test set of 198 clips, the mean labeling concordance of individual experts relative to the reference standard was 85.0% (SE 2.0), compared with 87.9% crowdsourced label concordance (P=.15). When individual experts' opinions were compared with reference standard labels created by majority vote excluding their own opinion, crowd concordance was higher than the mean concordance of individual experts to reference standards (87.4% vs 80.8%, SE 1.6 for expert concordance; P<.001). Clips with discrete B-lines had the most disagreement from both the crowd consensus and individual experts with the expert consensus. Using randomly sampled subsets of crowd opinions, 7 quality-filtered opinions were sufficient to achieve near the maximum crowd concordance. CONCLUSIONS: Crowdsourced labels for B-line classification on lung ultrasound clips via a gamified approach achieved expert-level accuracy. This suggests a strategic role for gamified crowdsourcing in efficiently generating labeled image data sets for training ML systems.