Recognition of Genetic Conditions After Learning With Images Created Using Generative Artificial Intelligence.

Importance: The lack of standardized genetics training in pediatrics residencies, along with a shortage of medical geneticists, necessitates innovative educational approaches. Objective: To compare pediatric resident recognition of Kabuki syndrome (KS) and Noonan syndrome (NS) after 1 of 4 educational interventions, including generative artificial intelligence (AI) methods. Design, Setting, and Participants: This comparative effectiveness study used generative AI to create images of children with KS and NS. From October 1, 2022, to February 28, 2023, US pediatric residents were provided images through a web-based survey to assess whether these images helped them recognize genetic conditions. Interventions: Participants categorized 20 images after exposure to 1 of 4 educational interventions (text-only descriptions, real images, and 2 types of images created by generative AI). Main Outcomes and Measures: Associations between educational interventions with accuracy and self-reported confidence. Results: Of 2515 contacted pediatric residents, 106 and 102 completed the KS and NS surveys, respectively. For KS, the sensitivity of text description was 48.5% (128 of 264), which was not significantly different from random guessing (odds ratio [OR], 0.94; 95% CI, 0.69-1.29; P = .71). Sensitivity was thus compared for real images vs random guessing (60.3% [188 of 312]; OR, 1.52; 95% CI, 1.15-2.00; P = .003) and 2 types of generative AI images vs random guessing (57.0% [212 of 372]; OR, 1.32; 95% CI, 1.04-1.69; P = .02 and 59.6% [193 of 324]; OR, 1.47; 95% CI, 1.12-1.94; P = .006) (denominators differ according to survey responses). The sensitivity of the NS text-only description was 65.3% (196 of 300). Compared with text-only, the sensitivity of the real images was 74.3% (205 of 276; OR, 1.53; 95% CI, 1.08-2.18; P = .02), and the sensitivity of the 2 types of images created by generative AI was 68.0% (204 of 300; OR, 1.13; 95% CI, 0.77-1.66; P = .54) and 71.0% (247 of 328; OR, 1.30; 95% CI, 0.92-1.83; P = .14). For specificity, no intervention was statistically different from text only. After the interventions, the number of participants who reported being unsure about important diagnostic facial features decreased from 56 (52.8%) to 5 (7.6%) for KS (P < .001) and 25 (24.5%) to 4 (4.7%) for NS (P < .001). There was a significant association between confidence level and sensitivity for real and generated images. Conclusions and Relevance: In this study, real and generated images helped participants recognize KS and NS; real images appeared most helpful. Generated images were noninferior to real images and could serve an adjunctive role, particularly for rare conditions.

Comparison of clinical geneticist and computer visual attention in assessing genetic conditions.

Artificial intelligence (AI) for facial diagnostics is increasingly used in the genetics clinic to evaluate patients with potential genetic conditions. Current approaches focus on one type of AI called Deep Learning (DL). While DL- based facial diagnostic platforms have a high accuracy rate for many conditions, less is understood about how this technology assesses and classifies (categorizes) images, and how this compares to humans. To compare human and computer attention, we performed eye-tracking analyses of geneticist clinicians (n = 22) and non-clinicians (n = 22) who viewed images of people with 10 different genetic conditions, as well as images of unaffected individuals. We calculated the Intersection-over-Union (IoU) and Kullback-Leibler divergence (KL) to compare the visual attentions of the two participant groups, and then the clinician group against the saliency maps of our deep learning classifier. We found that human visual attention differs greatly from DL model's saliency results. Averaging over all the test images, IoU and KL metric for the successful (accurate) clinician visual attentions versus the saliency maps were 0.15 and 11.15, respectively. Individuals also tend to have a specific pattern of image inspection, and clinicians demonstrate different visual attention patterns than non-clinicians (IoU and KL of clinicians versus non-clinicians were 0.47 and 2.73, respectively). This study shows that humans (at different levels of expertise) and a computer vision model examine images differently. Understanding these differences can improve the design and use of AI tools, and lead to more meaningful interactions between clinicians and AI technologies.

Generative Methods for Pediatric Genetics Education.

Artificial intelligence (AI) is used in an increasing number of areas, with recent interest in generative AI, such as using ChatGPT to generate programming code or DALL-E to make illustrations. We describe the use of generative AI in medical education. Specifically, we sought to determine whether generative AI could help train pediatric residents to better recognize genetic conditions. From publicly available images of individuals with genetic conditions, we used generative AI methods to create new images, which were checked for accuracy with an external classifier. We selected two conditions for study, Kabuki (KS) and Noonan (NS) syndromes, which are clinically important conditions that pediatricians may encounter. In this study, pediatric residents completed 208 surveys, where they each classified 20 images following exposure to one of 4 possible educational interventions, including with and without generative AI methods. Overall, we find that generative images perform similarly but appear to be slightly less helpful than real images. Most participants reported that images were useful, although real images were felt to be more helpful. We conclude that generative AI images may serve as an adjunctive educational tool, particularly for less familiar conditions, such as KS.

Human and computer attention in assessing genetic conditions.

Deep learning (DL) and other types of artificial intelligence (AI) are increasingly used in many biomedical areas, including genetics. One frequent use in medical genetics involves evaluating images of people with potential genetic conditions to help with diagnosis. A central question involves better understanding how AI classifiers assess images compared to humans. To explore this, we performed eye-tracking analyses of geneticist clinicians and non-clinicians. We compared results to DL-based saliency maps. We found that human visual attention when assessing images differs greatly from the parts of images weighted by the DL model. Further, individuals tend to have a specific pattern of image inspection, and clinicians demonstrate different visual attention patterns than non-clinicians.

Uptake of permanent contraception among women in sub-Saharan Africa: a literature review of barriers and facilitators.

OBJECTIVE: Uptake of permanent contraception among women remains low in sub-Saharan Africa compared to other regions. We aimed to synthesize available evidence on barriers to, and facilitators of permanent contraception with regards to tubal ligation among women in sub-Saharan Africa. STUDY DESIGN: We reviewed literature on tubal ligation among African women published between January 1, 2000 and October 30, 2017. We searched PubMed, Global health, EMBASE, Web of science, and Google scholar for quantitative, qualitative, and mixed methods studies which reported on barriers and/or facilitators to uptake of tubal ligation in sub-Saharan Africa. Finally, we conducted a narrative synthesis and categorized our findings using a framework based on the social ecological model. RESULTS: We included 48 articles in the review. Identified barriers to tubal ligation among women included individual-level (myths and misconceptions, fear of surgery, irreversibility of procedure, religious beliefs), interpersonal-level (male partner disapproval), and organizational-level (lack of healthcare worker expertise and equipment) factors. Facilitating factors included achievement of desired family size and perceived effectiveness (individual-level), supportive male partners and knowing other women with permanent contraception experience (interpersonal-level), and finally, subsidized cost of the procedure and task-sharing with lower cadre healthcare workers (organizational-level). CONCLUSIONS: Barriers to, and facilitators of permanent contraception among women in sub-Saharan Africa are multilevel in nature. Strategies countering these barriers should be prioritized, as effective contraception can promote women's health and economic development in sub-Saharan Africa. In addition to these strategies, more quantitative research is needed to further understand patient-level factors associated with uptake of permanent contraception among women.

Variation in airborne fungal spore concentrations among five monitoring locations in a desert urban environment.

Fungal spores are biological particles that are ubiquitous in the outdoor air. Spores of several very common fungal species are known allergens, with the potential to cause respiratory illnesses by exacerbating asthma and allergic rhinitis. The National Allergy Bureau typically has one monitoring station established per city to determine fungal spore counts for an entire metropolitan area. However, variations in fungal spore concentrations could occur among different locations. The objective of this study was to measure and compare airborne fungal spore concentrations in five locations in Las Vegas for the year 2015 to determine if there are differences among microenvironments in the city. Twenty-four-hour or 7-day air samples were collected from five sites across the Las Vegas Valley. Samples were analyzed with a light microscope for fungal spores and counts were converted to concentrations of spores per volume of air. Mixed-model methods were used to evaluate mean differences. Results showed that smuts (basidiomycetes) were the dominant spore type for all five sites during the spring season. Cladosporium species were responsible for the second most dominant spore type with the highest concentrations occurring during the summer and fall months. Results obtained from the five stations established in Las Vegas show that there are important variations among the sites regarding fungal spore concentrations. The data suggest that more sites and additional monitoring of outdoor allergens are needed to provide information necessary to inform the community of outdoor air quality conditions and their potential effects on public health. This study presents new outdoor fungal spore data for the southwest region of the USA, focused in the Las Vegas Valley.

Variation in airborne pollen concentrations among five monitoring locations in a desert urban environment.

The urbanization of the Las Vegas Valley has transformed this part of the Mohave Desert into a green oasis by introducing many non-native plant species, some of which are allergenic. Typically, one monitoring station is established per city to obtain pollen counts for an entire metropolitan area. However, variations in pollen concentrations could occur among different microenvironments. The objective of this study is to measure and compare pollen concentrations in five locations in Las Vegas to determine if there are significant differences between microenvironments within the city. Air samples were collected from five sites across the Las Vegas Valley over a 1-year period. Prepared slides were analyzed with a light microscope for pollen grains and converted into airborne pollen concentrations. Mixed model methods were used to determine mean differences. Tree pollen was the greatest contributor to the annual average airborne pollen concentrations (130 grains/m3) compared to weeds (6 grains/m3) and grass (3 grains/m3). The highest peak occurred in March 2016 (9589 total grains/m3). There were several differences among sites with respect to concentrations of individual tree species and for total weed and grass concentrations. We observed significant variations in concentration and composition among the five pollen collection stations that were established across the Las Vegas Valley. This study presented new outdoor pollen data for the southwest region of the USA, focused in Las Vegas. The results indicate that more sites and comprehensive monitoring of outdoor allergens are needed to provide accurate information to the community about outdoor air quality conditions.