GestaltMatcher Database - A global reference for the facial phenotypic variability of rare human diseases

Dysmorphologists sometimes encounter challenges in recognizing disorders due to phenotypic variability influenced by factors such as age and ethnicity. Moreover, the performance of Next Generation Phenotyping Tools such as GestaltMatcher is dependent on the diversity of the training set. Therefore, we developed GestaltMatcher Database (GMDB) - a global reference for the phenotypic variability of rare diseases that complies with the FAIR-principles. We curated dysmorphic patient images and metadata from 2,224 publications, transforming GMDB into an online dynamic case report journal. To encourage clinicians worldwide to contribute, each case can receive a Digital Object Identifier (DOI), making it a citable micro-publication. This resulted in a collection of 2,312 unpublished images, partly with longitudinal data. We have compiled a collection of 10,189 frontal images from 7,695 patients representing 683 disorders. The web interface enables gene- and phenotype-centered queries for registered users (https://db.gestaltmatcher.org/). Despite the predominant European ancestry of most patients (59%), our global collaborations have facilitated the inclusion of data from frequently underrepresented ethnicities, with 17% Asian, 4% African, and 6% with other ethnic backgrounds. The analysis has revealed a significant enhancement in GestaltMatcher performance across all ethnic groups, incorporating non-European ethnicities, showcasing a remarkable increase in Top-1-Accuracy by 31.56% and Top-5-Accuracy by 12.64%. Importantly, this improvement was achieved without altering the performance metrics for European patients. GMDB addresses dysmorphology challenges by representing phenotypic variability and including underrepresented groups, enhancing global diagnostic rates and serving as a vital clinician reference database.

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.

Scoping review and classification of deep learning in medical genetics

Deep learning (DL) is applied in many biomedical areas. We performed a scoping review on DL in medical genetics. We first assessed 14,002 articles, of which 133 involved DL in medical genetics. DL in medical genetics increased rapidly during the studied period. In medical genetics, DL has largely been applied to small data sets of affected individuals (mean = 95, median = 29) with genetic conditions (71 different genetic conditions were studied; 24 articles studied multiple conditions). A variety of data types have been used in medical genetics, including radiologic (20%), ophthalmologic (14%), microscopy (8%), and text-based data (4%); the most common data type was patient facial photographs (46%). DL authors and research subjects overrepresent certain geographic areas (United States, Asia, and Europe). Convolutional neural networks (89%) were the most common method. Results were compared with human performance in 31% of studies. In total, 51% of articles provided data access; 16% released source code. To further explore DL in genomics, we conducted an additional analysis, the results of which highlight future opportunities for DL in medical genetics. Finally, we expect DL applications to increase in the future. To aid data curation, we evaluated a DL, random forest, and rule-based classifier at categorizing article abstracts.

Neural Networks for Classification and Image Generation of Aging in Genetic Syndromes

Background: In medical genetics, one application of neural networks is the diagnosis of genetic diseases based on images of patient faces. While these applications have been validated in the literature with primarily pediatric subjects, it is not known whether these applications can accurately diagnose patients across a lifespan. We aimed to extend previous works to determine whether age plays a factor in facial diagnosis as well as to explore other factors that may contribute to the overall diagnostic accuracy.

Methods: To investigate this, we chose two relatively common conditions, Williams syndrome and 22q11.2 deletion syndrome. We built a neural network classifier trained on images of affected and unaffected individuals of different ages and compared classifier accuracy to clinical geneticists. We analyzed the results of saliency maps and the use of generative adversarial networks to boost accuracy.

Results: Our classifier outperformed clinical geneticists at recognizing face images of these two conditions within each of the age groups (the performance varied between the age groups): 1) under 2 years old, 2) 2–9 years old, 3) 10–19 years old, 4) 20–34 years old, and 5) ≥35 years old. The overall accuracy improvement by our classifier over the clinical geneticists was 15.5 and 22.7% for Williams syndrome and 22q11.2 deletion syndrome, respectively. Additionally, comparison of saliency maps revealed that key facial features learned by the neural network differed with respect to age. Finally, joint training real images with multiple different types of fake images created by a generative adversarial network showed up to 3.25% accuracy gain in classification accuracy.

Conclusion: The ability of clinical geneticists to diagnose these conditions is influenced by the age of the patient. Deep learning technologies such as our classifier can more accurately identify patients across the lifespan based on facial features. Saliency maps of computer vision reveal that the syndromic facial feature attributes change with the age of the patient. Modest improvements in the classifier accuracy were observed when joint training was carried out with both real and fake images. Our findings highlight the need for a greater focus on age as a confounder in facial diagnosis.

Neural network classifiers for images of genetic conditions with cutaneous manifestations

Neural networks have shown strong potential in research and in healthcare. Mainly due to the need for large datasets, these applications have focused on common medical conditions, where more data are typically available. Leveraging publicly available data, we trained a neural network classifier on images of rare genetic conditions with skin findings. We used approximately 100 images per condition to classify 6 different genetic conditions. We analyzed both preprocessed images that were cropped to show only the skin lesions as well as more complex images showing features such as the entire body segment, the person, and/or the background. The classifier construction process included attribution methods to visualize which pixels were most important for computer-based classification. Our classifier was significantly more accurate than pediatricians or medical geneticists for both types of images and suggests steps for further research involving clinical scenarios and other applications.

The effect of c-myc on stem cell fate influences skin tumor phenotype

Nonmelanoma skin cancers (NMSCs) consist of a variety of tumor types including basal cell carcinoma, squamous cell carcinoma, a variety of hair follicle tumors, and sebaceous gland tumors. Genetic alterations that alter the fate of multipotent stem cells are believed to influence NMSC phenotype. We previously generated a transgenic mouse line which constitutively expressed c-myc under the control of the K14 promoter (K14.MYC2). These mice exhibited an increase in size and number of sebaceous glands, suggesting that c-myc diverted multipotential stem cells to a sebaceous lineage. Our goal in the current study was to determine if alterations in the commitment of multipotent stem cells to different cell fates would influence tumor phenotype. To this end, we exposed K14.MYC2 mice to a chemical carcinogenesis protocol and discovered that these mice were predisposed to develop sebaceous adenomas. Our data demonstrate that genetic alterations that alter the fate of multipotent stem cells during embryonic development can markedly influence the phenotype of NMSC that develop following exposure to carcinogens.

CCR6 expression distinguishes mouse myeloid and lymphoid dendritic cell subsets: demonstration using a CCR6 EGFP knock-in mouse

The homing properties of subsets of lymphocytes and dendritic cells (DC) are regulated in part by the profile of chemokine receptors expressed. To determine how CCR6 influences cell trafficking, a mutant allele of the mouse CCR6 gene was produced that includes an enhanced green fluorescent protein (EGFP) reporter under the control of the CCR6 promoter. In mice heterozygous for the EGFP/CCR6 knock-in, CCR6 expression was detected on all mature B cells, subpopulations of splenic CD4(+) and CD8(+) T cells, and on some CD11c(+) DC. Most CD11b(+) myeloid DC expressed CCR6, but CD8alpha(+) lymphoid DC were negative for CCR6. Among myeloid DC, the CD4(+) subset was uniformly positive for CCR6 expression and the CD4(-) subset was mostly CCR6 positive. Epidermal Langerhans cells (LC) also expressed CCR6, but at lower levels than splenic myeloid DC. Culture of bone marrow precursors from the knock-in mice with GM-CSF for 4 to 6 days led to the appearance of a subset of CD11c(+) DC expressing CCR6. The differences in CCR6 expression among the major DC subsets indicate that CCR6 and its chemokine ligand MIP-3alpha participate in determining the positioning of DC subsets in epithelial and lymphoid tissues.

NAD(P)H:quinone oxidoreductase 1 deficiency and increased susceptibility to 7,12-dimethylbenz[a]-anthracene-induced carcinogenesis in mouse skin

BACKGROUND

The phase II enzyme NAD(P)H :quinone oxidoreductase 1 (NQO1) catalyzes quinone detoxification, protecting cells from redox cycling, oxidative stress, mutagenicity, and cytotoxicity induced by quinones and its precursors. We have used NQO1(-/-) C57BL/6 mice to show that NQO1 protects them from skin cancer induced by the polycyclic aromatic hydrocarbon benzo[a]pyrene. Herein, we used NQO1(-/-) mice to investigate whether NQO1 also protects them against 7,12-dimethylbenz[a]anthracene (DMBA), where methyl substituents diminish primary quinone formation.

METHODS

Dorsal skin of NQO1(-/-) or wild-type C57BL/6 mice was shaved. When tested as a complete carcinogen, DMBA (500 or 750 microg in 100 microL of acetone) alone was applied to the shaved area. When tested as a tumor initiator, DMBA (200 or 400 nmol in 100 microL of acetone) was applied to the shaved area; 1 week later, twice-weekly applications of phorbol 12-myristate 13-acetate (PMA)-10 microg dissolved in 200 microL of acetone-to the same area began and were continued for 20 weeks. Tumor development was monitored in all mice (12-15 per group). All statistical tests were two-sided.

RESULTS

When DMBA (750 microg) was tested as a complete carcinogen, about 50% of the DMBA-treated NQO1(-/-) mice but no DMBA-treated wild-type mouse developed skin tumors. When DMBA (both concentrations) was used as a tumor initiator, NQO1(-/-) mice developed larger tumors at a greater frequency than their wild-type littermates. Twenty-three weeks after the first PMA treatment in the tumor initiator test, all 30 NQO1(-/-) mice given 400 nmol of DMBA had developed skin tumors, compared with 33% (10 of 30) of treated wild-type mice (P<.001).

CONCLUSIONS

NQO1(-/-) mice are more susceptible to DMBA-induced skin cancer than are their wild-type littermates, suggesting that NQO1 may protect cells from DMBA carcinogenesis.

Deregulated expression of c-Myc depletes epidermal stem cells

The beta-catenin/TCF signaling pathway is essential for the maintenance of epithelial stem cells in the small intestine. c-Myc a downstream target of beta-catenin/TCF (ref. 2), can induce differentiation of epidermal stem cells in vitro. To determine the role of c-Myc in epidermal stem cells in vivo, we have targeted expression of human MYC2 to the hair follicles and the basal layer of mouse epidermis using a keratin 14 vector (K14.MYC2). Adult K14.MYC2 mice gradually lose their hair and develop spontaneous ulcerated lesions due to a severe impairment in wound healing; their keratinocytes show impaired migration in response to wounding. The expression of beta1 integrin, which is preferentially expressed in epidermal stem cells is unusually low in the epidermis of K14.MYC2 mice. Label-retaining analysis to identify epidermal stem cells reveals a 75% reduction in the number of stem cells in 3-month-old K14.MYC2 mice, compared with wildtype mice. We conclude that deregulated expression of c-Myc in stem cells reduces beta1 integrin expression, which is essential to both keratinocyte migration and stem cell maintenance.

NAD(P)H:quinone oxidoreductase 1 deficiency increases susceptibility to benzo(a)pyrene-induced mouse skin carcinogenesis

NAD(P)H:quinone oxidoreductase 1 (NQO1) is a flavoprotein that catalyzes the metabolic detoxification of quinones and their derivatives. This protects cells against quinone-induced oxidative stress, cytotoxicity, and mutagenicity. C57BL6 NQO1-/- mice, deficient in NQO1 RNA and protein, were generated in our laboratory. To investigate the role of NQO1 in chemical carcinogenesis, the dorsal skin of NQO1-deficient (NQO1-/-) and wild-type (NQO1+/+) mice were treated with a single dose of benzo(a)pyrene, followed by twice weekly applications of phorbol-12-myristate-13-acetate. The NQO1-/- mice showed a much higher frequency of skin tumor development when compared with their wild-type littermates. Interestingly, the male NQO1-/- mice were slower to develop skin tumors than their NQO1-/- female littermates. Histological analysis of the NQO1-/- tumors showed proliferative activity. These results demonstrate that NQO1 acts as an endogenous factor in protection against benzo(a)pyrene carcinogenicity.

Targeted expression of c-Myc in the epidermis alters normal proliferation, differentiation and UV-B induced apoptosis

c-Myc overexpression has been associated with several types of human cancers. To study the role of c-myc in epidermal differentiation and carcinogenesis, a transgenic mouse model was created to overexpress c-Myc in the epidermis. Human c-myc 2 cDNA was subcloned into a 6.5 kb mouse loricrin expression vector, ML.myc2. This loricrin promoter primarily directs expression in the epidermis in both proliferating and differentiated keratinocytes. On day 4, ML.myc2 transgenic pups develop a hyperkeratotic phenotype, which progressively worsens until day 7. Upon histological analysis, both hyperplasia and hyperkeratosis were evident. Bromodeoxyuridine (BrdU) incorporation revealed that transgenic mice had a threefold increase in the number of proliferating cells as compared with a normal littermate. Proliferative cells in the ML.myc2 epidermis were also found to be suprabasal, suggesting an inhibition of terminal differentiation in keratinocytes. Inhibition of terminal differentiation by c-Myc overexpression was further suggested by aberrant expression of differentiation markers, keratin 1, keratin 6, loricrin, and filaggrin in ML.myc2 transgenic mice. Interestingly, ML.myc2 keratinocytes exhibit a reduced sensitivity to UV-B induced apoptosis, in vivo. In vitro studies reveal the reduced sensitivity of ML.myc2 keratinocytes to UV-B irradiation is growth factor dependent. These findings provide evidence that overexpression of c-Myc in the epidermis induces proliferation, inhibits terminal differentiation and decreases the sensitivity of keratinocytes to UV-B induced apoptosis.