Neural network classifiers for images of genetic conditions with cutaneous manifestations

Duong, Dat; Waikel, Rebekah L.; Hu, Ping; Tekendo‐Ngongang, Cedrik; Solomon, Benjamin D.

doi:10.1016/j.xhgg.2021.100053

Cited by 11 publications

(20 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To provide examples of ways to bolster the standard diagnostic process as well as to build on the impressive findings of previous, related studies, ( Gurovich et al, 2019 ; Duong et al, 2021b ; Hsieh et al, 2021 ; Porras et al, 2021 ), we analyzed and provided a larger dataset of WS and 22q individuals (although these other studies contained a much larger total number of individuals having multiple other diseases). We also compared results for different ages of individuals.…”

Section: Discussionmentioning

confidence: 99%

“…First, we collected a dataset of publicly available WS and 22q images, which may be larger than others previously studied ( Gurovich et al, 2019 ; Liu et al, 2021 ; Porras et al, 2021 ). Second, beyond the dataset, our approaches (and available code) may be used as subcomponents of other algorithms ( Duong et al, 2021b ). We trained a neural network classifier on our dataset (N = 1,894), which is still small compared to many other deep learning datasets, thus pushing the capability of the neural network model.…”

Section: Discussionmentioning

confidence: 99%

“…Following previous methods ( Tschandl et al, 2018 ; Tschandl et al, 2019 ; Duong et al, 2021b ), we estimated that 30 participants would provide a statistical power of 95% to detect a 10% difference. The participants were recruited via email.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

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

Duong

Tekendo‐Ngongang

et al. 2022

Front. Genet.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

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

Duong

Tekendo‐Ngongang

et al. 2022

Front. Genet.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Studies have shown that these types of tools may be effective in many other contexts related to clinical genetics, such as assessing birthmarks, analysing ophthalmologic imaging, and evaluating EEGs or radiologic studies, to name just a few [34 ▪ ,40,41]. In the future, there will be likely tools that can assess more and more data types, including photographic and radiologic images, results of laboratory-based testing, clinical notes and other medical records [42].…”

Section: Reviewmentioning

confidence: 99%

The future of commercial genetic testing

Solomon

2023

Current Opinion in Pediatrics

View full text Add to dashboard Cite

Purpose of review There are thousands of different clinical genetic tests currently available. Genetic testing and its applications continue to change rapidly for multiple reasons. These reasons include technological advances, accruing evidence about the impact and effects of testing, and many complex financial and regulatory factors. Recent findings This article considers a number of key issues and axes related to the current and future state of clinical genetic testing, including targeted versus broad testing, simple/Mendelian versus polygenic and multifactorial testing models, genetic testing for individuals with high suspicion of genetic conditions versus ascertainment through population screening, the rise of artificial intelligence in multiple aspects of the genetic testing process, and how developments such as rapid genetic testing and the growing availability of new therapies for genetic conditions may affect the field. Summary Genetic testing is expanding and evolving, including into new clinical applications. Developments in the field of genetics will likely result in genetic testing becoming increasingly in the purview of a very broad range of clinicians, including general paediatricians as well as paediatric subspecialists.

show abstract

“…AI is not new, but, for a variety of reasons, including better computers, larger, shared datasets, and proven algorithms, AI is garnering increasing attention. The many versions and iterations of AI have already started to be coupled with medical genetics, including in ways that offer glimpses into great potential (a few of many example citations are given) (Brasil et al, 2021; Brasil et al, 2019; Clark et al, 2019; Dias & Torkamani, 2019; Dowsett et al, 2019; Duong et al, 2021; Gurovich et al, 2019; Jumper et al, 2021; Kruszka et al, 2020; Kruszka et al, 2017; Kruszka et al, 2018; Mak et al, 2021; Porras et al, 2021; Schaefer et al, 2020; Shchelochkov et al, 2021; Tekendo‐Ngongang et al, 2020). For example, the use of AI can help develop a differential diagnosis for a patient, can support the analysis of genomic data for that patient, and may help researchers studying possible treatments to select compounds of interest for further study.…”

mentioning

confidence: 99%