Can artificial intelligence save medical genetics?

Solomon, Benjamin D.

doi:10.1002/ajmg.a.62538

Cited by 11 publications

(7 citation statements)

References 35 publications

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“…Despite the rarity (and therefore lack of data availability) of many genetic conditions, neural networks have high potential in this area, due to both the ability to accurately categorize patients based on underlying molecular causes and the lack of trained experts throughout the world such that these tools could be highly valuable ( Solomon, 2021 ). This area provides a ripe opportunity for patients, clinicians, researchers, and others to collaborate for the good of the impacted community.…”

Section: Discussionmentioning

confidence: 99%

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

Duong

Tekendo‐Ngongang

et al. 2022

Front. Genet.

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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.

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Section: Discussionmentioning

confidence: 99%

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

Duong

Tekendo‐Ngongang

et al. 2022

Front. Genet.

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“…However, it is worth emphasizing that AI at present still has limited scope of use in clinical laboratory genomics due to continuously evolving genomic databases, a steady pace of discoveries of gene‐disease relationships, and limited deployment for providing education and information (Luca et al, 2023). Expert clinicians and laboratory geneticists will therefore need to carefully oversee the emerging use of AI in genomic medicine and help other professionals incorporate genomic information appropriately for clinical management of their patients (Solomon, 2022). It is also important to note the potential lost opportunity costs of delaying the use of AI, since that would hinder diagnosis and treatment for millions of individuals who need conclusive genetic testing results, even as the data that could provide answers are available and waiting to be analyzed.…”

Section: Discussionmentioning

confidence: 99%

Applications of artificial intelligence in clinical laboratory genomics

Aradhya

Facio

Metz

et al. 2023

American J of Med Genetics Pt C

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The transition from analog to digital technologies in clinical laboratory genomics is ushering in an era of “big data” in ways that will exceed human capacity to rapidly and reproducibly analyze those data using conventional approaches. Accurately evaluating complex molecular data to facilitate timely diagnosis and management of genomic disorders will require supportive artificial intelligence methods. These are already being introduced into clinical laboratory genomics to identify variants in DNA sequencing data, predict the effects of DNA variants on protein structure and function to inform clinical interpretation of pathogenicity, link phenotype ontologies to genetic variants identified through exome or genome sequencing to help clinicians reach diagnostic answers faster, correlate genomic data with tumor staging and treatment approaches, utilize natural language processing to identify critical published medical literature during analysis of genomic data, and use interactive chatbots to identify individuals who qualify for genetic testing or to provide pre‐test and post‐test education. With careful and ethical development and validation of artificial intelligence for clinical laboratory genomics, these advances are expected to significantly enhance the abilities of geneticists to translate complex data into clearly synthesized information for clinicians to use in managing the care of their patients at scale.

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“…As with broad sequencing approaches, these tools may further democratize clinical genetics and genetic testing by putting knowledge at the fingertips of many clinicians. In many ways, genetic testing is ideally situated for support via artificial intelligence, as the field involves so many rare and esoteric conditions that any one clinician (even a geneticist) cannot possibly be expert in every disorder they will encounter [43][44][45]. However, robust implementation studies are needed to carefully examine the efficacy of these tools.…”

Section: The Rise Of Artificial Intelligencementioning

confidence: 99%

The future of commercial genetic testing

Solomon

2023

Current Opinion in Pediatrics

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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.

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Can artificial intelligence save medical genetics?

Abstract: Genetics and genomics is one of the most exciting and quickly evolving fields in medicine. Relatively recent genomic discoveries have

Cited by 11 publications

References 35 publications

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

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

Applications of artificial intelligence in clinical laboratory genomics

The future of commercial genetic testing

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