AI-MARRVEL - A Knowledge-Driven AI System for Diagnosing Mendelian Disorders

Authors

Dongxue Mao
Chaozhong Liu
Linhua Wang
Rami Ai-Ouran
Cole Deisseroth
Sasidhar Pasupuleti
Seon Young Kim
Lucian Li
Jill A Rosenfeld
Linyan Meng
Lindsay C Burrage
Michael F Wangler
Shinya Yamamoto
Undiagnosed Diseases Network
Michael Santana
Victor Perez
Priyank Shukla
Christine M Eng
Brendan Lee
Bo Yuan
Fan Xia
Hugo J Bellen
Pengfei Liu
Zhandong Liu

Publication Date

5-1-2024

Journal

AI in Medicine

DOI

10.1056/aioa2300009

PMID

38962029

PMCID

PMC11221788

PubMedCentral® Posted Date

7-3-2024

PubMedCentral® Full Text Version

Author MSS

Published Open-Access

yes

Abstract

BACKGROUND: Diagnosing genetic disorders requires extensive manual curation and interpretation of candidate variants, a labor-intensive task even for trained geneticists. Although artificial intelligence (AI) shows promise in aiding these diagnoses, existing AI tools have only achieved moderate success for primary diagnosis.

METHODS: AI-MARRVEL (AIM) uses a random-forest machine-learning classifier trained on over 3.5 million variants from thousands of diagnosed cases. AIM additionally incorporates expert-engineered features into training to recapitulate the intricate decision-making processes in molecular diagnosis. The online version of AIM is available at https://ai.marrvel.org. To evaluate AIM, we benchmarked it with diagnosed patients from three independent cohorts.

RESULTS: AIM improved the rate of accurate genetic diagnosis, doubling the number of solved cases as compared with benchmarked methods, across three distinct real-world cohorts. To better identify diagnosable cases from the unsolved pools accumulated over time, we designed a confidence metric on which AIM achieved a precision rate of 98% and identified 57% of diagnosable cases out of a collection of 871 cases. Furthermore, AIM's performance improved after being fine-tuned for targeted settings including recessive disorders and trio analysis. Finally, AIM demonstrated potential for novel disease gene discovery by correctly predicting two newly reported disease genes from the Undiagnosed Diseases Network.

CONCLUSIONS: AIM achieved superior accuracy compared with existing methods for genetic diagnosis. We anticipate that this tool may aid in primary diagnosis, reanalysis of unsolved cases, and the discovery of novel disease genes. (Funded by the NIH Common Fund and others.).

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