Deep learning based phenotyping of medical images improves power for gene discovery of complex disease
Rattachement africain : us. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Electronic health records are often incomplete, reducing the power of genetic association studies. For some diseases, such as knee osteoarthritis where the routine course of diagnosis involves an X-ray, image-based phenotyping offers an alternate and unbiased way to ascertain disease cases. We investigated this by training a deep-learning model to ascertain knee osteoarthritis cases from knee DXA scans that achieved clinician-level performance. Using our model, we identified 1931 (178%) more cases than currently diagnosed in the health record. Individuals diagnosed as cases by our model had higher rates of self-reported knee pain, for longer durations and with increased severity compared to control individuals. We trained another deep-learning model to measure the knee joint space width, a quantitative phenotype linked to knee osteoarthritis severity. In performing genetic association analysis, we found that use of a quantitative measure improved the number of genome-wide significant loci we discovered by an order of magnitude compared with our binary model of cases and controls despite the two phenotypes being highly genetically correlated. In addition we discovered associations between our quantitative measure of knee osteoarthritis and increased risk of adult fractures- a leading cause of injury-related death in older individuals-, illustrating the capability of image-based phenotyping to reveal epidemiological associations not captured in the electronic health record. For diseases with radiographic diagnosis, our results demonstrate the potential for using deep learning to phenotype at biobank scale, improving power for both genetic and epidemiological association analysis.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Deep learning based phenotyping of medical images improves power for gene discovery of complex disease
- Date Crossref
- 21/08/2023
- Éditeur
- Springer Science and Business Media LLC
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
-
The University of Texas at Austin Department of Integrative Biology pays non établi dans la noticeUniversité ou école supérieure
-
Dell Children's Medical Center of Central Texas pays non établi dans la noticeÉtablissement de santé
-
Columbia University Irving Medical Center pays non établi dans la noticeÉtablissement de santé
-
New York Genome Center pays non établi dans la noticeStructure de recherche
-
Mortimer B. Zuckerman Mind Brain Behavior Institute pays non établi dans la noticeStructure de recherche
-
Department of Surgery and Perioperative Care pays non établi dans la noticeInstitution
Department of Integrative Biology — The University of Texas at Austin, Dell Children's Medical Center of Central Texas et Columbia University Irving Medical Center, avec 3 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.