Application of the artificial intelligence-rapid whole-genome sequencing diagnostic system in the neonatal/pediatric intensive care unit.
- Author:
Fang LUO
1
;
Hao-Min LI
Author Information
1. Department of Pediatrics, First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, China.
- Publication Type:Journal Article
- MeSH:
Artificial Intelligence;
Child;
Critical Illness;
Humans;
Infant, Newborn;
Intensive Care Units, Neonatal;
Intensive Care Units, Pediatric;
Whole Genome Sequencing
- From:
Chinese Journal of Contemporary Pediatrics
2021;23(5):433-437
- CountryChina
- Language:Chinese
-
Abstract:
Pediatric patients in the neonatal intensive care unit (NICU) and the pediatric intensive care unit (PICU) have a high incidence rate of genetic diseases, and early rapid etiological diagnosis and targeted interventions can help to reduce mortality or improve prognosis. Whole-genome sequencing covers more comprehensive information including point mutation, copy number, and structural and rearrangement variations in the intron region and has become one of the powerful diagnostic tools for genetic diseases. Sequencing data require highly professional judgment and interpretation and are returned for clinical application after several weeks, which cannot meet the need for the diagnosis and treatment of genetic diseases in children. This article introduces the clinical application of rapid whole-genome sequencing in the NICU/PICU and briefly describes related techniques of artificial intelligence-rapid whole-genome sequencing diagnostic system, a rapid high-throughput automated platform for the diagnosis of genetic diseases. The diagnostic system introduces artificial intelligence into the processing of data after whole-genome sequencing and can solve the problems of long time and professional interpretation required for routine genome sequencing and provide a rapid diagnostic regimen for critically ill children suspected of genetic diseases within 24 hours, and therefore, it holds promise for clinical application.
