An Open Dataset of Annotated Metaphase Cell Images for Chromosome Identification

Abstract

Chromosomes are a principal target of clinical cytogenetic studies. While chromosomal analysis is an integral part of prenatal care, the conventional manual identification of chromosomes in images is time-consuming and costly. This study developed a chromosome detector that uses deep learning and that achieved an accuracy of 98.88% in chromosomal identification. Specifically, we compiled and made available a large and publicly accessible database containing chromosome images and annotations for training chromosome detectors. The database contains five thousand 24 chromosome class annotations and 2,000 single chromosome annotations. This database also contains examples of chromosome variations. Our database provides a reference for researchers in this field and may help expedite the development of clinical applications.

Applications of artificial intelligence in clinical laboratory genomics

“…Because AI methods have been developed for visual pattern recognition in X‐rays, computed tomography scans, and stained tissue slices, one might predict that these methods could also be applied to the analysis of chromosomal karyotypes for constitutional rearrangements or to the analysis of tumor tissue for chromosomal rearrangements. To date, little to no AI appears to be used to routinely analyze chromosomal karyotypes for constitutional rearrangements (Tseng et al, 2023), although various efforts have been used to decipher chromosomal rearrangements in cancer specimens, such as from karyotyped hematologic malignancies (Bokhari et al, 2022; Cox et al, 2022; Vajen et al, 2022; Walter et al, 2021). As genomic analysis increasingly shifts toward molecular approaches, even for chromosomal disorders, AI methods are being developed to identify chromosomal deletions, duplications, and other types of rearrangements from NGS data directly (Lin et al, 2022; Popic et al, 2023).…”

Manually-established abnormal karyotype dataset based on normal chromosomes effectively train artificial intelligence model for better cytogenetic abnormalities prediction

“…However, similar to the challenges of AI implementation in other medicine-related applications 5 , the lack of representative abnormal karyotype images of hematological malignancies hinder the data analysis and further accurate diagnosis in clinic. Even there are databases existing, they fail to provide a comprehensive dataset covering almost all the abnormal karyogram images 14 . To overcome these challenges, we aim to establish a dataset based on AI, providing the proof-of-concept of the utilization of 'manually-built' karyogram dataset to assist and improve chromosome karyotyping in leukemia, which can effectively facilitate the progress of AI in hematological diseases by producing more high-quality data.…”