ChromoEnhancer: An Artificial-Intelligence-Based Tool to Enhance Neoplastic Karyograms as an Aid for Effective Analysis

Abstract

Cytogenetics laboratory tests are among the most important procedures for the diagnosis of genetic diseases, especially in the area of hematological malignancies. Manual chromosomal karyotyping methods are time consuming and labor intensive and, hence, expensive. Therefore, to alleviate the process of analysis, several attempts have been made to enhance karyograms. The current chromosomal image enhancement is based on classical image processing. This approach has its limitations, one of which is that it has a mandatory application to all chromosomes, where customized application to each chromosome is ideal. Moreover, each chromosome needs a different level of enhancement, depending on whether a given area is from the chromosome itself or it is just an artifact from staining. The analysis of poor-quality karyograms, which is a difficulty faced often in preparations from cancer samples, is time consuming and might result in missing the abnormality or difficulty in reporting the exact breakpoint within the chromosome. We developed ChromoEnhancer, a novel artificial-intelligence-based method to enhance neoplastic karyogram images. The method is based on Generative Adversarial Networks (GANs) with a data-centric approach. GANs are known for the conversion of one image domain to another. We used GANs to convert poor-quality karyograms into good-quality images. Our method of karyogram enhancement led to robust routine cytogenetic analysis and, therefore, to accurate detection of cryptic chromosomal abnormalities. To evaluate ChromoEnahancer, we randomly assigned a subset of the enhanced images and their corresponding original (unenhanced) images to two independent cytogeneticists to measure the karyogram quality and the elapsed time to complete the analysis, using four rating criteria, each scaled from 1 to 5. Furthermore, we compared the enhanced images with our method to the original ones, using quantitative measures (PSNR and SSIM metrics).

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

“…It is well known that chromosome karyotypes are the most important factors for accurate diagnosis and prognostic strati cation. However, karyotype analysis is extremely time-consuming and labor-intensive, and the results are always restricted by the experience of technicians, which limits the development of cytogenetic techniques 12 .…”

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).…”

Reimagining Healthcare: Unleashing the Power of Artificial Intelligence in Medicine

“…AI-driven diagnostics and imaging are proliferating and finding applications in every possible field of medicine. Further exploration into these algorithms can be assimilated to provide better patient care, considering the ethics and social regulations [ 30 ].…”