Artificial Intelligence-Assisted Diagnostic Cytology and Genomic Testing for Hematologic Disorders

Abstract

Artificial intelligence (AI) is a rapidly evolving field of computer science that involves the development of computational programs that can mimic human intelligence. In particular, machine learning and deep learning models have enabled the identification and grouping of patterns within data, leading to the development of AI systems that have been applied in various areas of hematology, including digital pathology, alpha thalassemia patient screening, cytogenetics, immunophenotyping, and sequencing. These AI-assisted methods have shown promise in improving diagnostic accuracy and efficiency, identifying novel biomarkers, and predicting treatment outcomes. However, limitations such as limited databases, lack of validation and standardization, systematic errors, and bias prevent AI from completely replacing manual diagnosis in hematology. In addition, the processing of large amounts of patient data and personal information by AI poses potential data privacy issues, necessitating the development of regulations to evaluate AI systems and address ethical concerns in clinical AI systems. Nonetheless, with continued research and development, AI has the potential to revolutionize the field of hematology and improve patient outcomes. To fully realize this potential, however, the challenges facing AI in hematology must be addressed and overcome.

Keywords: artificial intelligence; diagnostic cytology; genomic testing; hematologic disorders; machine learning.

Figure 1

The process of classifying abnormal cells using CellaVision’s ANNs. (A) Normal blood cell morphology. The system detects and classifies normal mature WBCs, RBCs, platelets, and immature cells. (B) WBC disorder. The system detects abnormal WBCs, such as increased blast cells, and suggests acute leukemia. (C) RBC disorder. The system characterizes RBC size, color, and shape and suggests iron-deficiency anemia. (D) Platelet clumps. The system detects and classifies platelets, including platelet clumps. The automatically captured images are pre-classified as different cell types by a pre-processing algorithm and then fed into an ANN model for further classification into subtypes such as “abnormal WBCs”. The ANNs are trained on a large dataset of labeled images to learn the features that differentiate each subtype, allowing for accurate and efficient classification of abnormal cells. ANN, artificial neural networks; RBC, red blood cell; WBC, white blood cell.

Figure 2

The workflow of the Morphogo system. Clear cell images are automatically obtained and standardized. Following this, the nucleated cells in the images are located, segmented, and identified. Finally, further cell classification and quantification are auto-analyzed and auto-calculated, respectively, using the CNN model.

Figure 3

ChromoEnhancer model architecture. The images of karyogram, taken from a cytogenetics state-of-the-art system, are input into the cycleGAN model, then the enhanced karyogram images with improved resolution, sharp contrast, and high accuracy are output for further analysis.

Figure 4

The application of AI in genomic testing for hematologic disorders. AI-based computational methods, including ML and DL, have been developed in hematologic disorders of genomic analysis, such as chromosome karyotyping, copy number variation, cell clustering, and epigenetic profile prediction. ANN, artificial neural network; CNN, convolution neural network; CNV, copy number variation; DNA, deoxyribonucleic acid; GAN, generative adversarial network; RF, random forest; SVM, support vector machine; SVR, support vector regression.