Abstract

There is a broad spectrum of hematological diseases, and their origins can be attributed to a variety of factors, including genetic abnormalities such as leukemia, sickle cell anemia (SCA), and thalassemia, as well as conditions associated with the lack of certain blood components, such as iron deficiency anemia (IDA). Testing and analyzing for hematological disorders is intensive in terms of time, effort, and labor. Additionally, there is a higher chance of human error and variance during the manual examination and analysis of the test samples, depending on the expertise, skills, and experience of the examiner. Recent developments in artificial intelligence (AI), such as Machine Learning (ML) and Deep Learning (DL) algorithms—such as Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM)—have demonstrated the considerable contribution they could make to more rapid and accurate disease diagnosis, detection, and classification. An increasing number of hematological diseases are being diagnosed using AI techniques, which combine tabular and image data to eliminate human error, generate more precise results, and decrease the time required for diagnosis. This review discusses several widely utilized AI disease evaluation algorithms and their applicability to hematological disorders. Additionally, we highlight key challenges such as the lack of accessible clinical data, which inhibits the implementation of AI in the field of medicine.

Introduction

An accurate early diagnosis is a crucial factor that plays a vital role in determining the treatment and prognosis for any disease, particularly in hematological disorders. In addition to the complete blood count (CBC), bone marrow (BM) and peripheral blood smears (PBS) examined under a microscope are two of the main tests used to diagnose a hematological disorder. However, the results and analysis of these diagnostic tests can vary based on the expertise and interpretation of the examiner. Hence, there is a high probability of significant human error, which could prove detrimental to a patient’s treatment plan and prognosis1.

Moreover, the time-intensive and labor-intensive nature of these tests adds to the testing costs, making them less accessible to a large portion of the population. For example, iron deficiency anemia (IDA) and thalassemia exhibit similar symptoms; therefore, when a misdiagnosis occurs, a thalassemic patient could be prescribed unnecessary iron supplements while the thalassemia could go untreated2. In the case of leukemia, a delayed diagnosis may result in rapid disease progression and possible death of the patient. Therefore, a correct diagnosis made at an early stage of the development of a hematological disorder is crucial as it enables medical professionals to develop suitable treatment plans. Automated approaches, such as artificial intelligence (AI), have been proposed as more accurate, time-efficient, and less labor-intensive means for disease detection1, 2, 3.

The early foundations of AI were laid in the 1970s with the introduction of rule-based systems like MYCIN, which were utilized for diagnosing bacterial infections and prescribing appropriate antibiotics4. Since the inception of AI in healthcare, it has advanced significantly and shown high success rates when utilized in numerous intelligent applications to solve several data-related issues5. A growing number of AI applications, and its subcategory Machine Learning (ML), are being developed in the field of medicine to assist clinicians in analyzing test results for rapid disease diagnosis and to devise better therapeutic interventions6, 7. This development was catalyzed by the abundance of clinical data combined with powerful AI tools, which have allowed for a wider range of applications in this area8.

The purpose of this review is to highlight the development of AI technology and its application in hematological diseases, as compared to other diseases like brain tumors where the field has been explored in detail. Additionally, with the recent exponential advancements in this technology, there are opportunities to implement a myriad of AI technologies. For example, analyzing blood cell morphology can be quite complex and variable, but it is becoming consistently more accurate and faster thanks to numerous AI algorithms and the availability of big data sets. Therefore, in this review, we discuss various models and examples of AI that are being applied in the field of medicine, particularly in hematological disorders. Additionally, the challenges faced in implementing AI in disease detection and classification are also discussed in this study.

Adjustments include spelling changes (e.g., "hematological" instead of "haematological") for consistency and readability, the use of semicolons to connect related clauses, and slight wording changes for clarity and flow.

Methods

This review screened three databases (PubMed, Google Scholar, and ScienceDirect) for relevant literature related to the term(s). Studies that utilized AI in the field of hematology and hematological cancers were searched using keywords like artificial intelligence, clinical translation, deep learning, hematological disorders, machine learning, diagnostics, healthcare technology, leukemia, anemia, and other terms related to artificial intelligence and its applications in the medical domain.

× Figure 1 . Subcategories of artificial intelligence . Figure 1 . Subcategories of artificial intelligence . Machine Learning (ML)

ML is a part of AI that analyzes data samples to create models using mathematical and statistical approaches, allowing machines to learn without programming (Figure 1 ). When a computer program is given a set of tasks, it is said to have learned from its experience if its quantifiable performance on those tasks improves over time as it completes them9. ML was first used in a checkers game by Arthur Samuel in 1959, who utilized annotated moves from experienced players10. Since then, the algorithm has been validated and applied to a wide range of different applications1, 10, 11, 12. Currently, with the rapid increase in computational capabilities and data availability, training data-driven ML models has become more feasible, resulting in more time and cost-effective ML applications13.

ML is classified mainly into two types based on the type of input: supervised and unsupervised. The supervised learning ML algorithm makes use of “labeled” input and output training datasets to learn from in order to classify unlabeled datasets and predict the output accurately once the supervised ML algorithm is refined14. On the other hand, unsupervised ML examines unlabeled datasets and reveals unknown patterns using various clustering and/or association algorithms14. Supervised ML is grouped into two subtypes based on the type of output: classification and regression15. Classification is used to classify datasets into specific segments based on chosen parameters, while regression uses statistical methods to find a correlation between dependent and independent variables, which helps to make the cause-and-effect prediction. The following sections provide a bird’s eye view of the frequently used supervised ML algorithms utilized in the medical field due to the homogeneous and consistent nature of clinical tests along with the availability of sufficient data to form training datasets.

Decision Tree (DT)

The modern DT algorithm was developed in 1986 by John Quinlan, who developed features from a family and its members addressing the same task16. According to Suthaharan (2016), this supervised learning method could be interpreted as a “hierarchical domain division technique” due to its role in splitting the data domain, also known as a “node.” Ideally, an optimized DT model would split the dataset into subsets leading to maximum information gain, hence leading to better classification17. To support this hierarchical structure, DT comprises leaf nodes, branches, and a root node. Every internal node contains an attribute test, followed by branching of the test result, and finally, leaf nodes that indicate class labels18. In a small dataset, DT is simple to interpret and the accuracy of DT is comparable to other classification algorithms; however, this could lead to overfit classification (Table 1).

Table 1.

The advantages and disadvantages between AI algorithms used in detection of diseases

Algorithm Advantages Disadvantages Decision tree (DT) 19 •Small-sized trees are simple to interpret. Over fit with classification in some dataset Random forest (RF) 19 •Managed unequal data sets with missing variables. Over fit for some datasets with noisy classification task. Support vector machine (SVM) 19 •Works well in high dimensional space. •Requires a long time for training with a huge data set. K-nearest neighbor (KNN) 20 •Implementation is simple and clear. •Sensitivity to irrelevant or noisy data. Naïve Bayes (NB) 21 For classification, only a little quantity of training data is required. Interaction between features cannot be learned because of feature independence. Convolutional Neural Network (CNN) 22 •It is used in image processing without the need for feature engineering. •Longer time for training set Random Forest (RF)

The RF algorithm, initially introduced by Breiman in 2001, comprises a collection of predictive trees that are aggregated such that each tree is constructed using randomly selected vector values and sampled simultaneously while adhering to the same distribution23, 24. During the process, the prediction that acquires most of the tree votes will be predicted as the main result. RF possesses the capability to handle datasets containing missing variables and uneven data. Additionally, it is capable of computing crucial features for classification, which establishes it as one of the most efficient classifier algorithms in the domain (Table 1). It is widely used in detecting and classifying cancer cells, for example, in the detection of acute lymphoblastic leukemia (ALL)25, 26, 27, 28.

Support Vector Machine (SVM)

The SVM algorithm, first introduced in 1992 by Vladimir Vapnik, is utilized to determine a suitable class label to segment data samples29. SVM, in brief, is a learning algorithm that analyzes data used for categorization; when data patterns are highly dimensional and well-spaced, it is an excellent choice for classification30. With larger datasets, however, this approach requires an extended training period (Table 1). In addition to disease diagnosis, SVM is applied in the classification of hematological malignancies and their subtypes utilizing its categorization capabilities1, 31, 32.

K-Nearest Neighbor (KNN)

KNN is a non-parametric classification algorithm developed by Joseph Hodges and Evelyn Fix in 195133. The model attempts to classify non-linear sample data points in a database into several classes34. As a result, it tends to identify irrelevant data that are distant, so it takes a significantly longer time in the testing procedure (Table 1). In the medical and health industry, this method is used on electrocardiogram (ECG) data to detect abnormalities in the heart, such as tachycardia35.

Naïve Bayes (NB)

NB is a classification method based on Bayes' Theorem, developed by Thomas Bayes in 176036. It is an independent predictor hypothesis that evaluates the degree of relationship between the classification variables. Essentially, the class with the greatest likelihood is the class with the highest probability. It is effective with small datasets and has been implemented in the classification of hematological disorders and heart disease, among other disease diagnoses37, 38, 39. For instance, in the study of cardiovascular disease, scientists utilized NB as a data mining technique to discover correlations in a database between variables such as age, sex, and fasting blood sugar37, 40. NB is also applied in the classification of red blood cells (RBCs) and sickle cells39.

Deep Learning (DL)

DL is a sub-domain of ML comprised of numerous layers that extract information from various input data formats, including images, numbers, etc. The DL model derives its structure and operations from the neural network found in the human brain and thus closely resembles them41. The structure of its processing units consists of output, hidden, and input layers. Employing units or nodes, each layer's nodes are connected to those in the layer beneath it; each link is assigned a weight42. DL is divided into three subtypes: supervised, semi-supervised, and unsupervised. The main DL models that are currently being practiced are artificial neural networks (ANN), deep neural networks (DNN), recurrent neural networks (RNN), and convolutional neural networks (CNN). Leukemia31, malaria43, and thalassemia19 are among the many diseases that have been diagnosed using CNN in medical image processing. A key advantage of DL compared to conventional ML is that it eliminates the necessity for segmentation and feature extraction operations that are intrinsic to ML’s functionality.

Convolutional Neural Network (CNN)

CNN, which Yann LeCun introduced in 1980 as a subfield of artificial neural networks (ANN), is widely employed in the domain of image processing3, 20. CNN operates by assigning weights to the components within an image and then differentiating them from the remainder of the image; this makes it faster in the testing set than any other method (Table 1 ). Nonetheless, in applications where data is scarce, the algorithm's utility is restricted due to the necessity for sizable datasets to ensure greater accuracy and efficiency (Table 1). Over the past three decades, a specific type of CNN has proven effective in diagnosing cancer, particularly leukemia12.

Performance Metrics in AI Application

Performance metrics are crucial for evaluating the effectiveness of AI models, especially in the medical field, where accuracy is paramount. In this context, these metrics play a vital role in the AI development pipeline. AI models that achieve over 90% in these evaluations are typically regarded as highly effective or reliable, given the high stakes involved in patient care and diagnosis. The metrics that are commonly used are accuracy, sensitivity/recall, specificity, F1-Score, and precision21.

Accuracy: Accuracy is described as the proportion of true predictions made by a classifier to the total of all predictions as Equation 1 :

Sensitivity/Recall: Sensitivity gives only true positive measure considering total prediction and can be measured as Equation 2 :

Specificity: Specificity calculates how many true negatives are correctly detected and identified as Equation 3 :

F1 Score: The F1 score is the harmonic mean of accuracy and recall. Its calculation is as Equation 4 :

Precision: Precision is defined as the metric that measures the accuracy of predicted positive observations to all expected positive observations. It is determined as Equation 5 :

Application of AI in the Diagnosis of Hematological Disorders

In the area of hematology, a precise and rapid diagnosis of disease is necessary, as some blood disorders, such as acute myeloid leukemia (AML), are highly heterogeneous and exhibit transcriptomic, proteomic, and metabolomic variations22, 44, 45. Furthermore, depending on the knowledge and expertise of the hematologist, there is a significant chance of human error, which may delay or result in an incorrect diagnosis of the disease, causing severe disease progression or death. Thus, there is a growing need for a medical system that can reduce diagnosis turnaround time while preventing human errors.

In 1995, AI was first applied in a hematology laboratory for peripheral blood interpretation, flow cytometry immunophenotyping, and bone marrow reporting, and it became a strong point due to its high accuracy and specificity46. The implementation of AI in hematology includes, but is not limited to, diagnosis, detection, and classification of diseases. Among the most common uses for machine learning (ML) in hematology are image processing, recognition, and classification8. Moreover, ML and deep learning (DL) have been applied to the detection of diseases utilizing visual and tabular data, among others47. Visual data contains images of blood and bone marrow smears, while tabular data provides information such as age, gender, and test results.

Table 2.

Relevant literature on ML-based leukaemia disease diagnostics

Author Year Disease Classifier Dataset Performance metrics Dasariragu et al .