Machine learning applications in the diagnosis of leukemia: Current trends and future directions

Abstract

Augmented human intelligence (AHI) and artificial intelligence (AI) tools might shape the future of medical practice. The expansion of data generated by our systems, medical literature, and the inefficiencies of healthcare systems will necessitate utilizing the power of AI tools. 1,2 The integration of AHI tools into medical practice, including machine learning (ML) and deep learning algorithms, has begun. For instance, the United States food and drug administration (US-FDA) has approved many AI-based softwares since 2017 for medical use. 2,3 The introduction of digital pathology has brought many opportunities to the field of pathology, such as telemedicine. 4,5 Recently, the use of digital pathology has allowed for the use of ML (including deep learning algorithms) in the automation of pathological diagnosis. 6,7 The challenges facing the use of ML in pathology are many, including digitalizing slides, labeling in case of Abstract Machine learning (ML) offers opportunities to advance pathological diagnosis, especially with increasing trends in digitalizing microscopic images. Diagnosing leukemia is time-consuming and challenging in many areas globally and there is a growing trend in utilizing ML techniques for its diagnosis. In this review, we aimed to describe the literature of ML utilization in the diagnosis of the four common types of leukemia: acute lymphocytic leukemia (ALL), chronic lymphocytic leukemia (CLL), acute myeloid leukemia (AML), and chronic myelogenous leukemia (CML). Using a strict selection criterion, utilizing MeSH terminology and Boolean logic, an electronic search of MEDLINE and IEEE Xplore Digital Library was performed. The electronic search was complemented by handsearching of references of related studies and the top results of Google Scholar. The full texts of 58 articles were reviewed, out of which, 22 studies were included. The number of studies discussing ALL, AML, CLL, and CML was 12, 8, 3, and 1, respectively. No studies were prospectively applying algorithms in real-world scenarios. Majority of studies had small and homogenous samples and used supervised learning for classification tasks. 91% of the studies were performed after 2010, and 74% of the included studies applied ML algorithms to microscopic diagnosis of leukemia. The included studies illustrated the need to develop the field of ML research, including the transformation from solely designing algorithms to practically applying them clinically. K E Y W O R D S diagnosis, digital, leukemia, machine learning, pathology How to cite this article: Salah HT, Muhsen IN, Salama ME, Owaidah T, Hashmi SK. Machine learning applications in the diagnosis of leukemia: Current trends and future directions. Int J Lab Hematol. 2019;41:717-725. https ://doi.

Artificial Intelligence and Digital Microscopy Applications in Diagnostic Hematopathology

“…Recently, investigators have been applying unsupervised methods to minimize the need for manual segmentation and feature extraction, which are time consuming tasks for operators, particularly in hematology and cytology. This is in line with conducting studies to apply ML in the hematopathology field [38].…”

Artificial intelligence–based morphological fingerprinting of megakaryocytes: a new tool for assessing disease in MPN patients

“…This compliments other novel AI strategies aimed at utilizing mutational and peripheral blood data for more accurate MPN diagnosis, 30 along with improved interpretation of peripheral blood/bone marrow smear preparations. [31][32][33][34] Secondly, a comprehensive and easily interpreted summary of the megakaryocytic population will enable the pathologist to concentrate on the "higher-level" process of integrating the broader pathological features with the clinical and laboratory findings. Thirdly, this approach is ideally suited for more accurate assessment of sequential specimens from patients undergoing treatment and/or repeated investigation, in whom quantitative morphological correlates of disease response are currently unavailable.…”

Development and Interpretation of Multiple Machine Learning Models for Predicting Postoperative Delayed Remission of Acromegaly Patients During Long-Term Follow-Up

“…ML is increasingly used in the medical community, particularly in the field of oncology. Previous studies have demonstrated that ML models can provide better accuracy and discrimination for the prediction of prognoses for lung adenocarcinoma (12) and breast cancer (13), chemoradiation therapy response in rectal cancer (14), radiotherapy response for acromegaly (15), surgical outcomes for head and neck cancer (16), and diagnosis for leukemia (17). For sellar region tumors, ML could be more effective for predicting a patient's clinical outcome and could provide better clinical decision support for neuroendocrinologists and neurosurgeons (18).…”