A decision-tree approach for the differential diagnosis of chronic lymphoid leukemias and peripheral B-cell lymphomas

Application of artificial intelligence in chronic myeloid leukemia (CML) disease prediction and management: a scoping review

BACKGROUND

Navigating the complexity of chronic myeloid leukemia (CML) diagnosis and management poses significant challenges, including the need for accurate prediction of disease progression and response to treatment. Artificial intelligence (AI) presents a transformative approach that enables the development of sophisticated predictive models and personalized treatment strategies that enhance early detection and improve therapeutic interventions for better patient outcomes.

METHODS

An extensive search was conducted to retrieve relevant articles from PubMed, Scopus, and Web of Science databases up to April 24, 2023. Data were collected using a standardized extraction form, and the results are presented in tables and graphs, showing frequencies and percentages. The authors adhered to the PRISMA-ScR checklist to ensure transparent reporting of the study.

RESULTS

Of the 176 articles initially identified, 12 were selected for our study after removing duplicates and applying the inclusion and exclusion criteria. AI's primary applications of AI in managing CML included tumor diagnosis/classification (n = 9, 75%), prediction/prognosis (n = 2, 17%), and treatment (n = 1, 8%). For tumor diagnosis, AI is categorized into blood smear image-based (n = 5), clinical parameter-based (n = 2), and gene profiling-based (n = 2) approaches. The most commonly employed AI models include Support Vector Machine (SVM) (n = 5), eXtreme Gradient Boosting (XGBoost) (n = 4), and various neural network methods, such as Artificial Neural Network (ANN) (n = 3). Furthermore, Hybrid Convolutional Neural Network with Interactive Autodidactic School (HCNN-IAS) achieved 100% accuracy and sensitivity in organizing leukemia data types, whereas MayGAN attained 99.8% accuracy and high performance in diagnosing CML from blood smear images.

CONCLUSIONS

AI offers groundbreaking insights and tools for enhancing prediction, prognosis, and personalized treatment in chronic myeloid leukemia. Integrated AI systems empower healthcare practitioners with advanced analytics, optimizing patient care and improving clinical outcomes in CML management.

A machine learning tool for the diagnosis of SARS-CoV-2 infection from hemogram parameters

Monocytes and neutrophils play key roles in the cytokine storm triggered by SARS-CoV-2 infection, which changes their conformation and function. These changes are detectable at the cellular and molecular level and may be different to what is observed in other respiratory infections. Here, we applied machine learning (ML) to develop and validate an algorithm to diagnose COVID-19 using blood parameters. In this retrospective single-center study, 49 hemogram parameters from 12,321 patients with clinical suspicion of COVID-19 and tested by RT-PCR (4239 positive and 8082 negative) were analysed. The dataset was randomly divided into training and validation sets. Blood cell parameters and patient age were used to construct the predictive model with the support vector machine (SVM) tool. The model constructed from the training set (5936 patients) achieved an accuracy for diagnosis of SARS-CoV-2 infection of 0.952 (95% CI: 0.875-0.892). Test sensitivity and specificity was 0.868 and 0.899, respectively, with a positive (PPV) and negative (NPV) predictive value of 0.896 and 0.872, respectively (prevalence 0.50). The validation set model (4964 patients) achieved an accuracy of 0.894 (95% CI: 0.883-0.903). Test sensitivity and specificity was 0.8922 and 0.8951, respectively, with a positive (PPV) and negative (NPV) predictive value of 0.817 and 0.94, respectively (prevalence 0.34). The area under the receiver operating characteristic curve was 0.952 for the algorithm performance. This algorithm may allow to rule out COVID-19 diagnosis with 94% of probability. This represents a great advance for early diagnostic orientation and guiding clinical decisions.

Use of a hybrid intelligence decision tree to identify mature B-cell neoplasms

BACKGROUND

Mature B-cell neoplasms are challenging to diagnose due to their heterogeneity and overlapping clinical and biological features. In this study, we present a new workflow strategy that leverages a large amount of flow cytometry data and an artificial intelligence approach to classify these neoplasms.

METHODS

By combining mathematical tools, such as classification algorithms and regression tree (CART) models, with biological expertise, we have developed a decision tree that accurately identifies mature B-cell neoplasms. This includes chronic lymphocytic leukemia (CLL), for which cytometry has been extensively used, as well as other non-CLL subtypes.

RESULTS

The decision tree is easy to use and proposes a diagnosis and classification of mature B-cell neoplasms to the users. It can identify the majority of CLL cases using just three markers: CD5, CD43, and CD200.

CONCLUSION

This approach has the potential to improve the accuracy and efficiency of mature B-cell neoplasm diagnosis.

Methodological and conceptual challenges to the flow cytometric classification of leukemic lymphoproliferative disorders

The diagnosis of leukemic B-cell lymphoproliferative disorders (B-LPDs) is made by integrating clinical, cytological, cytometric, cytogenetic, and molecular data. This leaves room for differences and inconsistencies between experts. In this study, we examine methodological and conceptual aspects of the flow cytometric classification of leukemic B-LPDs that could explain them. Among methodological aspects, we discuss (1) the different statistical tests used to select and evaluate markers, (2) how these markers are analyzed, (3) how scores are interpreted, (4) different degrees to which diagnostic information is used, and (5) and the impact of differences in study populations. Among conceptual aspects, we discuss (1) challenges to integrating different biological data points, (2) the under examination of the costs of misclassification (false positives and false negatives), and finally, (3) we delve into the impact of the lack of a true diagnostic gold standard and the indirect evidence suggesting poor reproducibility in the diagnosis of leukemic B-LPDs. We then outline current harmonization efforts and our personal approach. We conclude that numerous flow cytometry scores and diagnostic systems are now available; however, as long as the considerations discussed remain unaddressed, external reproducibility and interobserver agreement will not be achieved, and the field will not be able to move forward if a true gold standard is not found.

A new method for disease diagnosis based on hierarchical BRB with power set

Disease diagnosis occupies an important position in the medical field. The diagnosis of the disease is the basis for choosing the right treatment plan. Doctors must first diagnose what the patient has based on the clinical characteristics of various diseases, and then they can administer the right medicine. When building models for disease diagnosis, models are required to be able to handle various uncertainty information. The belief rule base (BRB) can effectively handle various information under uncertainty by introducing belief distributions. However, in current research, BRB-based disease diagnosis models still have problems of combinatorial rule explosion and inability to deal with local ignorance effectively. Therefore, a hierarchical BRB with power set (H-BRBp)-based disease diagnosis model is proposed in this paper. First, the physiological indexes and data of the patients were analyzed, and the data were preprocessed using the principal component regression (PCR) algorithm. Second, the H-BRBp disease diagnosis model was constructed to solve the deficiencies in the above BRB disease diagnosis model. Finally, the validity and advantages of the model were verified by experiments on lumbar spine disease diagnosis and a large number of comparison experiments.

A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects

Background

Machine learning (ML) and deep learning (DL) methods have recently garnered a great deal of attention in the field of cancer research by making a noticeable contribution to the growth of predictive medicine and modern oncological practices. Considerable focus has been particularly directed toward hematologic malignancies because of the complexity in detecting early symptoms. Many patients with blood cancer do not get properly diagnosed until their cancer has reached an advanced stage with limited treatment prospects. Hence, the state-of-the-art revolves around the latest artificial intelligence (AI) applications in hematology management.

Objective

This comprehensive review provides an in-depth analysis of the current AI practices in the field of hematology. Our objective is to explore the ML and DL applications in blood cancer research, with a special focus on the type of hematologic malignancies and the patient’s cancer stage to determine future research directions in blood cancer.

Methods

We searched a set of recognized databases (Scopus, Springer, and Web of Science) using a selected number of keywords. We included studies written in English and published between 2015 and 2021. For each study, we identified the ML and DL techniques used and highlighted the performance of each model.

Results

Using the aforementioned inclusion criteria, the search resulted in 567 papers, of which 144 were selected for review.

Conclusions

The current literature suggests that the application of AI in the field of hematology has generated impressive results in the screening, diagnosis, and treatment stages. Nevertheless, optimizing the patient’s pathway to treatment requires a prior prediction of the malignancy based on the patient’s symptoms or blood records, which is an area that has still not been properly investigated.

Different Data Mining Approaches Based Medical Text Data

The amount of medical text data is increasing dramatically. Medical text data record the progress of medicine and imply a large amount of medical knowledge. As a natural language, they are characterized by semistructured, high-dimensional, high data volume semantics and cannot participate in arithmetic operations. Therefore, how to extract useful knowledge or information from the total available data is very important task. Using various techniques of data mining can extract valuable knowledge or information from data. In the current study, we reviewed different approaches to apply for medical text data mining. The advantages and shortcomings for each technique compared to different processes of medical text data were analyzed. We also explored the applications of algorithms for providing insights to the users and enabling them to use the resources for the specific challenges in medical text data. Further, the main challenges in medical text data mining were discussed. Findings of this paper are benefit for helping the researchers to choose the reasonable techniques for mining medical text data and presenting the main challenges to them in medical text data mining.

A novel differential diagnosis algorithm for chronic lymphocytic leukemia using immunophenotyping with flow cytometry

INTRODUCTION

The availability of a clinical decision algorithm for diagnosis of chronic lymphocytic leukemia (CLL) may greatly contribute to the diagnosis of CLL, particularly in cases with ambiguous immunophenotypes. Herein we propose a novel differential diagnosis algorithm for the CLL diagnosis using immunophenotyping with flow cytometry.

METHODS

The hierarchical logistic regression model (Backward LR) was used to build a predictive algorithm for the diagnosis of CLL, differentiated from other lymphoproliferative disorders (LPDs).

RESULTS

A total of 302 patients, of whom 220 (72.8%) had CLL and 82 (27.2%), B-cell lymphoproliferative disorders other than CLL, were included in the study. The Backward LR model comprised the variables CD5, CD43, CD81, ROR1, CD23, CD79b, FMC7, sIg and CD200 in the model development process. The weak expression of CD81 and increased intensity of expression in markers CD5, CD23 and CD200 increased the probability of CLL diagnosis, (p < 0.05). The odd ratio for CD5, C23, CD200 and CD81 was 1.088 (1.050 - 1.126), 1.044 (1.012 - 1.077), 1.039 (1.007 - 1.072) and 0.946 (0.921 - 0.970) [95% C.I.], respectively. Our model provided a novel diagnostic algorithm with 95.27% of sensitivity and 91.46% of specificity. The model prediction for 97.3% (214) of 220 patients diagnosed with CLL, was CLL and for 91.5% (75) of 82 patients diagnosed with an LPD other than CLL, was others. The cases were correctly classified as CLL and others with a 95.7% correctness rate.

CONCLUSIONS

Our model highlighting 4 markers (CD81, CD5, CD23 and CD200) provided high sensitivity and specificity in the CLL diagnosis and in distinguishing of CLL among other LPDs.

A geno-clinical decision model for the diagnosis of myelodysplastic syndromes

We developed a machine learning–based model to assist in the differential diagnosis of myeloid malignancies.

Our work also describes genotype-phenotype correlations in different myeloid malignancies.

The differential diagnosis of myeloid malignancies is challenging and subject to interobserver variability. We used clinical and next-generation sequencing (NGS) data to develop a machine learning model for the diagnosis of myeloid malignancies independent of bone marrow biopsy data based on a 3-institution, international cohort of patients. The model achieves high performance, with model interpretations indicating that it relies on factors similar to those used by clinicians. In addition, we describe associations between NGS findings and clinically important phenotypes and introduce the use of machine learning algorithms to elucidate clinicogenomic relationships.

Accurate Machine-Learning-Based classification of Leukemia from Blood Smear Images

BACKGROUND

Conventional identification of blood disorders based on visual inspection of blood smears through microscope is time consuming, error-prone and is limited by hematologist's physical acuity. Therefore, an automated optical image processing system is required to support the clinical decision-making.

MATERIALS AND METHODS

Blood smear slides (n = 250) were prepared from clinical samples, imaged and analyzed in Jimma Medical Center, Hematology department. Samples were collected, analyzed and preserved from out and in-patients. The system was able to categorize four common types of leukemia's such as acute and chronic myeloid leukemia; and acute and chronic lymphoblastic leukemia, through a robust image segmentation protocol, followed by classification using the support vector machine.

RESULTS

The system was able to classify leukemia types with an accuracy, sensitivity, specificity of 97.69%, 97.86% and 100%, respectively for the test datasets, and 97.5%, 98.55% and 100%, respectively, for the validation datasets. In addition, the system also showed an accuracy of 94.75% for the WBC counts that include both lymphocytes and monocytes. The computer-assisted diagnosis system took less than one minute for processing and assigning the leukemia types, compared to an average period of 30 minutes by unassisted manual approaches. Moreover, the automated system complements the healthcare workers' in their efforts, by improving the accuracy rates in diagnosis from ∼70% to over 97%.

CONCLUSION

Importantly, our module is designed to assist the healthcare facilities in the rural areas of sub-Saharan Africa, equipped with fewer experienced medical experts, especially in screening patients for blood associated diseases including leukemia.

Clinical Decision Support Trees Can Help Optimize Utilization of Anaplasma phagocytophilum Nucleic Acid Amplification Testing

Anaplasmosis, a tick-borne illness caused by Anaplasma phagocytophilum (AP), presents with nonspecific clinical symptoms, including fever and headache, and is often accompanied by laboratory abnormalities of leukopenia, thrombocytopenia, and mildly elevated liver function tests (LFTs). Laboratory confirmation of acute infection occurs with nucleic acid amplification testing (NAAT). This retrospective cohort study aimed to develop a clinical decision support algorithm to aid in decision-making about test ordering. A data set was constructed with AP NAAT results and time-adjacent complete blood count and LFT results for adult patients tested for AP in a 12.5-year period. A second, smaller data set matched each patient with a positive AP NAAT to two patients with negative tests. Chart review for clinical symptoms was performed on this smaller data set. A decision tree algorithm was deployed to identify patient clusters with negative AP NAAT results. A total of 137/1,204 (11%) patients tested positive by NAAT for AP. In the larger, laboratory-only data set (n = 1,204), patients with a platelet count of >177 × 10³/μl and age of <48 years had a negative AP NAAT (204/1,204, 17%, P < 0.05). In the smaller, cohorted data set with chart review (n = 402), patients with a platelet count of >188 × 10³/μl and no fever or chills also did not have positive AP NAAT (58/402, 14%, P < 0.05). We generated two decision trees that can help determine the utility of AP NAAT using readily available clinical and laboratory data. These have the potential to significantly reduce unnecessary AP testing.

Cognitive Computing-Based CDSS in Medical Practice

Importance. The last decade has witnessed the advances of cognitive computing technologies that learn at scale and reason with purpose in medicine studies. From the diagnosis of diseases till the generation of treatment plans, cognitive computing encompasses both data-driven and knowledge-driven machine intelligence to assist health care roles in clinical decision-making. This review provides a comprehensive perspective from both research and industrial efforts on cognitive computing-based CDSS over the last decade.Highlights. (1) A holistic review of both research papers and industrial practice about cognitive computing-based CDSS is conducted to identify the necessity and the characteristics as well as the general framework of constructing the system. (2) Several of the typical applications of cognitive computing-based CDSS as well as the existing systems in real medical practice are introduced in detail under the general framework. (3) The limitations of the current cognitive computing-based CDSS is discussed that sheds light on the future work in this direction.Conclusion. Different from medical content providers, cognitive computing-based CDSS provides probabilistic clinical decision support by automatically learning and inferencing from medical big data. The characteristics of managing multimodal data and computerizing medical knowledge distinguish cognitive computing-based CDSS from other categories. Given the current status of primary health care like high diagnostic error rate and shortage of medical resources, it is time to introduce cognitive computing-based CDSS to the medical community which is supposed to be more open-minded and embrace the convenience and low cost but high efficiency brought by cognitive computing-based CDSS.

Machine learning and augmented human intelligence use in histomorphology for haematolymphoid disorders

Advances in digital pathology have allowed a number of opportunities such as decision support using artificial intelligence (AI). The application of AI to digital pathology data shows promise as an aid for pathologists in the diagnosis of haematological disorders. AI-based applications have embraced benign haematology, diagnosing leukaemia and lymphoma, as well as ancillary testing modalities including flow cytometry. In this review, we highlight the progress made to date in machine learning applications in haematopathology, summarise important studies in this field, and highlight key limitations. We further present our outlook on the future direction and trends for AI to support diagnostic decisions in haematopathology.

Artificial Intelligence in Hematology: Current Challenges and Opportunities

PURPOSE OF REVIEW

Artificial intelligence (AI), and in particular its subcategory machine learning, is finding an increasing number of applications in medicine, driven in large part by an abundance of data and powerful, accessible tools that have made AI accessible to a larger circle of investigators.

RECENT FINDINGS

AI has been employed in the analysis of hematopathological, radiographic, laboratory, genomic, pharmacological, and chemical data to better inform diagnosis, prognosis, treatment planning, and foundational knowledge related to benign and malignant hematology. As more widespread implementation of clinical AI nears, attention has also turned to the effects this will have on other areas in medicine. AI offers many promising tools to clinicians broadly, and specifically in the practice of hematology. Ongoing research into its various applications will likely result in an increasing utilization of AI by a broader swath of clinicians.

Machine learning in haematological malignancies

Machine learning is a branch of computer science and statistics that generates predictive or descriptive models by learning from training data rather than by being rigidly programmed. It has attracted substantial attention for its many applications in medicine, both as a catalyst for research and as a means of improving clinical care across the cycle of diagnosis, prognosis, and treatment of disease. These applications include the management of haematological malignancy, in which machine learning has created inroads in pathology, radiology, genomics, and the analysis of electronic health record data. As computational power becomes cheaper and the tools for implementing machine learning become increasingly democratised, it is likely to become increasingly integrated into the research and practice landscape of haematology. As such, machine learning merits understanding and attention from researchers and clinicians alike. This narrative Review describes important concepts in machine learning for unfamiliar readers, details machine learning's current applications in haematological malignancy, and summarises important concepts for clinicians to be aware of when appraising research that uses machine learning.

Predicted Optimal Bifunctional Electrocatalysts for the Hydrogen Evolution Reaction and the Oxygen Evolution Reaction Using Chalcogenide Heterostructures Based on Machine Learning Analysis of in Silico Quantum Mechanics Based High Throughput Screening

Two-dimensional van der Waals heterostructure materials, particularly transition metal dichalcogenides (TMDC), have proved to be excellent photoabsorbers for solar radiation, but performance for such electrocatalysis processes as water splitting to form H₂ and O₂ is not adequate. We propose that dramatically improved performance may be achieved by combining two independent TMDC while optimizing such descriptors as rotational angle, bond length, distance between layers, and the ratio of the bandgaps of two component materials. In this paper we apply the least absolute shrinkage and selection operator (LASSO) process of artificial intelligence incorporating these descriptors together with quantum mechanics (density functional theory) to predict novel structures with predicted superior performance. Our predicted best system is MoTe₂/WTe₂ with a rotation of 300°, which is predicted to have an overpotential of 0.03 V for HER and 0.17 V for OER, dramatically improved over current electrocatalysts for water splitting.