ML Applications Research Articles

Systematic Analysis of Ovarian Cancer Empowered with Machine and Deep Learning: A Taxonomy and Future Challenges

Machine and Deep learning has witnessed an exceptional amount of admiration in recent years. ML has ability to learn data itself by predicting uncertain conditions or future and classify categories with minimum intervention of human. While in DL, computers are able to automatically, learn useful features and representation precisely from raw data. ML and DL potentially a disruptive technology in predictive healthcare analysis. A detailed understanding and evaluation of the applications and principles of radiomics, machine and deep learning is an important task to construct possible solutions that are capable of accomplish compulsory and ethical requirements, which can enhance efficiency, quality and outcomes. Machine and deep learning extensively being use in medical image analysis, medical diagnostics and medical image technology. The wide scope and sudden progress of ML and DL has remarkably change the ways of diagnosis, prediction, classification and analyzing ovarian, lungs, brain, skin and various other types of cancer. In view of multiple applications of ML and DL, in this article a review conducted on ovarian cancer. This review comprises the detailed analysis of OC (prognosis, diagnosis, classification, evaluation) by covering all the major contributions of machine and deep learning. Furthermore, a literature taxonomy of the research conferred and emerging aspects analyzed. A section of discussion also signified to elaborate the limitations and future challenges of machine and deep learning comprises with ovarian cancer.

Increasing trust and fairness in machine learning applications within the mortgage industry

The integration of machine learning in applications provides opportunities for increased efficiency in many organisations. However, the deployment of such systems is often hampered by the lack of insight into how their decisions are reached, resulting in concerns about trust and fairness. In this article, we investigate to what extent the addition of explainable AI components to ML applications can contribute to alleviating these issues. As part of this research, explainable AI functionality was developed for an existing ML model used for mortgage fraud detection at a large international financial institution based in The Netherlands A system implementing local explanation techniques was deployed to support the day-to-day work of fraud detection experts working with the model. In addition, a second system implementing global explanation techniques was developed to support the model management processes involving data-scientists, legal experts and compliance officers. A controlled experiment using actual mortgage applications was carried out to measure the effectiveness of these two systems, using both quantitative and qualitative assessment methods. Our results show that the addition of explainable AI functionality results in a statistically significant improvement in the levels of trust and usability by its daily users. The explainable AI system implementing global interpretability was found to considerably increase confidence in the ability to perform the processes focused on compliance and fairness. In particular, bias detection towards demographic groups successfully aided in the identification and removal of bias towards applicants with a migration background.

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

BackgroundMachine 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.ObjectiveThis 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.MethodsWe 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.ResultsUsing the aforementioned inclusion criteria, the search resulted in 567 papers, of which 144 were selected for review.ConclusionsThe 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.

Effects of Soil Application of Chitosan and Foliar Melatonin on Growth, Photosynthesis, and Heavy Metals Accumulation in Wheat Growing on Wastewater Polluted Soil

Due to freshwater scarcity in developing countries, irrigating the arable land with wastewater poses potential ecological risks to the environment and food quality. Using cheap soil amendments and foliar application of a newly discovered molecule “melatonin” (ML) can alleviate these effects. The objectives of this pot study were to evaluate the effectiveness of the sole addition of chitosan (CH) and sugar beet factory lime (SBL) in wastewater impacted soil, foliar application of ML, and combining each soil amendment with ML on the heavy metals (HMs) accumulation, growth, nutritional quality and photosynthesis in wheat. Results showed that CH was more effective than SBL for reducing HMs bioavailability in soil, HMs distribution in plants, improving photosynthesis, nutritional quality, and growth. ML application also influenced plant parameters but less than CH and SBL. The CH+ML treatment was the most effective for influencing plant parameters and reducing HMs bioavailability in the soil. Compared to control, CH+ML significantly reduced the concentrations of Pb, Cd, Cr, Ni, Cu, and Co in roots, shoots, and grain up to 89%. We conclude that adding CH+ML in wastewater impacted soils can remediate the soil; reduce HMs concentrations in plants; and improve their photosynthesis, plant growth, grain yield, and nutrition.

Coevolution of machine learning and process‐based modelling to revolutionize Earth and environmental sciences: A perspective

AbstractMachine learning (ML) applications in Earth and environmental sciences (EES) have gained incredible momentum in recent years. However, these ML applications have largely evolved in ‘isolation’ from the mechanistic, process‐based modelling (PBM) paradigms, which have historically been the cornerstone of scientific discovery and policy support. In this perspective, we assert that the cultural barriers between the ML and PBM communities limit the potential of ML, and even its ‘hybridization’ with PBM, for EES applications. Fundamental, but often ignored, differences between ML and PBM are discussed as well as their strengths and weaknesses in light of three overarching modelling objectives in EES, (1) nowcasting and prediction, (2) scenario analysis, and (3) diagnostic learning. The paper ponders over a ‘coevolutionary’ approach to model building, shifting away from a borrowing to a co‐creation culture, to develop a generation of models that leverage the unique strengths of ML such as scalability to big data and high‐dimensional mapping, while remaining faithful to process‐based knowledge base and principles of model explainability and interpretability, and therefore, falsifiability.

Applications of artificial intelligence and machine learning in heart failure.

Machine learning (ML) is a sub-field of artificial intelligence that uses computer algorithms to extract patterns from raw data, acquire knowledge without human input, and apply this knowledge for various tasks. Traditional statistical methods that classify or regress data have limited capacity to handle large datasets that have a low signal-to-noise ratio. In contrast to traditional models, ML relies on fewer assumptions, can handle larger and more complex datasets, and does not require predictors or interactions to be pre-specified, allowing for novel relationships to be detected. In this review, we discuss the rationale for the use and applications of ML in heart failure, including disease classification, early diagnosis, early detection of decompensation, risk stratification, optimal titration of medical therapy, effective patient selection for devices, and clinical trial recruitment. We discuss how ML can be used to expedite implementation and close healthcare gaps in learning healthcare systems. We review the limitations of ML, including opaque logic and unreliable model performance in the setting of data errors or data shift. Whilst ML has great potential to improve clinical care and research in HF, the applications must be externally validated in prospective studies for broad uptake to occur.

Potential applications of machine learning for BIM in tunnelling

AbstractMachine Learning (ML) and Building Information Modelling (BIM) are two topics that are part of a revolutionizing transformation in the construction industry – commonly referred to as digitalization. Being part of the research for artificial intelligence (AI), most of today's ML applications deal with computational processes that try to make sense of data. Automatic rockmass behaviour classification based on tunnel boring machine (TBM) data or tunnel construction site surveillance via closed‐circuit television (CCTV) analysis is an example for applications of ML in tunnelling. BIM describes a new type of planning, including model‐based collaboration and information exchange, which requires well‐organized storage and handling of data – a precondition and valuable source for any automated analysis method like ML. While other sectors of the construction industry have implemented BIM systems successfully, the development in underground engineering is currently at its beginning with multiple actors working towards common standards for semantics, data exchange formats, etc. This article seeks to combine the two fields by giving an overview of the two topics and then points out four potential fields of applications: semantic enrichment and labelling, automation of technical processes, knowledge derivation and online data analysis.

HPC-oriented Canonical Workflows for Machine Learning Applications in Climate and Weather Prediction

Abstract Machine learning (ML) applications in weather and climate are gaining momentum as big data and the immense increase in High-performance computing (HPC) power are paving the way. Ensuring FAIR data and reproducible ML practices are significant challenges for Earth system researchers. Even though the FAIR principle is well known to many scientists, research communities are slow to adopt them. Canonical Workflow Framework for Research (CWFR) provides a platform to ensure the FAIRness and reproducibility of these practices without overwhelming researchers. This conceptual paper envisions a holistic CWFR approach towards ML applications in weather and climate, focusing on HPC and big data. Specifically, we discuss Fair Digital Object (FDO) and Research Object (RO) in the DeepRain project to achieve granular reproducibility. DeepRain is a project that aims to improve precipitation forecast in Germany by using ML. Our concept envisages the raster datacube to provide data harmonization and fast and scalable data access. We suggest the Juypter notebook as a single reproducible experiment. In addition, we envision JuypterHub as a scalable and distributed central platform that connects all these elements and the HPC resources to the researchers via an easy-to-use graphical interface.

Reliable Algorithms for Machine Learning Models: Implementation Research in Data Science

Machine Learning generates programs that make predictions and informed decisions about complex problems in an efficient and reliable way. These ML programs autonomously test solutions against the dataset to find the best fit for the problem. The paper aims to review the ML algorithms that develop prediction models by utilizing training dataset and known output. The paper also focuses on ML principles, algorithms, approaches, and applications for Supervised, Unsupervised, and Reinforcement learning that can perform tasks without being explicitly programmed for it. Completely opposite to rule-based programming, the machine learning paradigm uses examples of real data sets and pre-process it before providing the desired outputs based on these examples. In the case of more involved and complex tasks, it can be challenging for humans to explicitly program the models. On the other hand, it can be more effective to help the machines develop the algorithms for advanced tasks. This paper will also present the trending real-world applications of Machine Learning in Image Recognition and Biomedicine. Additionally, it will provide a background analysis of machine learning and related fields of data science.

Long short-term memory model – A deep learning approach for medical data with irregularity in cancer predication with tumor markers

BackgroundMachine learning (ML) has emerged as a superior method for the analysis of large datasets. Application of ML is often hindered by incompleteness of the data which is particularly evident when approaching disease screening data due to varied testing regimens across medical institutions. Here we explored the utility of multiple ML algorithms to predict cancer risk when trained using a large but incomplete real-world dataset of tumor marker (TM) values. MethodsTM screening data were collected from a large asymptomatic cohort (n = 163,174) at two independent medical centers. The cohort included 785 individuals who were subsequently diagnosed with cancer. Data included levels of up to eight TMs, but for most subjects, only a subset of the biomarkers were tested. In some instances, TM values were available at multiple time points, but intervals between tests varied widely. The data were used to train and test various machine learning models to evaluate their robustness for predicting cancer risk. Multiple methods for data imputation were explored and models were developed for both single time-point as well as time-series data. ResultsThe ML algorithm, long short-term memory (LSTM), demonstrated superiority over other models for dealing with irregular medical data. A cancer risk prediction tool was trained and validated for a single time-point test of a TM panel including up to four biomarkers (AUROC = 0.831, 95% CI: 0.827–0.835) which outperformed a single threshold method using the same biomarkers. A second model relying on time series data of up to four time-points for 5 TMs had an AUROC of 0.931. ConclusionsA cancer risk prediction tool was developed by training a LSTM model using a large but incomplete real-world dataset of TM values. The LSTM model was best able to handle irregular data compared to other ML models. The use of time-series TM data can further improve the predictive performance of LSTM models even when the intervals between tests vary widely. These risk prediction tools are useful to direct subjects to further screening sooner, resulting in earlier detection of occult tumors.

Artificial Intelligence, Machine Learning and Deep Learning (Literature: Review and Metrics)

Nowadays the ‘Artificial Intelligence (AI)’ research phenomena is exponentially growing due to its immense applications in all disciplines. That is why all the business corporates are in development of AI products and services in a bigger way. The humanoid robot Sophia, supply chain logistics drones, the Amazon voice recognition Alexa, and Tesla’s self-driving cars are some of the AI and its sister technological footprints in our life. Hollywood movies also witnessed the inevitable role of AI and its solicitations in human life. Hence, to provide quality of life to human beings as well as quality of service in professional life, there need to be more AI innovative solutions. As the concept is contemporary, the study comes under exploratory study however, data have been collected from online databases including Web of Science, Scopus, EBSCO and Google Scholar. The aim of this research paper is to give an overview of AI and its sister technologies of Machine Learning and Deep Learning. Through an in-depth literature review, the manuscript explored many business applications of these technologies. The study revealed that AI, ML and DL applications are exponentially growing in many sectors including banking, insurance, healthcare, e-commerce, social media networking and transportation to name a few. Moreover, the authors also provided VOSviewer’s functionality for displaying bibliometric maps on AI, ML and DL literature analysis. This shows the importance of the phenomenon, authors, countries, journals and year of publications.

A Comparative Analysis of Business Machine Learning in Making Effective Financial Decisions Using Structural Equation Model (SEM)

Globally, organisations are focused on deriving more value from the data which has been collected from various sources. The purpose of this research is to examine the key components of machine learning in making efficient financial decisions. The business leaders are now faced with huge volume of data, which needs to be stored, analysed, and retrieved so as to make effective decisions for achieving competitive advantage. Machine learning is considered to be the subset of artificial intelligence which is mainly focused on optimizing the business process with lesser or no human interventions. The ML techniques enable analysing the pattern and recognizing from large data set and provide the necessary information to the management for effective decision making in different areas covering finance, marketing, supply chain, human resources, etc. Machine learning enables extracting the quality patterns and forecasting the data from the data base and fosters growth; the machine learning enables transition from the physical data to electronically stored data, enables enhancing the memory, and supports with financial decision making and other aspects. This study is focused on addressing the application of machine learning in making the effective financial decision making among the companies; the application of ML has emerged as a critical technology which is being applied in the current competitive market, and it has offered more opportunities to the business leaders in leveraging the large volume of data. The study is intended to collect the data from employees, managers, and business leaders in various industries to understand the influence of machine learning in financial decision making .

Toward Excellence of Electrocatalyst Design by Emerging Descriptor‐Oriented Machine Learning

AbstractMachine learning (ML) is emerging as a powerful tool for identifying quantitative structure–activity relationships to accelerate electrocatalyst design by learning from historic data without explicit programming. The algorithms, data/database, and descriptors are usually the decisive factors for ML and the descriptors play a pivotal role for electrocatalysis as they contain the essence of catalysis from the physicochemical nature. Despite the considerable research efforts regarding electrocatalyst design with ML, the lack of universal selection tactics for descriptors bridging the gap between structures and activity impedes its wider application. A timely summary of the application of ML in electrocatalyst design helps to deepen the understanding of the nature of descriptors and improve the application scope and design efficiency. This review summarizes the geometrical, electronic, and activity descriptors used as input for ML training and predicting to reveal the general rules for their application in the design of electrocatalysts. In response to the challenges of hydrogen evolution reaction, oxygen evolution reaction, oxygen reduction reaction, CO2 reduction reaction, and nitrogen reduction reaction, the ML application in these areas is tracked for the progress and prospective changes. Additionally, the potential application of the automated design and discovery are discussed for the other well‐known electrocatalytic processes.

How Can We Ensure Reproducibility and Clinical Translation of Machine Learning Applications in Laboratory Medicine?

Recent studies demonstrating problems with COVID (1) and sepsis (2) prediction models have helped raise awareness (3) about the need for better practices in developing and reporting machine learning (ML; or artificial intelligence) methods in healthcare. This so-called reproducibility (or replication) crisis has been recognized and extends beyond clinical applications (4). In fact, this issue is not specific to ML. Many scientific journals, including Clinical Chemistry, have adopted principles (specified in the article submission guidelines) to facilitate reproducibility, rigor, and transparency in published findings. Although there are common elements that support transparency and rigor in science, each technology has its own set of pitfalls that must be addressed. This is true for the rapidly developing field of ML. The growth and development of open-source software and digitized and publicly available data sources have made the application of ML methods highly accessible. While this has facilitated a surge in interest and publications, it has reduced the requirement for developers to have the necessary foundational or subject matter knowledge needed for quality publications and innovations. When ML methods are published in clinical journals, peer reviewers and editors may lack the expertise to appropriately evaluate technical aspects of submissions. Thus, we need best practices that help educate clinical experts and govern how ML for laboratory medicine should be developed and communicated. This is critical for ensuring practicality and reproducibility of ML applications and, ultimately, their successful translation to clinical practice.

Applications of Machine Learning in Antenna Array Processing [Book/Software Reviews

This book is intended to be a practical guide for students, engineers, and researchers in electromagnetics with minimal background in ML. It can also serve as a basic reference book for ML algorithms, advanced topics of electromagnetics, and applications of ML in antenna array processing. The book is divided into two parts. The first part contains five chapters, which cover the theoretical principles of the most common ML architectures and algorithms used today in machines, kernel methods for antenna array, neural networks, and deep learning for computational electromagnetics. The second part consists of four chapters that provide detailed applications of the algorithms of electromagnetic problems, such as in antenna array beamforming, angle of arrival detection, computational electromagnetics, antenna optimization, reconfigurable antennas, cognitive radio, and other aspects of electromagnetic design. This book is the front runner of ML as it applies to electromagnetics and antenna array processing. It requires extensive mathematics and statistics skills and knowledge by the reader to read, understand, and use this book. The original graphics and charts were in color, with explanations of the charts based on the color in the charts, but the color has been removed in the published copy, which may cause the reader some confusion.

Application of Machine Learning in Drug Discovery and Development Lifecycle

Machine learning and Artificial Intelligence have significantly advanced in recent years owing to their potential to considerably increase the quality of life while reducing human workload. The paper demonstrates how AI and ML are used in the drug development process to shorten and enhance the overall timeline. It contains pertinent information on a variety of Machine Learning approaches and algorithms that are used across the whole drug development process to speed up research, save expenses, and reduce risks related to clinical trials. A range of QSAR analysis, hit finding, and de novo drug design applications are used in the pharmaceutical industry to enhance decision-making. As technologies like high-throughput screening and computation analysis of databases used for lead and target identification and development create and integrate vast volumes of data, machine learning and deep learning have grown in importance. It has also been emphasized how these cognitive models and tools may be used in lead creation, optimization, and thorough virtual screening. In this paper, problem statements and the corresponding state-of-the-art models have been considered for target validation, prognostic biomarkers, and digital pathology. Machine Learning models play a vital role in the various operations related to clinical trials embracing protocol optimization, participant management, data analysis and storage, clinical trial data verification, and surveillance. Post-development drug monitoring and unique industrially prevalent ML applications of pharmacovigilance have also been discussed. As a result, the goal of this study is to investigate the machine learning and deep learning algorithms utilised across the drug development lifecycle as well as the supporting techniques that have the potential to be useful.

A review on remote sensing imagery augmentation using deep learning

The quantity of remote sensing pictures being captured is rising dramatically due to advancements in earth observation technology, bringing remote sensing in a long and distinct period of history of big data. Discovering how to successfully harvest this enormous intensity of remote sensing data will be a new challenge. With deep learning, remote sensing data analysis may take on a narrative tone. CNNs are a Deep Learning model capable of directly extracting features from large volumes of picture data and are effective at exploiting realistic imagery data characteristics. With regards to computer vision, CNNs have shown to be highly successful. CNNs are a powerful tool. There have been several studies employing CNNs for remote sensing picture classification in the last few years, and these studies show that CNNs can produce fast and accurate results. It is the goal of this study to offer an overview of the most current and advanced applications of deep learning based on CNNs for remote sensing picture enhancement. To begin, the concepts and features of convolutional neural networks (CNNs) will be briefly reviewed in this review. There are new innovations and structural enhancements to neural network models that make them more suited for RS picture enhancement, as well as data for remote sensing image categorization and augmentation approaches that may be obtained easily and quickly.. As a result of the survey, we expect remote sensing scientists will be better equipped to tackle categorization challenges utilizing cutting-edge deep learning algorithms and methodologies. To increase the number of training examples with a considerable reduction in model over-fitting, data augmentation is a well-known approach used in most ML & AI applications.

Machine Learning in Pituitary Surgery.

Machine learning applications in neurosurgery are increasingly reported for diverse tasks such as faster and more accurate preoperative diagnosis, enhanced lesion characterization, as well as surgical outcome, complications and healthcare cost prediction. Even though the pertinent literature in pituitary surgery is less extensive with respect to other neurosurgical diseases, past research attempted to answer clinically relevant questions to better assist surgeons and clinicians. In the present chapter we review reported ML applications in pituitary surgery including differential diagnosis, preoperative lesion characterization (immunohistochemistry, cavernous sinus invasion, tumor consistency), surgical outcome and complication predictions (gross total resection, tumor recurrence, and endocrinological remission, cerebrospinal fluid leak, postoperative hyponatremia). Moreover, we briefly discuss from a practical standpoint the current barriers to clinical translation of machine learning research. On the topic of pituitary surgery, published reports can be considered mostly preliminary, requiring larger training populations and strong external validation. Thoughtful selection of clinically relevant outcomes of interest and transversal application of model development pipeline-together with accurate methodological planning and multicenter collaborations-have the potential to overcome current limitations and ultimately provide additional tools for more informed patient management.

NIMG-17. SYSTEMATIC REVIEW OF LITERATURE EVALUATING MACHINE LEARNING ALGORITHMS TO DEVELOP OUTCOME PREDICTION MODELS IN GLIOMA USING MOLECULAR IMAGING WITH AMINO ACID PET

Abstract PURPOSE Machine learning (ML) algorithms demonstrate accurate prediction of tumor segmentation, molecular pathology, and outcomes in gliomas using MRI and recently application of ML tools has expanded into molecular imaging with PET. We performed a systematic review to evaluate the role and applications of ML in characterization of gliomas with PET. METHODS Four databases were searched by medical school librarian and confirmed by an independent librarian: Ovid Embase, Ovid MEDLINE, Cochrane trials (CENTRAL), and Web of Science-Core Collection. The search strategy used keywords and controlled vocabulary combining the terms for: artificial intelligence, machine learning, deep learning, radiomics, magnetic resonance imaging, glioma, and related terms. All articles were reviewed by at least 2 independent reviewers at abstract screening, full text review, data extraction, and bias analysis using TRIPOD. RESULTS An initial 11,727 publications were imported to Covidence for screening. After review, 1135 studies moved to full-text review and 715 articles were included. Twelve publications included PET imaging of gliomas. All publications used single-center databases (3-73 patients) with distribution of tracers being [18F]-FDG (1), [18F]-FET (6), [11C]-MET (3), [18F]-FDOPA (1), and [18F]-AMP (1). All but 2 papers used supervised machine learning algorithms. Number of features ranged from 4-19,284. Nine papers manually extracted semiquantitative features TBRmax, TBRmean, SUV, TTP, in addition to demographics. Study outcomes included prediction of treatment response, survival, molecular subtypes, tumor grade, segmentation, and accuracy of image fusion. Accuracy ranged from 0.64-0.95 with AUC 0.43-0.9. CONCLUSION ML can be used on small datasets of PET imaging of brain tumors. While majority of the clinical scans are performed with FDG-PET, the machine learning approaches are being applied to mostly amino acid tracers. Extending ML approaches to FDG-PET, which is more common in clinical practice, is recommended. Overall, ML has potential as a useful tool for predicting patient outcomes and improving image postprocessing.

NIMG-23. MACHINE LEARNING METHODS IN GLIOMA GRADE PREDICTION: A SYSTEMATIC REVIEW

Abstract PURPOSE Machine learning (ML) technologies have demonstrated highly accurate prediction of glioma grade, though it is unclear which methods and algorithms are superior. We have conducted a systematic review of the literature in order to identify the ML applications most promising for future research and clinical implementation. MATERIALS AND METHODS A literature review, in agreement with PRISMA, was conducted by a university librarian in October 2020 and verified by a second librarian in February 2021 using four databases: Cochrane trials (CENTRAL), Ovid Embase, Ovid MEDLINE, and Web of Science core-collection. Keywords and controlled vocabulary included artificial intelligence, machine learning, deep learning, radiomics, magnetic resonance imaging, glioma, and glioblastoma. Screening of publications was done in Covidence, and TRIPOD was used for bias assessment. RESULTS The search identified 11,727 candidate articles with 1,135 articles undergoing full text review. 86 articles published since 1995 met the criteria for our study. 79% of the articles were published between 2018 and 2020. The average glioma prediction accuracy of the highest performing model in each study was 90% (range: 53% to 100%). The most common algorithm used for cML studies was Support Vector Machine (SVM) and for DL studies was Convolutional Neural Network (CNN). BRATS and TCIA datasets were used in 47% of the studies, with the average patient number of study datasets being 186 (range: 23 to 662). The average number of features used in machine learning prediction was 55 (range: 2 to 580). Classical machine learning (cML) was the primary machine learning model in 68% of studies, with deep learning (DL) used in 32%. CONCLUSIONS Using multimodal sequences in ML methods delivers significantly higher grading accuracies than single sequences. Potential areas of improvement for ML glioma grade prediction studies include increasing sample size, incorporating molecular subtypes, and validating on external datasets.