scholarly journals Machine Learning in Detection and Classification of Leukemia Using Smear Blood Images: A Systematic Review

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Mustafa Ghaderzadeh ◽  
Farkhondeh Asadi ◽  
Azamossadat Hosseini ◽  
Davood Bashash ◽  
Hassan Abolghasemi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Lymphoblastic Leukemia ◽  
Peripheral Blood Smear ◽  
Disease Diagnosis ◽  
Diagnostic Methods ◽  
Study Results ◽  
The Status ◽  
Detection And Diagnosis ◽  
Learning Machine

Introduction. The early detection and diagnosis of leukemia, i.e., the precise differentiation of malignant leukocytes with minimum costs in the early stages of the disease, is a major problem in the domain of disease diagnosis. Despite the high prevalence of leukemia, there is a shortage of flow cytometry equipment, and the methods available at laboratory diagnostic centers are time-consuming. Motivated by the capabilities of machine learning (machine learning (ML)) in disease diagnosis, the present systematic review was conducted to review the studies aiming to discover and classify leukemia by using machine learning. Methods. A systematic search in four databases (PubMed, Scopus, Web of Science, and ScienceDirect) and Google Scholar was performed via a search strategy using Machine Learning (ML), leukemia, peripheral blood smear (PBS) image, detection, diagnosis, and classification as the keywords. Initially, 116 articles were retrieved. After applying the inclusion and exclusion criteria, 16 articles remained as the population of the study. Results. This review study presents a comprehensive and systematic view of the status of all published ML-based leukemia detection and classification models that process PBS images. The average accuracy of the ML methods applied in PBS image analysis to detect leukemia was >97%, indicating that the use of ML could lead to extraordinary outcomes in leukemia detection from PBS images. Among all ML techniques, deep learning (DL) achieved higher precision and sensitivity in detecting different cases of leukemia, compared to its precedents. ML has many applications in analyzing different types of leukemia images, but the use of ML algorithms to detect acute lymphoblastic leukemia (ALL) has attracted the greatest attention in the fields of hematology and artificial intelligence. Conclusion. Using the ML method to process leukemia smear images can improve accuracy, reduce diagnosis time, and provide faster, cheaper, and safer diagnostic services. In addition to the current diagnostic methods, clinical and laboratory experts can also adopt ML methods in laboratory applications and tools.

scholarly journals Regulatory-approved Deep Learning/Machine Learning-Based Medical Devices in Japan as of 2020: A Systematic Review

2021 ◽  
Author(s):  
Nao Aisu ◽  
Masahiro Miyake ◽  
Kohei Takeshita ◽  
Masato Akiyama ◽  
Ryo Kawasaki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Deep Learning ◽  
Medical Devices ◽  
The Public ◽  
Search Service ◽  
Public Announcements ◽  
The Status ◽  
Learning Machine ◽  
E Mail

AbstractMachine learning (ML) and deep learning (DL) are changing the world and reshaping the medical field. Thus, we conducted a systematic review to determine the status of regulatory-approved ML/DL-based medical devices in Japan, a leading stakeholder in international regulatory harmonization. Information about the medical devices were obtained from the Japan Association for the Advancement of Medical Equipment search service. The usage of ML/DL methodology in the medical devices was confirmed using public announcements or by contacting the marketing authorization holders via e-mail when the public announcements were insufficient for confirmation. Among the 114,150 medical devices found, 11 were regulatory-approved ML/DL-based Software as a Medical Device (SaMD), with 6 products (54.5%) related to radiology and 5 products (45.5%) related to gastroenterology. The domestic ML/DL-based SaMD were mostly related to health check-ups, which are common in Japan. Our review provides a global overview that can foster international competitiveness and further tailored advancements.

scholarly journals Iron overload during the treatment of acute leukemia: pretransplant transfusion experience

2021 ◽  
Author(s):  
Osman Yokus ◽  
Celalettin Herek ◽  
Tahir Alper Cinli ◽  
Hasan Goze ◽  
Istemi Serin
Keyword(s):  
Acute Leukemia ◽  
Iron Overload ◽  
Myeloid Leukemia ◽  
Lymphoblastic Leukemia ◽  
Risk Of Mortality ◽  
Increased Risk ◽  
Study Results ◽  
The Status ◽  
The Difference ◽  
Correct Point

Background: Recent studies have shown the increased risk of mortality in cases with acute leukemia and iron overload. We aimed to determine the status of iron overload in patients with acute leukemia. Materials & Methods: Patients diagnosed with acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) between January 2015 and December 2019 were included in the study. Results: At 6 months, there were statistically more patients with serum ferritin >1000 in the AML group compared to the ALL group (p = 0,011). Conclusion: Iron overload occurs earlier in patients with AML; the difference disappears after 6 months of treatment. It is the correct point to emphasize that iron overload is an important factor of pretransplant morbidity, especially in AML cases.

scholarly journals The Role of Machine Learning Techniques to Tackle COVID-19 Crisis: A Systematic Review.

2020 ◽  
Author(s):  
Hafsa Bareen Syeda ◽  
Mahanazuddin Syed ◽  
Kevin Wayne Sexton ◽  
Shorabuddin Syed ◽  
Salma Begum ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Disease Progression ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Data Types ◽  
Detection And Diagnosis ◽  
Novel Coronavirus ◽  
The Impact

Background: The novel coronavirus responsible for COVID-19 has caused havoc with patients presenting a spectrum of complications forcing the healthcare experts around the globe to explore new technological solutions, and treatment plans. Machine learning (ML) based technologies have played a substantial role in solving complex problems, and several organizations have been swift to adopt and customize them in response to the challenges posed by the COVID-19 pandemic. Objective: The objective of this study is to conduct a systematic literature review on the role of ML as a comprehensive and decisive technology to fight the COVID-19 crisis in the arena of epidemiology, diagnosis, and disease progression. Methods: A systematic search in PubMed, Web of Science, and CINAHL databases was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines to identify all potentially relevant studies published and made available between December 1, 2019, and June 27, 2020. The search syntax was built using keywords specific to COVID-19 and ML. A total of 128 qualified articles were reviewed and analyzed based on the study objectives. Results: The 128 publications selected were classified into three themes based on ML applications employed to combat the COVID-19 crisis: Computational Epidemiology (CE), Early Detection and Diagnosis (EDD), and Disease Progression (DP). Of the 128 studies, 70 focused on predicting the outbreak, the impact of containment policies, and potential drug discoveries, which were grouped into the CE theme. For the EDD, we grouped forty studies that applied ML techniques to detect the presence of COVID-19 using the patient's radiological images or lab results. Eighteen publications that focused on predicting the disease progression, outcomes (recovery and mortality), Length of Stay (LOS), and number of Intensive Care Unit (ICU) days for COVID-19 positive patients were classified under the DP theme. Conclusions: In this systematic review, we assembled the current COVID-19 literature that utilized ML methods to provide insights into the COVID-19 themes, highlighting the important variables, data types, and available COVID-19 resources that can assist in facilitating clinical and translational research.

scholarly journals New method for rapid diagnosis of Hepatitis disease based on reduction feature and machine learning

2015 ◽  
Vol 4 (1) ◽  
pp. 148
Author(s):  
Nahid Khorashadizade ◽  
Hassan Rezaei
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Classification Accuracy ◽  
Principal Component ◽  
Disease Diagnosis ◽  
Rapid Diagnosis ◽  
Machine Learning Algorithms ◽  
University Of California ◽  
Learning Machine ◽  
Two Stages

<p>Hepatitis disease is caused by liver injury. Rapid diagnosis of this disease prevents its development and suffering to cirrhosis of the liver. Data mining is a new branch of science that helps physicians for proper decision making. In data mining using reduction feature and machine learning algorithms are useful for reducing the complexity of the problem and method of disease diagnosis, respectively. In this study, a new algorithm is proposed for hepatitis diagnosis according to Principal Component Analysis (PCA) and Error Minimized Extreme Learning Machine (EMELM). The algorithm includes two stages; in reduction feature phase, missing records were deleted and hepatitis dataset was normalized in [0,1] range. Thereafter, analysis of the principal component was applied for reduction feature. In classification phase, the reduced dataset is classified using EMELM. For evaluation of the algorithm, hepatitis disease dataset from UCI Machine Learning Repository (University of California) was selected. The features of this dataset reduced from 19 to 6 using PCA and the accuracy of the reduced dataset was obtained using EMELM. The results revealed that the proposed hybrid intelligent diagnosis system reached the higher classification accuracy and shorter time compared with other methods.<strong></strong></p>

scholarly journals Deep Learning in the Detection and Diagnosis of COVID-19 Using Radiology Modalities: A Systematic Review

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Mustafa Ghaderzadeh ◽  
Farkhondeh Asadi
Keyword(s):  
Systematic Review ◽  
Deep Learning ◽  
Diagnostic Methods ◽  
Efficient System ◽  
Current State ◽  
Radiological Images ◽  
Research Population ◽  
Radiologic Image ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis

Introduction. The early detection and diagnosis of COVID-19 and the accurate separation of non-COVID-19 cases at the lowest cost and in the early stages of the disease are among the main challenges in the current COVID-19 pandemic. Concerning the novelty of the disease, diagnostic methods based on radiological images suffer from shortcomings despite their many applications in diagnostic centers. Accordingly, medical and computer researchers tend to use machine-learning models to analyze radiology images. Material and Methods. The present systematic review was conducted by searching the three databases of PubMed, Scopus, and Web of Science from November 1, 2019, to July 20, 2020, based on a search strategy. A total of 168 articles were extracted and, by applying the inclusion and exclusion criteria, 37 articles were selected as the research population. Result. This review study provides an overview of the current state of all models for the detection and diagnosis of COVID-19 through radiology modalities and their processing based on deep learning. According to the findings, deep learning-based models have an extraordinary capacity to offer an accurate and efficient system for the detection and diagnosis of COVID-19, the use of which in the processing of modalities would lead to a significant increase in sensitivity and specificity values. Conclusion. The application of deep learning in the field of COVID-19 radiologic image processing reduces false-positive and negative errors in the detection and diagnosis of this disease and offers a unique opportunity to provide fast, cheap, and safe diagnostic services to patients.

scholarly journals Lactic acid-containing products for bacterial vaginosis and their impact on the vaginal microbiota: A systematic review

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246953 ◽  
Author(s):  
Erica L. Plummer ◽  
Catriona S. Bradshaw ◽  
Michelle Doyle ◽  
Christopher K. Fairley ◽  
Gerald L. Murray ◽  
...  
Keyword(s):  
Systematic Review ◽  
Lactic Acid ◽  
Bacterial Vaginosis ◽  
Vaginal Microbiota ◽  
Diagnostic Methods ◽  
Control Group ◽  
Microbiota Composition ◽  
Randomised Trials ◽  
High Quality Evidence ◽  
Study Results

Objective The vaginal microbiota in bacterial vaginosis (BV) typically has low abundance of lactic acid producing lactobacilli. Lactic acid has properties that may make it effective for treating BV and/or restoring an optimal lactobacillus-dominated vaginal microbiota. We conducted a systematic review to describe the effect of intravaginal lactic acid-containing products on BV cure, and their impact on vaginal microbiota composition (PROSPERO registration: CRD42018115982). Methods PubMed, Embase and OVID were searched from inception to November 2019 to identify eligible studies. Included studies evaluated an intravaginal lactic acid-containing product and reported BV cure using established diagnostic methods, and/or vaginal microbiota composition using molecular methods. Studies were independently screened and assessed, and the proportion of women cured post-treatment was calculated. Study results were described in a qualitative manner. Results We identified 1,883 articles and assessed 57 full-texts for eligibility. Seven different lactic acid-containing products were evaluated and differed with respect to excipients, lactic acid concentration and pH. Most studies had medium or high risk of bias. Three trials compared the efficacy of a lactic acid-containing product to metronidazole for BV cure. One study found lactic acid to be equivalent to metronidazole and two studies found lactic acid to be significantly inferior to metronidazole. Two studies included a control group receiving a placebo or no treatment. One reported lactic acid to be superior than no treatment and the other reported lactic acid to be equivalent to placebo. Lactic acid-containing products did not significantly impact the vaginal microbiota composition. Conclusion There is a lack of high-quality evidence to support the use of lactic acid-containing products for BV cure or vaginal microbiota modulation. However, adequately powered and rigorous randomised trials with accompanying vaginal microbiota data are needed to evaluate the efficacy of lactic acid as a BV treatment strategy.

scholarly journals The Role of Machine Learning Techniques to Tackle COVID-19 Crisis: A Systematic Review. (Preprint)

2020 ◽  
Author(s):  
Hafsa Bareen Syeda ◽  
Mahanazuddin Syed ◽  
Kevin Wayne Sexton ◽  
Shorabuddin Syed ◽  
Salma Begum ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Disease Progression ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Data Types ◽  
Detection And Diagnosis ◽  
Novel Coronavirus ◽  
The Impact

BACKGROUND The novel coronavirus responsible for COVID-19 has caused havoc with patients presenting a spectrum of complications forcing the healthcare experts around the globe to explore new technological solutions, and treatment plans. Machine learning (ML) based technologies have played a substantial role in solving complex problems, and several organizations have been swift to adopt and customize them in response to the challenges posed by the COVID-19 pandemic. OBJECTIVE The objective of this study is to conduct a systematic literature review on the role of ML as a comprehensive and decisive technology to fight the COVID-19 crisis in the arena of epidemiology, diagnosis, and disease progression. METHODS A systematic search in PubMed, Web of Science, and CINAHL databases was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines to identify all potentially relevant studies published and made available between December 1, 2019, and June 27, 2020. The search syntax was built using keywords specific to COVID-19 and ML. A total of 128 qualified articles were reviewed and analyzed based on the study objectives. RESULTS The 128 publications selected were classified into three themes based on ML applications employed to combat the COVID-19 crisis: Computational Epidemiology (CE), Early Detection and Diagnosis (EDD), and Disease Progression (DP). Of the 128 studies, 70 focused on predicting the outbreak, the impact of containment policies, and potential drug discoveries, which were grouped into the CE theme. For the EDD, we grouped forty studies that applied ML techniques to detect the presence of COVID-19 using the patient's radiological images or lab results. Eighteen publications that focused on predicting the disease progression, outcomes (recovery and mortality), Length of Stay (LOS), and number of Intensive Care Unit (ICU) days for COVID-19 positive patients were classified under the DP theme. CONCLUSIONS In this systematic review, we assembled the current COVID-19 literature that utilized ML methods to provide insights into the COVID-19 themes, highlighting the important variables, data types, and available COVID-19 resources that can assist in facilitating clinical and translational research.

scholarly journals Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 241
Author(s):  
Vivek Lahoura ◽  
Harpreet Singh ◽  
Ashutosh Aggarwal ◽  
Bhisham Sharma ◽  
Mazin Abed Mohammed ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cloud Computing ◽  
Extreme Learning Machine ◽  
Feature Selection Method ◽  
Breast Cancer Diagnosis ◽  
Disease Diagnosis ◽  
Classification Problems ◽  
Risk Of Death ◽  
Learning Machine

Globally, breast cancer is one of the most significant causes of death among women. Early detection accompanied by prompt treatment can reduce the risk of death due to breast cancer. Currently, machine learning in cloud computing plays a pivotal role in disease diagnosis, but predominantly among the people living in remote areas where medical facilities are scarce. Diagnosis systems based on machine learning act as secondary readers and assist radiologists in the proper diagnosis of diseases, whereas cloud-based systems can support telehealth services and remote diagnostics. Techniques based on artificial neural networks (ANN) have attracted many researchers to explore their capability for disease diagnosis. Extreme learning machine (ELM) is one of the variants of ANN that has a huge potential for solving various classification problems. The framework proposed in this paper amalgamates three research domains: Firstly, ELM is applied for the diagnosis of breast cancer. Secondly, to eliminate insignificant features, the gain ratio feature selection method is employed. Lastly, a cloud computing-based system for remote diagnosis of breast cancer using ELM is proposed. The performance of the cloud-based ELM is compared with some state-of-the-art technologies for disease diagnosis. The results achieved on the Wisconsin Diagnostic Breast Cancer (WBCD) dataset indicate that the cloud-based ELM technique outperforms other results. The best performance results of ELM were found for both the standalone and cloud environments, which were compared. The important findings of the experimental results indicate that the accuracy achieved is 0.9868, the recall is 0.9130, the precision is 0.9054, and the F1-score is 0.8129.

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