scholarly journals Automatic Pulmonary Nodule Detection Applying Deep Learning or Machine Learning Algorithms to the LIDC-IDRI Database: A Systematic Review

Diagnostics ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 29 ◽  
Author(s):  
Lea Pehrson ◽  
Michael Nielsen ◽  
Carsten Ammitzbøl Lauridsen
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Deep Learning ◽  
Machine Learning Algorithms ◽  
Ct Scans ◽  
Lung Nodules ◽  
Original Research ◽  
Feature Based ◽  
High Level ◽  
Meta Analyses

The aim of this study was to provide an overview of the literature available on machine learning (ML) algorithms applied to the Lung Image Database Consortium Image Collection (LIDC-IDRI) database as a tool for the optimization of detecting lung nodules in thoracic CT scans. This systematic review was compiled according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Only original research articles concerning algorithms applied to the LIDC-IDRI database were included. The initial search yielded 1972 publications after removing duplicates, and 41 of these articles were included in this study. The articles were divided into two subcategories describing their overall architecture. The majority of feature-based algorithms achieved an accuracy >90% compared to the deep learning (DL) algorithms that achieved an accuracy in the range of 82.2%–97.6%. In conclusion, ML and DL algorithms are able to detect lung nodules with a high level of accuracy, sensitivity, and specificity using ML, when applied to an annotated archive of CT scans of the lung. However, there is no consensus on the method applied to determine the efficiency of ML algorithms.

scholarly journals Machine Learning Algorithms in Cardiology Domain: A Systematic Review (Preprint)

2019 ◽  
Author(s):  
Georgy Kopanitsa ◽  
Aleksei Dudchenko ◽  
Matthias Ganzinger
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Search Strategy ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Learning System ◽  
Prisma Statement ◽  
Meta Analyses ◽  
Conference Papers ◽  
Huge Variety

BACKGROUND It has been shown in previous decades, that Machine Learning (ML) has a huge variety of possible implementations in medicine and can be very helpful. Neretheless, cardiovascular diseases causes about third of of all global death. Does ML work in cardiology domain and what is current progress in that regard? OBJECTIVE The review aims at (1) identifying studies where machine-learning algorithms were applied in the cardiology domain; (2) providing an overview based on identified literature of the state of the art of the ML algorithm applying in cardiology. METHODS For organizing this review, we have employed PRISMA statement. PRISMA is a set of items for reporting in systematic reviews and meta-analyses, focused on the reporting of reviews evaluating randomized trials, but can also be used as a basis for reporting systematic review. For the review, we have adopted PRISMA statement and have identified the following items: review questions, information sources, search strategy, selection criteria. RESULTS In total 27 scientific articles or conference papers written in English and reporting about implementation of an ML-method or algorithm in cardiology domain were included in this review. We have examined four aspects: aims of ML-systems, methods, datasets and evaluation metrics. CONCLUSIONS We suppose, this systematic review will be helpful for researchers developing machine-learning system for a medical domain and in particular for cardiology.

scholarly journals OTHR-12. The development of machine learning algorithms for the differentiation of glioma and brain metastases – a systematic review

2021 ◽  
Vol 3 (Supplement_3) ◽  
pp. iii17-iii17
Author(s):  
Waverly Rose Brim ◽  
Leon Jekel ◽  
Gabriel Cassinelli Petersen ◽  
Harry Subramanian ◽  
Tal Zeevi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Deep Learning ◽  
Brain Metastases ◽  
Search Strategy ◽  
Controlled Vocabulary ◽  
Machine Learning Algorithms ◽  
Average Score ◽  
Support Vector ◽  
Therapeutic Decisions

Abstract Purpose Medical staging, surgical planning, and therapeutic decisions are significantly different for brain metastases versus gliomas. Machine learning (ML) algorithms have been developed to differentiate these pathologies. We performed a systematic review to characterize ML methods and to evaluate their accuracy. Methods Studies on the application of machine learning in neuro-oncology were searched in Ovid Embase, Ovid MEDLINE, Cochrane trials (CENTRAL) and Web of science core-collection. A search strategy was designed in compliance with a clinical librarian and confirmed by a second librarian. The search strategy comprised of controlled vocabulary including artificial intelligence, machine learning, deep learning, magnetic resonance imaging, and glioma. The initial search was performed in October 2020 and then updated in February 2021. Candidate articles were screened in Covidence by at least two reviewers each. A bias analysis was conducted in agreement with TRIPOD, a bias assessment tool similar to CLAIM. Results Twenty-nine articles were used for data extraction. Four articles specified model development for solitary brain metastases. Classical ML (cML) algorithms represented 85% of models used, while deep learning (DL) accounted for 15%. cML algorithms performed with an average accuracy, sensitivity, and specificity of 82%, 78%, 88%, respectively; DL performed 84%, 79%, 81%. The support vector machine (SVM) algorithm was the most common used cML model in the literature and convolutional neural networks (CNN) were standard for DL models. We also found T1, T1 post-gadolinium and T2 sequences were most commonly used for feature extraction. Preliminary TRIPOD analysis yielded an average score of 14.25 (range 8–18). Conclusion ML algorithms that can accurately classify glioma from brain metastases have been developed. SVM and CNN are leading approaches with high accuracy. Standardized algorithm performance reporting is a clear limitation to be addressed in future studies.

scholarly journals Machine Learning Approaches for Activity Recognition and/or Activity Prediction in Locomotion Assistive Devices—A Systematic Review

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6345
Author(s):  
Floriant Labarrière ◽  
Elizabeth Thomas ◽  
Laurine Calistri ◽  
Virgil Optasanu ◽  
Mathieu Gueugnon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Learning Algorithms ◽  
Assistive Devices ◽  
Machine Learning Algorithms ◽  
Assistive Device ◽  
Learning Approaches ◽  
Study Characteristics ◽  
Locomotion Mode ◽  
Meta Analyses

Locomotion assistive devices equipped with a microprocessor can potentially automatically adapt their behavior when the user is transitioning from one locomotion mode to another. Many developments in the field have come from machine learning driven controllers on locomotion assistive devices that recognize/predict the current locomotion mode or the upcoming one. This review synthesizes the machine learning algorithms designed to recognize or to predict a locomotion mode in order to automatically adapt the behavior of a locomotion assistive device. A systematic review was conducted on the Web of Science and MEDLINE databases (as well as in the retrieved papers) to identify articles published between 1 January 2000 to 31 July 2020. This systematic review is reported in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines and is registered on Prospero (CRD42020149352). Study characteristics, sensors and algorithms used, accuracy and robustness were also summarized. In total, 1343 records were identified and 58 studies were included in this review. The experimental condition which was most often investigated was level ground walking along with stair and ramp ascent/descent activities. The machine learning algorithms implemented in the included studies reached global mean accuracies of around 90%. However, the robustness of those algorithms seems to be more broadly evaluated, notably, in everyday life. We also propose some guidelines for homogenizing future reports.

scholarly journals Artificial Intelligence and Reflections from Educational Landscape: A Review of AI Studies in Half a Century

2021 ◽  
Vol 13 (2) ◽  
pp. 800
Author(s):  
Aras Bozkurt ◽  
Abdulkadir Karadeniz ◽  
David Baneres ◽  
Ana Elena Guerrero-Roldán ◽  
M. Elena Rodríguez
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Machine Learning ◽  
Systematic Review ◽  
Deep Learning ◽  
Adaptive Learning ◽  
Research Area ◽  
Learning Processes ◽  
Machine Learning Algorithms ◽  
Research Themes

Artificial intelligence (AI) has penetrated every layer of our lives, and education is not immune to the effects of AI. In this regard, this study examines AI studies in education in half a century (1970–2020) through a systematic review approach and benefits from social network analysis and text-mining approaches. Accordingly, the research identifies three research clusters (1) artificial intelligence, (2) pedagogical, and (3) technological issues, and suggests five broad research themes which are (1) adaptive learning and personalization of education through AI-based practices, (2) deep learning and machine Learning algorithms for online learning processes, (3) Educational human-AI interaction, (4) educational use of AI-generated data, and (5) AI in higher education. The study also highlights that ethics in AI studies is an ignored research area.

Machine Learning in Otolaryngology-Head and Neck surgery: A Systematic Review Protocol

2020 ◽  
Author(s):  
Chang Woo Lee ◽  
Angelos Mantelakis ◽  
Bhavesh Vijay Tailor ◽  
Ankur Khajuria
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Head And Neck ◽  
Neck Surgery ◽  
Head And Neck Surgery ◽  
Machine Learning Algorithms ◽  
Original Research ◽  
Learning Approaches ◽  
Operative Outcomes ◽  
Applications Of Machine Learning

Abstract Background: Machine learning describes a subfield of artificial intelligence which utilises statistical algorithms to identify patterns in large datasets. Based on previous learning, inferences or predictions can be made given novel data. Alongside its promising potential to revolutionise consumer technology, there has been growing interest in the application of machine learning algorithms to medical practice. The aim of this study is to evaluate the applications of machine learning in Otolaryngology-Head and Neck surgery.Methods: A systematic search of EMBASE, MEDLINE and CENTRAL will be conducted from January 1990 to June 2020. Studies utilising machine learning as a tool for diagnosis, or to predict disease prognosis or post-operative outcomes in the field of Otolaryngology-Head and Neck surgery will be included. The primary outcome of interest is the accuracy of machine learning models for clinical diagnosis, disease prognostication, and in predicting post-operative outcomes. This protocol adheres to the Preferred Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) guidelines.Discussion: To our knowledge, this will be the first systematic review to assimilate and critically appraise original research on the applications of machine learning across the field of Otolaryngology-Head and Neck surgery. This review has the potential to inform the current state of this technology and guide future study of machine learning approaches within the specialty.Systematic review registration: PROSPERO CRD42020192493

Machine learning and deep learning approaches for classification of sub-cm lung nodules in CT scans (Conference Presentation)

2020 ◽  
Author(s):  
Rohan Abraham ◽  
Ian Janzen ◽  
Saeed Seyyedi ◽  
Sukhinder Khattra ◽  
John Mayo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Ct Scans ◽  
Lung Nodules ◽  
Learning Approaches

scholarly journals Recognition of Fine-Grained Walking Patterns Using a Smartwatch with Deep Attentive Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6393
Author(s):  
Hyejoo Kim ◽  
Hyeon-Joo Kim ◽  
Jinyoon Park ◽  
Jeh-Kwang Ryu ◽  
Seung-Chan Kim
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Contextual Information ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Wearable System ◽  
Fine Grained ◽  
Feature Based ◽  
Recent Attention

Generally, people do various things while walking. For example, people frequently walk while looking at their smartphones. Sometimes we walk differently than usual; for example, when walking on ice or snow, we tend to waddle. Understanding walking patterns could provide users with contextual information tailored to the current situation. To formulate this as a machine-learning problem, we defined 18 different everyday walking styles. Noting that walking strategies significantly affect the spatiotemporal features of hand motions, e.g., the speed and intensity of the swinging arm, we propose a smartwatch-based wearable system that can recognize these predefined walking styles. We developed a wearable system, suitable for use with a commercial smartwatch, that can capture hand motions in the form of multivariate timeseries (MTS) signals. Then, we employed a set of machine learning algorithms, including feature-based and recent deep learning algorithms, to learn the MTS data in a supervised fashion. Experimental results demonstrated that, with recent deep learning algorithms, the proposed approach successfully recognized a variety of walking patterns, using the smartwatch measurements. We analyzed the results with recent attention-based recurrent neural networks to understand the relative contributions of the MTS signals in the classification process.

Diagnostic test accuracy for use of machine learning in diagnosis of autism spectrum disorder: A Systematic Review and Meta-Analysis (Preprint)

2019 ◽  
Author(s):  
Sun Jae Moon ◽  
Jin Seub Hwang ◽  
Rajesh Kana ◽  
John Torous ◽  
Jung Won Kim
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Meta Analysis ◽  
Learning Algorithms ◽  
Structural Mri ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
Test Accuracy

BACKGROUND Over the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, its application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder. However, given its complexity and potential clinical implications, there is ongoing need for further research on its accuracy. OBJECTIVE The current study aims to summarize the evidence for the accuracy of use of machine learning algorithms in diagnosing autism spectrum disorder (ASD) through systematic review and meta-analysis. METHODS MEDLINE, Embase, CINAHL Complete (with OpenDissertations), PsyINFO and IEEE Xplore Digital Library databases were searched on November 28th, 2018. Studies, which used a machine learning algorithm partially or fully in classifying ASD from controls and provided accuracy measures, were included in our analysis. Bivariate random effects model was applied to the pooled data in meta-analysis. Subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false negative and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw SROC curves, and obtain area under the curve (AUC) and partial AUC. RESULTS A total of 43 studies were included for the final analysis, of which meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural MRI subgroup meta-analysis (12 samples with 1,776 participants) showed the sensitivity at 0.83 (95% CI-0.76 to 0.89), specificity at 0.84 (95% CI -0.74 to 0.91), and AUC/pAUC at 0.90/0.83. An fMRI/deep neural network (DNN) subgroup meta-analysis (five samples with 1,345 participants) showed the sensitivity at 0.69 (95% CI- 0.62 to 0.75), the specificity at 0.66 (95% CI -0.61 to 0.70), and AUC/pAUC at 0.71/0.67. CONCLUSIONS Machine learning algorithms that used structural MRI features in diagnosis of ASD were shown to have accuracy that is similar to currently used diagnostic tools.

scholarly journals Semantic segmentation of PolSAR image data using advanced deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rajat Garg ◽  
Anil Kumar ◽  
Nikunj Bansal ◽  
Manish Prateek ◽  
Shashi Kumar
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Urban Area ◽  
Urban Areas ◽  
Learning Algorithms ◽  
Semantic Segmentation ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Deep Learning Model

AbstractUrban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively.

scholarly journals A Systematic Review on the Application of Machine Learning in Exploiting Mineralogical Data in Mining and Mineral Industry

Minerals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 816
Author(s):  
Mohammad Jooshaki ◽  
Alona Nad ◽  
Simon Michaux
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Raw Materials ◽  
Relevant Literature ◽  
Geological Structure ◽  
Subject Area ◽  
Ore Deposits ◽  
Machine Learning Algorithms ◽  
Global Demand ◽  
Ore Grade

Machine learning is a subcategory of artificial intelligence, which aims to make computers capable of solving complex problems without being explicitly programmed. Availability of large datasets, development of effective algorithms, and access to the powerful computers have resulted in the unprecedented success of machine learning in recent years. This powerful tool has been employed in a plethora of science and engineering domains including mining and minerals industry. Considering the ever-increasing global demand for raw materials, complexities of the geological structure of ore deposits, and decreasing ore grade, high-quality and extensive mineralogical information is required. Comprehensive analyses of such invaluable information call for advanced and powerful techniques including machine learning. This paper presents a systematic review of the efforts that have been dedicated to the development of machine learning-based solutions for better utilizing mineralogical data in mining and mineral studies. To that end, we investigate the main reasons behind the superiority of machine learning in the relevant literature, machine learning algorithms that have been deployed, input data, concerned outputs, as well as the general trends in the subject area.

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