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Sensors (Basel, Switzerland) Mar 2022Software defect prediction studies aim to predict defect-prone components before the testing stage of the software development process. The main benefit of these... (Review)
Review
Software defect prediction studies aim to predict defect-prone components before the testing stage of the software development process. The main benefit of these prediction models is that more testing resources can be allocated to fault-prone modules effectively. While a few software defect prediction models have been developed for mobile applications, a systematic overview of these studies is still missing. Therefore, we carried out a Systematic Literature Review (SLR) study to evaluate how machine learning has been applied to predict faults in mobile applications. This study defined nine research questions, and 47 relevant studies were selected from scientific databases to respond to these research questions. Results show that most studies focused on Android applications (i.e., 48%), supervised machine learning has been applied in most studies (i.e., 92%), and object-oriented metrics were mainly preferred. The top five most preferred machine learning algorithms are Naïve Bayes, Support Vector Machines, Logistic Regression, Artificial Neural Networks, and Decision Trees. Researchers mostly preferred Object-Oriented metrics. Only a few studies applied deep learning algorithms including Long Short-Term Memory (LSTM), Deep Belief Networks (DBN), and Deep Neural Networks (DNN). This is the first study that systematically reviews software defect prediction research focused on mobile applications. It will pave the way for further research in mobile software fault prediction and help both researchers and practitioners in this field.
Topics: Algorithms; Bayes Theorem; Machine Learning; Mobile Applications; Neural Networks, Computer; Support Vector Machine
PubMed: 35408166
DOI: 10.3390/s22072551 -
Neurology India 2021Annually, hydrocephalus affects nearly 7 children per 10,000 live births around the world. It significantly impairs the quality of life of such children and is... (Review)
Review
INTRODUCTION
Annually, hydrocephalus affects nearly 7 children per 10,000 live births around the world. It significantly impairs the quality of life of such children and is associated with increased morbidity and mortality The high cost of treatment and post-intervention complications add to the burden of disease. Deployment of machine learning (ML) models in actual clinical settings have led to improved outcomes.
OBJECTIVE
The aim of this systematic review is to analyze the utility as well as acknowledge the achievements of AI/ML in HCP decision making.
METHODOLOGY
PubMed and Cochrane databases were used to perform a systematic search with proper terminology to include all the relevant articles up to May 2021.
RESULTS
Fifteen studies that described the use of ML models in the diagnosis, treatment, and prognostication of pediatric hydrocephalus were identified. The median accuracy of prediction by the ML model in various tasks listed above was found to be 0.88. ML models were most commonly employed for ventricular segmentation for diagnosis of hydrocephalus. The most frequently used model was neural networks. ML models attained faster processing speeds than their manual and non-ML-based automated counterparts.
CONCLUSION
This study attempts to evaluate the important advances and applications of ML in pediatric hydrocephalus. These methods may be better suited for clinical use than manual methods alone due to faster automated processing and near-human accuracy. Future studies should evaluate whether the use of these models is feasible in the future for patient care and management in field settings.
Topics: Child; Databases, Factual; Humans; Hydrocephalus; Machine Learning; Quality of Life
PubMed: 35102993
DOI: 10.4103/0028-3886.332287 -
PloS One 2023This review aimed to summarize the use of machine learning for predicting the potential benefits of stroke rehabilitation treatments, to evaluate the risk of bias of...
OBJECTIVE
This review aimed to summarize the use of machine learning for predicting the potential benefits of stroke rehabilitation treatments, to evaluate the risk of bias of predictive models, and to provide recommendations for future models.
MATERIALS AND METHODS
This systematic review was conducted in accordance with the PRISMA statement and the CHARMS checklist. The PubMed, Embase, Cochrane Library, Scopus, and CNKI databases were searched up to April 08, 2023. The PROBAST tool was used to assess the risk of bias of the included models.
RESULTS
Ten studies within 32 models met our inclusion criteria. The optimal AUC value of the included models ranged from 0.63 to 0.91, and the optimal R2 value ranged from 0.64 to 0.91. All of the included models were rated as having a high or unclear risk of bias, and most of them were downgraded due to inappropriate data sources or analysis processes.
DISCUSSION AND CONCLUSION
There remains much room for improvement in future modeling studies, such as high-quality data sources and model analysis. Reliable predictive models should be developed to improve the efficacy of rehabilitation treatment by clinicians.
Topics: Humans; Stroke; Stroke Rehabilitation; Bias; Machine Learning
PubMed: 37379289
DOI: 10.1371/journal.pone.0287308 -
The British Journal of Surgery Oct 2022Machine learning is a set of models and methods that can automatically detect patterns in vast amounts of data, extract information, and use it to perform...
BACKGROUND
Machine learning is a set of models and methods that can automatically detect patterns in vast amounts of data, extract information, and use it to perform decision-making under uncertain conditions. The potential of machine learning is significant, and breast surgeons must strive to be informed with up-to-date knowledge and its applications.
METHODS
A systematic database search of Embase, MEDLINE, the Cochrane database, and Google Scholar, from inception to December 2021, was conducted of original articles that explored the use of machine learning and/or artificial intelligence in breast surgery in EMBASE, MEDLINE, Cochrane database and Google Scholar.
RESULTS
The search yielded 477 articles, of which 14 studies were included in this review, featuring 73 847 patients. Four main areas of machine learning application were identified: predictive modelling of surgical outcomes; breast imaging-based context; screening and triaging of patients with breast cancer; and as network utility for detection. There is evident value of machine learning in preoperative planning and in providing information for surgery both in a cancer and an aesthetic context. Machine learning outperformed traditional statistical modelling in all studies for predicting mortality, morbidity, and quality of life outcomes. Machine learning patterns and associations could support planning, anatomical visualization, and surgical navigation.
CONCLUSION
Machine learning demonstrated promising applications for improving breast surgery outcomes and patient-centred care. Neveretheless, there remain important limitations and ethical concerns relating to implementing artificial intelligence into everyday surgical practices.
Topics: Artificial Intelligence; Breast Neoplasms; Databases, Factual; Female; Humans; Machine Learning; Quality of Life
PubMed: 35945894
DOI: 10.1093/bjs/znac224 -
Biomedical Engineering Online Dec 2023Artificial intelligence (AI) has shown excellent diagnostic performance in detecting various complex problems related to many areas of healthcare including... (Review)
Review
Artificial intelligence (AI) has shown excellent diagnostic performance in detecting various complex problems related to many areas of healthcare including ophthalmology. AI diagnostic systems developed from fundus images have become state-of-the-art tools in diagnosing retinal conditions and glaucoma as well as other ocular diseases. However, designing and implementing AI models using large imaging data is challenging. In this study, we review different machine learning (ML) and deep learning (DL) techniques applied to multiple modalities of retinal data, such as fundus images and visual fields for glaucoma detection, progression assessment, staging and so on. We summarize findings and provide several taxonomies to help the reader understand the evolution of conventional and emerging AI models in glaucoma. We discuss opportunities and challenges facing AI application in glaucoma and highlight some key themes from the existing literature that may help to explore future studies. Our goal in this systematic review is to help readers and researchers to understand critical aspects of AI related to glaucoma as well as determine the necessary steps and requirements for the successful development of AI models in glaucoma.
Topics: Humans; Artificial Intelligence; Deep Learning; Glaucoma; Machine Learning; Ophthalmology
PubMed: 38102597
DOI: 10.1186/s12938-023-01187-8 -
Surgical Endoscopy Jan 2023Machine learning (ML) has seen an increase in application, and is an important element of a digital evolution. The role of ML within upper gastrointestinal surgery for... (Review)
Review
BACKGROUND
Machine learning (ML) has seen an increase in application, and is an important element of a digital evolution. The role of ML within upper gastrointestinal surgery for malignancies has not been evaluated properly in the literature. Therefore, this systematic review aims to provide a comprehensive overview of ML applications within upper gastrointestinal surgery for malignancies.
METHODS
A systematic search was performed in PubMed, EMBASE, Cochrane, and Web of Science. Studies were only included when they described machine learning in upper gastrointestinal surgery for malignancies. The Cochrane risk-of-bias tool was used to determine the methodological quality of studies. The accuracy and area under the curve were evaluated, representing the predictive performances of ML models.
RESULTS
From a total of 1821 articles, 27 studies met the inclusion criteria. Most studies received a moderate risk-of-bias score. The majority of these studies focused on neural networks (n = 9), multiple machine learning (n = 8), and random forests (n = 3). Remaining studies involved radiomics (n = 3), support vector machines (n = 3), and decision trees (n = 1). Purposes of ML included predominantly prediction of metastasis, detection of risk factors, prediction of survival, and prediction of postoperative complications. Other purposes were predictions of TNM staging, chemotherapy response, tumor resectability, and optimal therapy.
CONCLUSIONS
Machine Learning algorithms seem to contribute to the prediction of postoperative complications and the course of disease after upper gastrointestinal surgery for malignancies. However, due to the retrospective character of ML studies, these results require trials or prospective studies to validate this application of ML.
Topics: Humans; Algorithms; Gastrointestinal Neoplasms; Machine Learning; Prospective Studies; Retrospective Studies
PubMed: 35953684
DOI: 10.1007/s00464-022-09516-z -
Artificial Intelligence in Medicine Dec 2022Machine learning algorithms play an essential role in bioinformatics and allow exploring the vast and noisy biological data in unrivaled ways. This paper is a systematic... (Review)
Review
Machine learning algorithms play an essential role in bioinformatics and allow exploring the vast and noisy biological data in unrivaled ways. This paper is a systematic review of the applications of machine learning in the study of HIV neutralizing antibodies. This significant and vast research domain can pave the way to novel treatments and to a vaccine. We selected the relevant papers by investigating the available literature from the Web of Science and PubMed databases in the last decade. The computational methods are applied in neutralization potency prediction, neutralization span prediction against multiple viral strains, antibody-virus binding sites detection, enhanced antibodies design, and the study of the antibody-induced immune response. These methods are viewed from multiple angles spanning data processing, model description, feature selection, evaluation, and sometimes paper comparisons. The algorithms are diverse and include supervised, unsupervised, and generative types. Both classical machine learning and modern deep learning were taken into account. The review ends with our ideas regarding future research directions and challenges.
Topics: Humans; HIV Antibodies; Machine Learning; HIV-1; Antibodies, Neutralizing; HIV Infections
PubMed: 36462896
DOI: 10.1016/j.artmed.2022.102429 -
International Journal of Medical... May 2023Clinical decision support systems (CDSSs) can provide various functions and advantages to healthcare delivery. Quality healthcare during pregnancy and childbirth is of... (Review)
Review
BACKGROUND
Clinical decision support systems (CDSSs) can provide various functions and advantages to healthcare delivery. Quality healthcare during pregnancy and childbirth is of vital importance, and machine learning-based CDSSs have shown positive impact on pregnancy care.
OBJECTIVE
This paper aims to investigate what has been done in CDSSs in the context of pregnancy care using machine learning, and what aspects require attention from future researchers.
METHODS
We conducted a systematic review of existing literature following a structured process of literature search, paper selection and filtering, and data extraction and synthesis.
RESULTS
17 research papers were identified on the topic of CDSS development for different aspects of pregnancy care using various machine learning algorithms. We discovered an overall lack of explainability in the proposed models. We also observed a lack of experimentation, external validation and discussion around culture, ethnicity and race from the source data, with most studies using data from a single centre or country, and an overall lack of awareness of applicability and generalisability of the CDSSs regarding different populations. Finally, we found a gap between machine learning practices and CDSS implementation, and an overall lack of user testing.
CONCLUSION
Machine learning-based CDSSs are still under-explored in the context of pregnancy care. Despite the open problems that remain, the few studies that tested a CDSS for pregnancy care reported positive effects, reinforcing the potential of such systems to improve clinical practice. We encourage future researchers to take into consideration the aspects we identified in order for their work to translate into clinical use.
Topics: Humans; Pregnancy; Female; Decision Support Systems, Clinical; Delivery of Health Care; Machine Learning; Algorithms; Empirical Research
PubMed: 36907027
DOI: 10.1016/j.ijmedinf.2023.105040 -
Applied Clinical Informatics May 2022As the storage of clinical data has transitioned into electronic formats, medical informatics has become increasingly relevant in providing diagnostic aid. The purpose...
OBJECTIVE
As the storage of clinical data has transitioned into electronic formats, medical informatics has become increasingly relevant in providing diagnostic aid. The purpose of this review is to evaluate machine learning models that use text data for diagnosis and to assess the diversity of the included study populations.
METHODS
We conducted a systematic literature review on three public databases. Two authors reviewed every abstract for inclusion. Articles were included if they used or developed machine learning algorithms to aid in diagnosis. Articles focusing on imaging informatics were excluded.
RESULTS
From 2,260 identified papers, we included 78. Of the machine learning models used, neural networks were relied upon most frequently (44.9%). Studies had a median population of 661.5 patients, and diseases and disorders of 10 different body systems were studied. Of the 35.9% ( = 28) of papers that included race data, 57.1% ( = 16) of study populations were majority White, 14.3% were majority Asian, and 7.1% were majority Black. In 75% ( = 21) of papers, White was the largest racial group represented. Of the papers included, 43.6% ( = 34) included the sex ratio of the patient population.
DISCUSSION
With the power to build robust algorithms supported by massive quantities of clinical data, machine learning is shaping the future of diagnostics. Limitations of the underlying data create potential biases, especially if patient demographics are unknown or not included in the training.
CONCLUSION
As the movement toward clinical reliance on machine learning accelerates, both recording demographic information and using diverse training sets should be emphasized. Extrapolating algorithms to demographics beyond the original study population leaves large gaps for potential biases.
Topics: Algorithms; Humans; Machine Learning
PubMed: 35613914
DOI: 10.1055/s-0042-1749119 -
Radiology Jan 2022Background Advances in computer processing and improvements in data availability have led to the development of machine learning (ML) techniques for mammographic... (Meta-Analysis)
Meta-Analysis
Background Advances in computer processing and improvements in data availability have led to the development of machine learning (ML) techniques for mammographic imaging. Purpose To evaluate the reported performance of stand-alone ML applications for screening mammography workflow. Materials and Methods Ovid Embase, Ovid Medline, Cochrane Central Register of Controlled Trials, Scopus, and Web of Science literature databases were searched for relevant studies published from January 2012 to September 2020. The study was registered with the PROSPERO International Prospective Register of Systematic Reviews (protocol no. CRD42019156016). Stand-alone technology was defined as a ML algorithm that can be used independently of a human reader. Studies were quality assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 and the Prediction Model Risk of Bias Assessment Tool, and reporting was evaluated using the Checklist for Artificial Intelligence in Medical Imaging. A primary meta-analysis included the top-performing algorithm and corresponding reader performance from which pooled summary estimates for the area under the receiver operating characteristic curve (AUC) were calculated using a bivariate model. Results Fourteen articles were included, which detailed 15 studies for stand-alone detection ( = 8) and triage ( = 7). Triage studies reported that 17%-91% of normal mammograms identified could be read by adapted screening, while "missing" an estimated 0%-7% of cancers. In total, an estimated 185 252 cases from three countries with more than 39 readers were included in the primary meta-analysis. The pooled sensitivity, specificity, and AUC was 75.4% (95% CI: 65.6, 83.2; = .11), 90.6% (95% CI: 82.9, 95.0; = .40), and 0.89 (95% CI: 0.84, 0.98), respectively, for algorithms, and 73.0% (95% CI: 60.7, 82.6), 88.6% (95% CI: 72.4, 95.8), and 0.85 (95% CI: 0.78, 0.97), respectively, for readers. Conclusion Machine learning (ML) algorithms that demonstrate a stand-alone application in mammographic screening workflows achieve or even exceed human reader detection performance and improve efficiency. However, this evidence is from a small number of retrospective studies. Therefore, further rigorous independent external prospective testing of ML algorithms to assess performance at preassigned thresholds is required to support these claims. ©RSNA, 2021 See also the editorial by Whitman and Moseley in this issue.
Topics: Breast Neoplasms; Female; Humans; Machine Learning; Mammography; Radiographic Image Interpretation, Computer-Assisted; Sensitivity and Specificity; Workflow
PubMed: 34665034
DOI: 10.1148/radiol.2021210391