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The Science of the Total Environment Jul 2024Microplastics are ubiquitous in the aquatic environment and have emerged as a significant environmental issue due to their potential impacts on human health and the... (Review)
Review
Microplastics are ubiquitous in the aquatic environment and have emerged as a significant environmental issue due to their potential impacts on human health and the ecosystem. Current laboratory-based microplastic detection methods suffer from various drawbacks, including a lack of standardisation, limited spatial and temporal coverage, high costs, and time-consuming procedures. Consequently, there is a need for the development of in-situ techniques to detect and monitor microplastics to effectively identify and understand their sources, pathways, and behaviours. Herein, we adopt a systematic literature review method to assess the development and application of experimental and field technologies designed for the in-situ detection and monitoring of aquatic microplastics, without the need for sample preparation. Four scientific databases were searched in March 2023, resulting in a review of 62 relevant studies. These studies were classified into seven sensor categories and their working principles were discussed. The sensor classes include optical devices, digital holography, Raman spectroscopy, other spectroscopy, hyperspectral imaging, remote sensing, and other methods. We also looked at how data from these technologies are integrated with machine learning models to develop classifiers capable of accurately characterising the physical and chemical properties of microplastics and discriminating them from other particles. This review concluded that in-situ detection of microplastics in aquatic environments is feasible and can be achieved with high accuracy, even though the methods are still in the early stages of development. Nonetheless, further research is still needed to enhance the in-situ detection of microplastics. This includes exploring the possibility of combining various detection methods and developing robust machine-learning classifiers. Additionally, there is a recommendation for in-situ implementation of the reviewed methods to assess their effectiveness in detecting microplastics and identify their limitations.
Topics: Microplastics; Environmental Monitoring; Water Pollutants, Chemical
PubMed: 38740219
DOI: 10.1016/j.scitotenv.2024.173111 -
Journal of Medical Systems May 2024Asthma, a common chronic respiratory disease among children and adults, affects more than 200 million people worldwide and causes about 450,000 deaths each year. Machine...
Asthma, a common chronic respiratory disease among children and adults, affects more than 200 million people worldwide and causes about 450,000 deaths each year. Machine learning is increasingly applied in healthcare to assist health practitioners in decision-making. In asthma management, machine learning excels in performing well-defined tasks, such as diagnosis, prediction, medication, and management. However, there remain uncertainties about how machine learning can be applied to predict asthma exacerbation. This study aimed to systematically review recent applications of machine learning techniques in predicting the risk of asthma attacks to assist asthma control and management. A total of 860 studies were initially identified from five databases. After the screening and full-text review, 20 studies were selected for inclusion in this review. The review considered recent studies published from January 2010 to February 2023. The 20 studies used machine learning techniques to support future asthma risk prediction by using various data sources such as clinical, medical, biological, and socio-demographic data sources, as well as environmental and meteorological data. While some studies considered prediction as a category, other studies predicted the probability of exacerbation. Only a group of studies applied prediction windows. The paper proposes a conceptual model to summarise how machine learning and available data sources can be leveraged to produce effective models for the early detection of asthma attacks. The review also generated a list of data sources that other researchers may use in similar work. Furthermore, we present opportunities for further research and the limitations of the preceding studies.
Topics: Humans; Asthma; Disease Progression; Machine Learning; Risk Assessment
PubMed: 38739297
DOI: 10.1007/s10916-024-02061-3 -
Radiotherapy and Oncology : Journal of... Jul 2024We performed this systematic review and meta-analysis to investigate the performance of ML in detecting genetic mutation status in NSCLC patients. (Meta-Analysis)
Meta-Analysis Review
BACKGROUND AND PURPOSE
We performed this systematic review and meta-analysis to investigate the performance of ML in detecting genetic mutation status in NSCLC patients.
MATERIALS AND METHODS
We conducted a systematic search of PubMed, Cochrane, Embase, and Web of Science up until July 2023. We discussed the genetic mutation status of EGFR, ALK, KRAS, and BRAF, as well as the mutation status at different sites of EGFR.
RESULTS
We included a total of 128 original studies, of which 114 constructed ML models based on radiomic features mainly extracted from CT, MRI, and PET-CT data. From a genetic mutation perspective, 121 studies focused on EGFR mutation status analysis. In the validation set, for the detection of EGFR mutation status, the aggregated c-index was 0.760 (95%CI: 0.706-0.814) for clinical feature-based models, 0.772 (95%CI: 0.753-0.791) for CT-based radiomics models, 0.816 (95%CI: 0.776-0.856) for MRI-based radiomics models, and 0.750 (95%CI: 0.712-0.789) for PET-CT-based radiomics models. When combined with clinical features, the aggregated c-index was 0.807 (95%CI: 0.781-0.832) for CT-based radiomics models, 0.806 (95%CI: 0.773-0.839) for MRI-based radiomics models, and 0.822 (95%CI: 0.789-0.854) for PET-CT-based radiomics models. In the validation set, the aggregated c-indexes for radiomics-based models to detect mutation status of ALK and KRAS, as well as the mutation status at different sites of EGFR were all greater than 0.7.
CONCLUSION
The use of radiomics-based methods for early discrimination of EGFR mutation status in NSCLC demonstrates relatively high accuracy. However, the influence of clinical variables cannot be overlooked in this process. In addition, future studies should also pay attention to the accuracy of radiomics in identifying mutation status of other genes in EGFR.
Topics: Humans; Lung Neoplasms; Machine Learning; Mutation; Carcinoma, Non-Small-Cell Lung; Positron Emission Tomography Computed Tomography; ErbB Receptors; Proto-Oncogene Proteins p21(ras)
PubMed: 38734145
DOI: 10.1016/j.radonc.2024.110325 -
International Journal of Medical... Aug 2024Radiomics is a rapidly growing field used to leverage medical radiological images by extracting quantitative features. These are supposed to characterize a patient's... (Review)
Review
BACKGROUND
Radiomics is a rapidly growing field used to leverage medical radiological images by extracting quantitative features. These are supposed to characterize a patient's phenotype, and when combined with artificial intelligence techniques, to improve the accuracy of diagnostic models and clinical outcome prediction.
OBJECTIVES
This review aims at examining the application areas of artificial intelligence-based radiomics (AI-based radiomics) for the management of head and neck cancer (HNC). It further explores the workflow of AI-based radiomics for personalized and precision oncology in HNC. Finally, it examines the current challenges of AI-based radiomics in daily clinical oncology and offers possible solutions to these challenges.
METHODS
Comprehensive electronic databases (PubMed, Medline via Ovid, Scopus, Web of Science, CINAHL, and Cochrane Library) were searched following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. The quality of included studies and their risk of biases were evaluated using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD)and Prediction Model Risk of Bias Assessment Tool (PROBAST).
RESULTS
Out of the 659 search hits retrieved, 45 fulfilled the inclusion criteria. Our review revealed that the application of AI-based radiomics model as an ancillary tool for improved decision-making in HNC management includes radiomics-based cancer diagnosis and radiomics-based cancer prognosis. The radiomics-based cancer diagnosis includes tumor staging, tumor grading, and classification of malignant and benign tumors. Similarly, radiomics-based cancer prognosis includes prediction for treatment response, recurrence, metastasis, and survival. In addition, the challenges in the implementation of these models for clinical evaluations include data imbalance, feature engineering (extraction and selection), model generalizability, multi-modal fusion, and model interpretability.
CONCLUSION
Considering the highly subjective and interobserver variability that is peculiar to the interpretation of medical images by expert clinicians, AI-based radiomics seeks to offer potentially useful quantitative information, which is not visible to the human eye or unintentionally often remain ignored during clinical imaging practice. By enabling the extraction of this type of information, AI-based radiomics has the potential to revolutionize HNC oncology, providing a platform for more personalized, higher quality, and cost-effective care for HNC patients.
Topics: Humans; Head and Neck Neoplasms; Artificial Intelligence; Precision Medicine; Prognosis; Radiomics
PubMed: 38728812
DOI: 10.1016/j.ijmedinf.2024.105464 -
F1000Research 2024The most recent advances in Computed Tomography (CT) image reconstruction technology are Deep learning image reconstruction (DLIR) algorithms. Due to drawbacks in... (Comparative Study)
Comparative Study
Influence of deep learning image reconstruction algorithm for reducing radiation dose and image noise compared to iterative reconstruction and filtered back projection for head and chest computed tomography examinations: a systematic review.
BACKGROUND
The most recent advances in Computed Tomography (CT) image reconstruction technology are Deep learning image reconstruction (DLIR) algorithms. Due to drawbacks in Iterative reconstruction (IR) techniques such as negative image texture and nonlinear spatial resolutions, DLIRs are gradually replacing them. However, the potential use of DLIR in Head and Chest CT has to be examined further. Hence, the purpose of the study is to review the influence of DLIR on Radiation dose (RD), Image noise (IN), and outcomes of the studies compared with IR and FBP in Head and Chest CT examinations.
METHODS
We performed a detailed search in PubMed, Scopus, Web of Science, Cochrane Library, and Embase to find the articles reported using DLIR for Head and Chest CT examinations between 2017 to 2023. Data were retrieved from the short-listed studies using Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines.
RESULTS
Out of 196 articles searched, 15 articles were included. A total of 1292 sample size was included. 14 articles were rated as high and 1 article as moderate quality. All studies compared DLIR to IR techniques. 5 studies compared DLIR with IR and FBP. The review showed that DLIR improved IQ, and reduced RD and IN for CT Head and Chest examinations.
CONCLUSIONS
DLIR algorithm have demonstrated a noted enhancement in IQ with reduced IN for CT Head and Chest examinations at lower dose compared with IR and FBP. DLIR showed potential for enhancing patient care by reducing radiation risks and increasing diagnostic accuracy.
Topics: Humans; Deep Learning; Radiation Dosage; Tomography, X-Ray Computed; Head; Algorithms; Image Processing, Computer-Assisted; Thorax; Radiography, Thoracic; Signal-To-Noise Ratio
PubMed: 38725640
DOI: 10.12688/f1000research.147345.1 -
Frontiers in Oncology 2024Accurate detection of the histological grade of pancreatic neuroendocrine tumors (PNETs) is important for patients' prognoses and treatment. Here, we investigated the...
BACKGROUND
Accurate detection of the histological grade of pancreatic neuroendocrine tumors (PNETs) is important for patients' prognoses and treatment. Here, we investigated the performance of radiological image-based artificial intelligence (AI) models in predicting histological grades using meta-analysis.
METHOD
A systematic literature search was performed for studies published before September 2023. Study characteristics and diagnostic measures were extracted. Estimates were pooled using random-effects meta-analysis. Evaluation of risk of bias was performed by the QUADAS-2 tool.
RESULTS
A total of 26 studies were included, 20 of which met the meta-analysis criteria. We found that the AI-based models had high area under the curve (AUC) values and showed moderate predictive value. The pooled distinguishing abilities between different grades of PNETs were 0.89 [0.84-0.90]. By performing subgroup analysis, we found that the radiomics feature-only models had a predictive value of 0.90 [0.87-0.92] with I = 89.91%, while the pooled AUC value of the combined group was 0.81 [0.77-0.84] with I = 41.54%. The validation group had a pooled AUC of 0.84 [0.81-0.87] without heterogenicity, whereas the validation-free group had high heterogenicity (I= 91.65%, P=0.000). The machine learning group had a pooled AUC of 0.83 [0.80-0.86] with I = 82.28%.
CONCLUSION
AI can be considered as a potential tool to detect histological PNETs grades. Sample diversity, lack of external validation, imaging modalities, inconsistent radiomics feature extraction across platforms, different modeling algorithms and software choices were sources of heterogeneity. Standardized imaging, transparent statistical methodologies for feature selection and model development are still needed in the future to achieve the transformation of radiomics results into clinical applications.
SYSTEMATIC REVIEW REGISTRATION
https://www.crd.york.ac.uk/prospero/, identifier CRD42022341852.
PubMed: 38725633
DOI: 10.3389/fonc.2024.1332387 -
BMC Medical Research Methodology May 2024Systematic literature reviews (SLRs) are critical for life-science research. However, the manual selection and retrieval of relevant publications can be a time-consuming...
OBJECTIVE
Systematic literature reviews (SLRs) are critical for life-science research. However, the manual selection and retrieval of relevant publications can be a time-consuming process. This study aims to (1) develop two disease-specific annotated corpora, one for human papillomavirus (HPV) associated diseases and the other for pneumococcal-associated pediatric diseases (PAPD), and (2) optimize machine- and deep-learning models to facilitate automation of the SLR abstract screening.
METHODS
This study constructed two disease-specific SLR screening corpora for HPV and PAPD, which contained citation metadata and corresponding abstracts. Performance was evaluated using precision, recall, accuracy, and F1-score of multiple combinations of machine- and deep-learning algorithms and features such as keywords and MeSH terms.
RESULTS AND CONCLUSIONS
The HPV corpus contained 1697 entries, with 538 relevant and 1159 irrelevant articles. The PAPD corpus included 2865 entries, with 711 relevant and 2154 irrelevant articles. Adding additional features beyond title and abstract improved the performance (measured in Accuracy) of machine learning models by 3% for HPV corpus and 2% for PAPD corpus. Transformer-based deep learning models that consistently outperformed conventional machine learning algorithms, highlighting the strength of domain-specific pre-trained language models for SLR abstract screening. This study provides a foundation for the development of more intelligent SLR systems.
Topics: Humans; Machine Learning; Papillomavirus Infections; Economics, Medical; Algorithms; Outcome Assessment, Health Care; Deep Learning; Abstracting and Indexing
PubMed: 38724903
DOI: 10.1186/s12874-024-02224-3 -
NPJ Digital Medicine May 2024Posttraumatic stress disorder (PTSD) recently becomes one of the most important mental health concerns. However, no previous study has comprehensively reviewed the... (Review)
Review
Posttraumatic stress disorder (PTSD) recently becomes one of the most important mental health concerns. However, no previous study has comprehensively reviewed the application of big data and machine learning (ML) techniques in PTSD. We found 873 studies meet the inclusion criteria and a total of 31 of those in a sample of 210,001 were included in quantitative analysis. ML algorithms were able to discriminate PTSD with an overall accuracy of 0.89. Pooled estimates of classification accuracy from multi-dimensional data (0.96) are higher than single data types (0.86 to 0.90). ML techniques can effectively classify PTSD and models using multi-dimensional data perform better than those using single data types. While selecting optimal combinations of data types and ML algorithms to be clinically applied at the individual level still remains a big challenge, these findings provide insights into the classification, identification, diagnosis and treatment of PTSD.
PubMed: 38724610
DOI: 10.1038/s41746-024-01117-5 -
International Journal of Medical... Jul 2024Human Emotion Recognition (HER) has been a popular field of study in the past years. Despite the great progresses made so far, relatively little attention has been paid... (Review)
Review
BACKGROUND
Human Emotion Recognition (HER) has been a popular field of study in the past years. Despite the great progresses made so far, relatively little attention has been paid to the use of HER in autism. People with autism are known to face problems with daily social communication and the prototypical interpretation of emotional responses, which are most frequently exerted via facial expressions. This poses significant practical challenges to the application of regular HER systems, which are normally developed for and by neurotypical people.
OBJECTIVE
This study reviews the literature on the use of HER systems in autism, particularly with respect to sensing technologies and machine learning methods, as to identify existing barriers and possible future directions.
METHODS
We conducted a systematic review of articles published between January 2011 and June 2023 according to the 2020 PRISMA guidelines. Manuscripts were identified through searching Web of Science and Scopus databases. Manuscripts were included when related to emotion recognition, used sensors and machine learning techniques, and involved children with autism, young, or adults.
RESULTS
The search yielded 346 articles. A total of 65 publications met the eligibility criteria and were included in the review.
CONCLUSIONS
Studies predominantly used facial expression techniques as the emotion recognition method. Consequently, video cameras were the most widely used devices across studies, although a growing trend in the use of physiological sensors was observed lately. Happiness, sadness, anger, fear, disgust, and surprise were most frequently addressed. Classical supervised machine learning techniques were primarily used at the expense of unsupervised approaches or more recent deep learning models. Studies focused on autism in a broad sense but limited efforts have been directed towards more specific disorders of the spectrum. Privacy or security issues were seldom addressed, and if so, at a rather insufficient level of detail.
Topics: Humans; Machine Learning; Emotions; Autistic Disorder; Facial Expression; Child
PubMed: 38723429
DOI: 10.1016/j.ijmedinf.2024.105469 -
Acta Neurochirurgica May 2024Mapping higher-order cognitive functions during awake brain surgery is important for cognitive preservation which is related to postoperative quality of life. A... (Review)
Review
PURPOSE
Mapping higher-order cognitive functions during awake brain surgery is important for cognitive preservation which is related to postoperative quality of life. A systematic review from 2018 about neuropsychological tests used during awake craniotomy made clear that until 2017 language was most often monitored and that the other cognitive domains were underexposed (Ruis, J Clin Exp Neuropsychol 40(10):1081-1104, 218). The field of awake craniotomy and cognitive monitoring is however developing rapidly. The aim of the current review is therefore, to investigate whether there is a change in the field towards incorporation of new tests and more complete mapping of (higher-order) cognitive functions.
METHODS
We replicated the systematic search of the study from 2018 in PubMed and Embase from February 2017 to November 2023, yielding 5130 potentially relevant articles. We used the artificial machine learning tool ASReview for screening and included 272 papers that gave a detailed description of the neuropsychological tests used during awake craniotomy.
RESULTS
Comparable to the previous study of 2018, the majority of studies (90.4%) reported tests for assessing language functions (Ruis, J Clin Exp Neuropsychol 40(10):1081-1104, 218). Nevertheless, an increasing number of studies now also describe tests for monitoring visuospatial functions, social cognition, and executive functions.
CONCLUSIONS
Language remains the most extensively tested cognitive domain. However, a broader range of tests are now implemented during awake craniotomy and there are (new developed) tests which received more attention. The rapid development in the field is reflected in the included studies in this review. Nevertheless, for some cognitive domains (e.g., executive functions and memory), there is still a need for developing tests that can be used during awake surgery.
Topics: Humans; Craniotomy; Wakefulness; Cognition; Neuropsychological Tests; Monitoring, Intraoperative; Intraoperative Neurophysiological Monitoring
PubMed: 38713405
DOI: 10.1007/s00701-024-06062-6