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Environmental Monitoring and Assessment Jun 2024Microplastics in the environment are considered complex pollutants as they are chemical and corrosive-resistant, non-biodegradable and ubiquitous. These microplastics...
Microplastics in the environment are considered complex pollutants as they are chemical and corrosive-resistant, non-biodegradable and ubiquitous. These microplastics may act as vectors for the dissemination of other pollutants and the transmission of microorganisms into the water environment. The currently available literature reviews focus on analysing the occurrence, environmental effects and methods of microplastic detection, however lacking a wide-scale systematic review and classification of the mathematical microplastic modelling applications. Thus, the current review provides a global overview of the modelling methodologies used for microplastic transport and fate in water environments. This review consolidates, classifies and analyses the methods, model inputs and results of 61 microplastic modelling studies in the last decade (2012-2022). It thoroughly discusses their strengths, weaknesses and common gaps in their modelling framework. Five main modelling types were classified as follows: hydrodynamic, process-based, statistical, mass-balance and machine learning models. Further, categorisations based on the water environments, location and published year of these applications were also adopted. It is concluded that addressed modelling types resulted in relatively reliable outcomes, yet each modelling framework has its strengths and weaknesses. However, common issues were found such as inputs being unrealistically assumed, especially biological processes, and the lack of sufficient field data for model calibration and validation. For future research, it is recommended to incorporate macroplastics' degradation rates, particles of different shapes and sizes and vertical mixing due to biofouling and turbulent conditions and also more experimental data to obtain precise model inputs and standardised sampling methods for surface and column waters.
Topics: Environmental Monitoring; Microplastics; Models, Chemical; Models, Theoretical; Water Pollutants, Chemical
PubMed: 38935176
DOI: 10.1007/s10661-024-12731-x -
Sensors (Basel, Switzerland) Jun 2024Measuring pilot mental workload (MWL) is crucial for enhancing aviation safety. However, MWL is a multi-dimensional construct that could be affected by multiple factors.... (Review)
Review
Measuring pilot mental workload (MWL) is crucial for enhancing aviation safety. However, MWL is a multi-dimensional construct that could be affected by multiple factors. Particularly, in the context of a more automated cockpit setting, the traditional methods of assessing pilot MWL may face challenges. Heart rate variability (HRV) has emerged as a potential tool for detecting pilot MWL during real-flight operations. This review aims to investigate the relationship between HRV and pilot MWL and to assess the performance of machine-learning-based MWL detection systems using HRV parameters. A total of 29 relevant papers were extracted from three databases for review based on rigorous eligibility criteria. We observed significant variability across the reviewed studies, including study designs and measurement methods, as well as machine-learning techniques. Inconsistent results were observed regarding the differences in HRV measures between pilots under varying levels of MWL. Furthermore, for studies that developed HRV-based MWL detection systems, we examined the diverse model settings and discovered that several advanced techniques could be used to address specific challenges. This review serves as a practical guide for researchers and practitioners who are interested in employing HRV indicators for evaluating MWL and wish to incorporate cutting-edge techniques into their MWL measurement approaches.
Topics: Humans; Heart Rate; Workload; Machine Learning; Pilots; Aviation
PubMed: 38931507
DOI: 10.3390/s24123723 -
Microorganisms Jun 2024Bacterial endocarditis (BE) is a severe infection of the endocardium and cardiac valves caused by bacterial agents in dogs. Diagnosis of endocarditis is challenging due... (Review)
Review
Bacterial endocarditis (BE) is a severe infection of the endocardium and cardiac valves caused by bacterial agents in dogs. Diagnosis of endocarditis is challenging due to the variety of clinical presentations and lack of definitive diagnostic tests in its early stages. This study aims to provide a research literature analysis on BE in dogs based on text mining (TM) and topic analysis (TA) identifying dominant topics, summarizing their temporal trend, and highlighting any possible research gaps. A literature search was performed utilizing the Scopus database, employing keywords pertaining to BE to analyze papers published in English from 1990 to 2023. The investigation followed a systematic approach based on the PRISMA guidelines. A total of 86 records were selected for analysis following screening procedures and underwent descriptive statistics, TM, and TA. The findings revealed that the number of records published per year has increased in 2007 and 2021. TM identified the words with the highest term frequency-inverse document frequency (TF-IDF), and TA highlighted the main research areas, in the following order: causative agents, clinical findings and predisposing factors, case reports on endocarditis, outcomes and biomarkers, and infective endocarditis and bacterial isolation. The study confirms the increasing interest in BE but shows where further studies are needed.
PubMed: 38930619
DOI: 10.3390/microorganisms12061237 -
Journal of Personalized Medicine Jun 2024AI is included in a lot of different systems. In facial surgery, there are some AI-based software programs oriented to diagnosis in facial surgery. This study aims to... (Review)
Review
AI is included in a lot of different systems. In facial surgery, there are some AI-based software programs oriented to diagnosis in facial surgery. This study aims to evaluate the capacity and training of models for diagnosis of dentofacial deformities in class II and class III patients using artificial intelligence and the potential use for indicating orthognathic surgery. The search strategy is from 1943 to April 2024 in PubMed, Embase, Scopus, Lilacs, and Web of Science. Studies that used imaging to assess anatomical structures, airway volume, and craniofacial positions using the AI algorithm in the human population were included. The methodological quality of the studies was assessed using the Effective Public Health Practice Project instrument. The systematic search identified 697 articles. Eight studies were obtained for descriptive analysis after exclusion according to our inclusion and exclusion criteria. All studies were retrospective in design. A total of 5552 subjects with an age range between 14.7 and 56 years were obtained; 2474 (44.56%) subjects were male, and 3078 (55.43%) were female. Six studies were analyzed using 2D imaging and obtained highly accurate results in diagnosing skeletal features and determining the need for orthognathic surgery, and two studies used 3D imaging for measurement and diagnosis. Limitations of the studies such as age, diagnosis in facial deformity, and the included variables were observed. Concerning the overall analysis bias, six studies were at moderate risk due to weak study designs, while two were at high risk of bias. We can conclude that, with the few articles included, using AI-based software allows for some craniometric recognition and measurements to determine the diagnosis of facial deformities using mainly 2D analysis. However, it is necessary to perform studies based on three-dimensional images, increase the sample size, and train models in different populations to ensure accuracy of AI applications in this field. After that, the models can be trained for dentofacial diagnosis.
PubMed: 38929868
DOI: 10.3390/jpm14060647 -
International Journal of Molecular... Jun 2024The process of identification and management of neurological disorder conditions faces challenges, prompting the investigation of novel methods in order to improve... (Review)
Review
The process of identification and management of neurological disorder conditions faces challenges, prompting the investigation of novel methods in order to improve diagnostic accuracy. In this study, we conducted a systematic literature review to identify the significance of genetics- and molecular-pathway-based machine learning (ML) models in treating neurological disorder conditions. According to the study's objectives, search strategies were developed to extract the research studies using digital libraries. We followed rigorous study selection criteria. A total of 24 studies met the inclusion criteria and were included in the review. We classified the studies based on neurological disorders. The included studies highlighted multiple methodologies and exceptional results in treating neurological disorders. The study findings underscore the potential of the existing models, presenting personalized interventions based on the individual's conditions. The findings offer better-performing approaches that handle genetics and molecular data to generate effective outcomes. Moreover, we discuss the future research directions and challenges, emphasizing the demand for generalizing existing models in real-world clinical settings. This study contributes to advancing knowledge in the field of diagnosis and management of neurological disorders.
Topics: Humans; Machine Learning; Nervous System Diseases
PubMed: 38928128
DOI: 10.3390/ijms25126422 -
Journal of Clinical Epidemiology Jun 2024Prognostic models have the potential to aid clinical decision-making after hip fracture. This systematic review aimed to identify, critically appraise and summarise...
Systematic review of multivariable prognostic models for outcomes at least 30 days after hip fracture finds 18 mortality models but no non-mortality models warranting validation.
OBJECTIVE
Prognostic models have the potential to aid clinical decision-making after hip fracture. This systematic review aimed to identify, critically appraise and summarise multivariable prediction models for mortality or other long-term recovery outcomes occurring at least 30 days after hip fracture.
STUDY DESIGN
MEDLINE, Embase, Scopus, Web of Science and CINAHL databases were searched up to May 2023. Studies were included that aimed to develop multivariable models to make predictions for individuals at least 30 days after hip fracture. Risk of bias (ROB) was dual-assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Study and model details were extracted and summarised.
RESULTS
From 5,571 records, 80 eligible studies were identified. They predicted mortality in n=55 studies/ 81 models, and non-mortality outcomes (mobility, function, residence, medical and surgical complications) in n=30 studies/ 45 models. Most (n=46; 58%) studies were published since 2020. A quarter of studies (n=19; 24%) reported using 'machine-learning methods', while the remainder used logistic regression (n=54; 68%) and other statistical methods (n=11; 14%) to build models. Overall, 15 studies (19%) presented 18 low ROB models, all predicting mortality. Common concerns were sample size, missing data handling, inadequate internal validation and calibration assessment. Many studies with non-mortality outcomes, (n=11; 37%) had clear data complexities that were not correctly modelled.
CONCLUSION
This review has comprehensively summarised and appraised multivariable prediction models for long-term outcomes after hip fracture. Only 15 studies out of 55 predicting mortality were rated as low ROB, warranting further development of their models. All studies predicting non-mortality outcomes were high or unclear ROB. Careful consideration is required for both the methods used and justification for developing further non-mortality prediction models for this clinical population.
PubMed: 38925343
DOI: 10.1016/j.jclinepi.2024.111439 -
Ecology Letters Jun 2024Understanding the interactions among anthropogenic stressors is critical for effective conservation and management of ecosystems. Freshwater scientists have invested...
Understanding the interactions among anthropogenic stressors is critical for effective conservation and management of ecosystems. Freshwater scientists have invested considerable resources in conducting factorial experiments to disentangle stressor interactions by testing their individual and combined effects. However, the diversity of stressors and systems studied has hindered previous syntheses of this body of research. To overcome this challenge, we used a novel machine learning framework to identify relevant studies from over 235,000 publications. Our synthesis resulted in a new dataset of 2396 multiple-stressor experiments in freshwater systems. By summarizing the methods used in these studies, quantifying trends in the popularity of the investigated stressors, and performing co-occurrence analysis, we produce the most comprehensive overview of this diverse field of research to date. We provide both a taxonomy grouping the 909 investigated stressors into 31 classes and an open-source and interactive version of the dataset (https://jamesaorr.shinyapps.io/freshwater-multiple-stressors/). Inspired by our results, we provide a framework to help clarify whether statistical interactions detected by factorial experiments align with stressor interactions of interest, and we outline general guidelines for the design of multiple-stressor experiments relevant to any system. We conclude by highlighting the research directions required to better understand freshwater ecosystems facing multiple stressors.
Topics: Ecosystem; Fresh Water; Human Activities; Stress, Physiological
PubMed: 38924275
DOI: 10.1111/ele.14463 -
Toxins Jun 2024Mycotoxins, toxic secondary metabolites produced by certain fungi, pose significant threats to global food safety and public health. These compounds can contaminate a... (Review)
Review
Mycotoxins, toxic secondary metabolites produced by certain fungi, pose significant threats to global food safety and public health. These compounds can contaminate a variety of crops, leading to economic losses and health risks to both humans and animals. Traditional lab analysis methods for mycotoxin detection can be time-consuming and may not always be suitable for large-scale screenings. However, in recent years, machine learning (ML) methods have gained popularity for use in the detection of mycotoxins and in the food safety industry in general due to their accurate and timely predictions. We provide a systematic review on some of the recent ML applications for detecting/predicting the presence of mycotoxin on a variety of food ingredients, highlighting their advantages, challenges, and potential for future advancements. We address the need for reproducibility and transparency in ML research through open access to data and code. An observation from our findings is the frequent lack of detailed reporting on hyperparameters in many studies and a lack of open source code, which raises concerns about the reproducibility and optimisation of the ML models used. The findings reveal that while the majority of studies predominantly utilised neural networks for mycotoxin detection, there was a notable diversity in the types of neural network architectures employed, with convolutional neural networks being the most popular.
Topics: Mycotoxins; Machine Learning; Food Contamination; Animals; Humans; Neural Networks, Computer
PubMed: 38922162
DOI: 10.3390/toxins16060268 -
Annals of the Academy of Medicine,... Mar 2024Automated machine learning (autoML) removes technical and technological barriers to building artificial intelligence models. We aimed to summarise the clinical... (Review)
Review
INTRODUCTION
Automated machine learning (autoML) removes technical and technological barriers to building artificial intelligence models. We aimed to summarise the clinical applications of autoML, assess the capabilities of utilised platforms, evaluate the quality of the evidence trialling autoML, and gauge the performance of autoML platforms relative to conventionally developed models, as well as each other.
METHOD
This review adhered to a prospectively registered protocol (PROSPERO identifier CRD42022344427). The Cochrane Library, Embase, MEDLINE and Scopus were searched from inception to 11 July 2022. Two researchers screened abstracts and full texts, extracted data and conducted quality assessment. Disagreement was resolved through discussion and if required, arbitration by a third researcher.
RESULTS
There were 26 distinct autoML platforms featured in 82 studies. Brain and lung disease were the most common fields of study of 22 specialties. AutoML exhibited variable performance: area under the receiver operator characteristic curve (AUCROC) 0.35-1.00, F1-score 0.16-0.99, area under the precision-recall curve (AUPRC) 0.51-1.00. AutoML exhibited the highest AUCROC in 75.6% trials; the highest F1-score in 42.3% trials; and the highest AUPRC in 83.3% trials. In autoML platform comparisons, AutoPrognosis and Amazon Rekognition performed strongest with unstructured and structured data, respectively. Quality of reporting was poor, with a median DECIDE-AI score of 14 of 27.
CONCLUSION
A myriad of autoML platforms have been applied in a variety of clinical contexts. The performance of autoML compares well to bespoke computational and clinical benchmarks. Further work is required to improve the quality of validation studies. AutoML may facilitate a transition to data-centric development, and integration with large language models may enable AI to build itself to fulfil user-defined goals.
Topics: Humans; Machine Learning; Lung Diseases; ROC Curve; Brain Diseases; Area Under Curve
PubMed: 38920245
DOI: 10.47102/annals-acadmedsg.2023113 -
Minerva Urology and Nephrology Jun 2024Artificial intelligence and machine learning are the new frontier in urology; they can assist the diagnostic work-up and in prognostication bring superior to the...
INTRODUCTION
Artificial intelligence and machine learning are the new frontier in urology; they can assist the diagnostic work-up and in prognostication bring superior to the existing nomograms. Infectious events and in particular the septic risk, are one of the most common and in some cases life threatening complication in patients with urolithiasis. We performed a scoping review to provide an overview of the current application of AI in prediction the infectious complications in patients affected by urolithiasis.
EVIDENCE ACQUISITION
A systematic scoping review of the literature was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses for Scoping Reviews (PRISMA-ScR) guidelines by screening Medline, PubMed, and Embase to detect pertinent studies.
EVIDENCE SYNTHESIS
A total of 467 articles were found, of which nine met the inclusion criteria and were considered. All studies are retrospective and published between 2021 and 2023. Only two studies performed an external validation of the described models. The main event considered is urosepsis in four articles, urinary tract infection in two articles and diagnosis of infection stones in three articles. Different AI models were trained, each of which exploited several types and numbers of variables. All studies reveal good performance. Random forest and artificial neural networks seem to have higher AUC, specificity and sensibility and perform better than the traditional statistical analysis.
CONCLUSIONS
Further prospective and multi-institutional studies with external validation are needed to better clarify which variables and AI models should be integrated in our clinical practice to predict infectious events.
Topics: Humans; Urolithiasis; Artificial Intelligence; Urinary Tract Infections; Risk Assessment; Sepsis; Machine Learning
PubMed: 38920010
DOI: 10.23736/S2724-6051.24.05686-6