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International Journal of Environmental... Dec 2022Modern lifestyles require new tools for determining a person's ability to return to daily activities after knee surgery. These quantitative instruments must feature high... (Review)
Review
Modern lifestyles require new tools for determining a person's ability to return to daily activities after knee surgery. These quantitative instruments must feature high discrimination, be non-invasive, and be inexpensive. Machine learning is a revolutionary approach that has the potential to satisfy the aforementioned requirements and bridge the knowledge gap. The scope of this study is to summarize the results of a systematic literature review on the identification of gait-related changes and the determination of the functional recovery status of patients after knee surgery using advanced machine learning algorithms. The current systematic review was conducted using multiple databases in accordance with the PRISMA guidelines, including Scopus, PubMed, and Semantic Scholar. Six out of the 405 articles met our inclusion criteria and were directly related to the quantification of the recovery status using machine learning and gait data. The results were interpreted using appropriate metrics. The results demonstrated a recent increase in the use of sophisticated machine learning techniques that can provide robust decision-making support during personalized post-treatment interventions for knee-surgery patients.
Topics: Humans; Gait; Knee Joint; Knee; Machine Learning; Algorithms
PubMed: 36612771
DOI: 10.3390/ijerph20010448 -
Frontiers in Oncology 2022Here, we conducted a scoping review to (i) establish which machine learning (ML) methods have been applied to hematological malignancy imaging; (ii) establish how ML is...
BACKGROUND
Here, we conducted a scoping review to (i) establish which machine learning (ML) methods have been applied to hematological malignancy imaging; (ii) establish how ML is being applied to hematological cancer radiology; and (iii) identify addressable research gaps.
METHODS
The review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews guidelines. The inclusion criteria were (i) pediatric and adult patients with suspected or confirmed hematological malignancy undergoing imaging (); (ii) any study using ML techniques to derive models using radiological images to apply to the clinical management of these patients (); and (iii) original research articles conducted in any setting globally (). Quality Assessment of Diagnostic Accuracy Studies 2 criteria were used to assess diagnostic and segmentation studies, while the Newcastle-Ottawa scale was used to assess the quality of observational studies.
RESULTS
Of 53 eligible studies, 33 applied diverse ML techniques to diagnose hematological malignancies or to differentiate them from other diseases, especially discriminating gliomas from primary central nervous system lymphomas (n=18); 11 applied ML to segmentation tasks, while 9 applied ML to prognostication or predicting therapeutic responses, especially for diffuse large B-cell lymphoma. All studies reported discrimination statistics, but no study calculated calibration statistics. Every diagnostic/segmentation study had a high risk of bias due to their case-control design; many studies failed to provide adequate details of the reference standard; and only a few studies used independent validation.
CONCLUSION
To deliver validated ML-based models to radiologists managing hematological malignancies, future studies should (i) adhere to standardized, high-quality reporting guidelines such as the Checklist for Artificial Intelligence in Medical Imaging; (ii) validate models in independent cohorts; (ii) standardize volume segmentation methods for segmentation tasks; (iv) establish comprehensive prospective studies that include different tumor grades, comparisons with radiologists, optimal imaging modalities, sequences, and planes; (v) include side-by-side comparisons of different methods; and (vi) include low- and middle-income countries in multicentric studies to enhance generalizability and reduce inequity.
PubMed: 36605438
DOI: 10.3389/fonc.2022.1080988 -
BMC Medical Research Methodology Dec 2022Clinical prediction models are often not evaluated properly in specific settings or updated, for instance, with information from new markers. These key steps are needed...
BACKGROUND
Clinical prediction models are often not evaluated properly in specific settings or updated, for instance, with information from new markers. These key steps are needed such that models are fit for purpose and remain relevant in the long-term. We aimed to present an overview of methodological guidance for the evaluation (i.e., validation and impact assessment) and updating of clinical prediction models.
METHODS
We systematically searched nine databases from January 2000 to January 2022 for articles in English with methodological recommendations for the post-derivation stages of interest. Qualitative analysis was used to summarize the 70 selected guidance papers.
RESULTS
Key aspects for validation are the assessment of statistical performance using measures for discrimination (e.g., C-statistic) and calibration (e.g., calibration-in-the-large and calibration slope). For assessing impact or usefulness in clinical decision-making, recent papers advise using decision-analytic measures (e.g., the Net Benefit) over simplistic classification measures that ignore clinical consequences (e.g., accuracy, overall Net Reclassification Index). Commonly recommended methods for model updating are recalibration (i.e., adjustment of intercept or baseline hazard and/or slope), revision (i.e., re-estimation of individual predictor effects), and extension (i.e., addition of new markers). Additional methodological guidance is needed for newer types of updating (e.g., meta-model and dynamic updating) and machine learning-based models.
CONCLUSION
Substantial guidance was found for model evaluation and more conventional updating of regression-based models. An important development in model evaluation is the introduction of a decision-analytic framework for assessing clinical usefulness. Consensus is emerging on methods for model updating.
Topics: Humans; Calibration; Prognosis; Models, Statistical
PubMed: 36510134
DOI: 10.1186/s12874-022-01801-8 -
Journal of Medical Internet Research Sep 2022American Academy of Sleep Medicine guidelines suggest that clinical prediction algorithms can be used to screen patients with obstructive sleep apnea (OSA) without... (Review)
Review
BACKGROUND
American Academy of Sleep Medicine guidelines suggest that clinical prediction algorithms can be used to screen patients with obstructive sleep apnea (OSA) without replacing polysomnography, the gold standard.
OBJECTIVE
We aimed to identify, gather, and analyze existing machine learning approaches that are being used for disease screening in adult patients with suspected OSA.
METHODS
We searched the MEDLINE, Scopus, and ISI Web of Knowledge databases to evaluate the validity of different machine learning techniques, with polysomnography as the gold standard outcome measure and used the Prediction Model Risk of Bias Assessment Tool (Kleijnen Systematic Reviews Ltd) to assess risk of bias and applicability of each included study.
RESULTS
Our search retrieved 5479 articles, of which 63 (1.15%) articles were included. We found 23 studies performing diagnostic model development alone, 26 with added internal validation, and 14 applying the clinical prediction algorithm to an independent sample (although not all reporting the most common discrimination metrics, sensitivity or specificity). Logistic regression was applied in 35 studies, linear regression in 16, support vector machine in 9, neural networks in 8, decision trees in 6, and Bayesian networks in 4. Random forest, discriminant analysis, classification and regression tree, and nomogram were each performed in 2 studies, whereas Pearson correlation, adaptive neuro-fuzzy inference system, artificial immune recognition system, genetic algorithm, supersparse linear integer models, and k-nearest neighbors algorithm were each performed in 1 study. The best area under the receiver operating curve was 0.98 (0.96-0.99) for age, waist circumference, Epworth Somnolence Scale score, and oxygen saturation as predictors in a logistic regression.
CONCLUSIONS
Although high values were obtained, they still lacked external validation results in large cohorts and a standard OSA criteria definition.
TRIAL REGISTRATION
PROSPERO CRD42021221339; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=221339.
Topics: Adult; Bayes Theorem; Humans; Machine Learning; Neural Networks, Computer; Polysomnography; Sleep Apnea, Obstructive
PubMed: 36178720
DOI: 10.2196/39452 -
PloS One 2022We aimed to identify existing hypertension risk prediction models developed using traditional regression-based or machine learning approaches and compare their... (Meta-Analysis)
Meta-Analysis
OBJECTIVE
We aimed to identify existing hypertension risk prediction models developed using traditional regression-based or machine learning approaches and compare their predictive performance.
METHODS
We systematically searched MEDLINE, EMBASE, Web of Science, Scopus, and the grey literature for studies predicting the risk of hypertension among the general adult population. Summary statistics from the individual studies were the C-statistic, and a random-effects meta-analysis was used to obtain pooled estimates. The predictive performance of pooled estimates was compared between traditional regression-based models and machine learning-based models. The potential sources of heterogeneity were assessed using meta-regression, and study quality was assessed using the PROBAST (Prediction model Risk Of Bias ASsessment Tool) checklist.
RESULTS
Of 14,778 articles, 52 articles were selected for systematic review and 32 for meta-analysis. The overall pooled C-statistics was 0.75 [0.73-0.77] for the traditional regression-based models and 0.76 [0.72-0.79] for the machine learning-based models. High heterogeneity in C-statistic was observed. The age (p = 0.011), and sex (p = 0.044) of the participants and the number of risk factors considered in the model (p = 0.001) were identified as a source of heterogeneity in traditional regression-based models.
CONCLUSION
We attempted to provide a comprehensive evaluation of hypertension risk prediction models. Many models with acceptable-to-good predictive performance were identified. Only a few models were externally validated, and the risk of bias and applicability was a concern in many studies. Overall discrimination was similar between models derived from traditional regression analysis and machine learning methods. More external validation and impact studies to implement the hypertension risk prediction model in clinical practice are required.
Topics: Adult; Bias; Humans; Hypertension; Machine Learning; Regression Analysis; Risk Factors
PubMed: 35390039
DOI: 10.1371/journal.pone.0266334 -
Insights Into Imaging Mar 2022We systematically reviewed the current literature evaluating the ability of fully-automated deep learning (DL) and semi-automated traditional machine learning (TML)... (Review)
Review
Comparative performance of fully-automated and semi-automated artificial intelligence methods for the detection of clinically significant prostate cancer on MRI: a systematic review.
OBJECTIVES
We systematically reviewed the current literature evaluating the ability of fully-automated deep learning (DL) and semi-automated traditional machine learning (TML) MRI-based artificial intelligence (AI) methods to differentiate clinically significant prostate cancer (csPCa) from indolent PCa (iPCa) and benign conditions.
METHODS
We performed a computerised bibliographic search of studies indexed in MEDLINE/PubMed, arXiv, medRxiv, and bioRxiv between 1 January 2016 and 31 July 2021. Two reviewers performed the title/abstract and full-text screening. The remaining papers were screened by four reviewers using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) for DL studies and Radiomics Quality Score (RQS) for TML studies. Papers that fulfilled the pre-defined screening requirements underwent full CLAIM/RQS evaluation alongside the risk of bias assessment using QUADAS-2, both conducted by the same four reviewers. Standard measures of discrimination were extracted for the developed predictive models.
RESULTS
17/28 papers (five DL and twelve TML) passed the quality screening and were subject to a full CLAIM/RQS/QUADAS-2 assessment, which revealed a substantial study heterogeneity that precluded us from performing quantitative analysis as part of this review. The mean RQS of TML papers was 11/36, and a total of five papers had a high risk of bias. AUCs of DL and TML papers with low risk of bias ranged between 0.80-0.89 and 0.75-0.88, respectively.
CONCLUSION
We observed comparable performance of the two classes of AI methods and identified a number of common methodological limitations and biases that future studies will need to address to ensure the generalisability of the developed models.
PubMed: 35347462
DOI: 10.1186/s13244-022-01199-3 -
Frontiers in Psychology 2022Human faces are one of the most prominent stimuli in the visual environment of young infants and convey critical information for the development of social cognition....
Human faces are one of the most prominent stimuli in the visual environment of young infants and convey critical information for the development of social cognition. During the COVID-19 pandemic, mask wearing has become a common practice outside the home environment. With masks covering nose and mouth regions, the facial cues available to the infant are impoverished. The impact of these changes on development is unknown but is critical to debates around mask mandates in early childhood settings. As infants grow, they increasingly interact with a broader range of familiar and unfamiliar people outside the home; in these settings, mask wearing could possibly influence social development. In order to generate hypotheses about the effects of mask wearing on infant social development, in the present work, we systematically review = 129 studies selected based on the most recent PRISMA guidelines providing a state-of-the-art framework of behavioral studies investigating face processing in early infancy. We focused on identifying sensitive periods during which being exposed to specific facial features or to the entire face configuration has been found to be important for the development of perceptive and socio-communicative skills. For perceptive skills, infants gradually learn to analyze the eyes or the gaze direction within the context of the entire face configuration. This contributes to identity recognition as well as emotional expression discrimination. For socio-communicative skills, direct gaze and emotional facial expressions are crucial for attention engagement while eye-gaze cuing is important for joint attention. Moreover, attention to the mouth is particularly relevant for speech learning. We discuss possible implications of the exposure to masked faces for developmental needs and functions. Providing groundwork for further research, we encourage the investigation of the consequences of mask wearing for infants' perceptive and socio-communicative development, suggesting new directions within the research field.
PubMed: 35250718
DOI: 10.3389/fpsyg.2022.778247 -
Nutrients Feb 2022Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by a persistent pattern of inattention and/or hyperactivity-impulsivity.... (Review)
Review
Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by a persistent pattern of inattention and/or hyperactivity-impulsivity. ADHD impairments arise from irregularities primarily in dopamine (DA) and norepinephrine (NE) circuits within the prefrontal cortex. Due to ADHD medication's controversial side effects and high rates of diagnosis, alternative/complementary pharmacological therapeutic approaches for ADHD are needed. Although the number of publications that study the potential effects of caffeine consumption on ADHD treatment have been accumulating over the last years, and caffeine has recently been used in ADHD research in the context of animal models, an updated evidence-based systematic review on the effects of caffeine on ADHD-like symptoms in animal studies is lacking. To provide insight and value at the preclinical level, a systematic review based on PRISMA guidelines was performed for all publications available up to 1 September 2021. Caffeine treatment increases attention and improves learning, memory, and olfactory discrimination without altering blood pressure and body weight. These results are supported at the neuronal/molecular level. Nonetheless, the role of caffeine in modulating ADHD-like symptoms of hyperactivity and impulsivity is contradictory, raising discrepancies that require further clarification. Our results strengthen the hypothesis that the cognitive effects of caffeine found in animal models could be translated to human ADHD, particularly during adolescence.
Topics: Animals; Attention Deficit Disorder with Hyperactivity; Caffeine; Disease Models, Animal; Dopamine; Humans; Impulsive Behavior
PubMed: 35215389
DOI: 10.3390/nu14040739 -
Thoracic Cancer Mar 2022Screening with low-dose computed tomography (LDCT) is an efficient way to detect lung cancer at an earlier stage, but has a high false-positive rate. Several pulmonary... (Review)
Review
BACKGROUND
Screening with low-dose computed tomography (LDCT) is an efficient way to detect lung cancer at an earlier stage, but has a high false-positive rate. Several pulmonary nodules risk prediction models were developed to solve the problem. This systematic review aimed to compare the quality and accuracy of these models.
METHODS
The keywords "lung cancer," "lung neoplasms," "lung tumor," "risk," "lung carcinoma" "risk," "predict," "assessment," and "nodule" were used to identify relevant articles published before February 2021. All studies with multivariate risk models developed and validated on human LDCT data were included. Informal publications or studies with incomplete procedures were excluded. Information was extracted from each publication and assessed.
RESULTS
A total of 41 articles and 43 models were included. External validation was performed for 23.2% (10/43) models. Deep learning algorithms were applied in 62.8% (27/43) models; 60.0% (15/25) deep learning based researches compared their algorithms with traditional methods, and received better discrimination. Models based on Asian and Chinese populations were usually built on single-center or small sample retrospective studies, and the majority of the Asian models (12/15, 80.0%) were not validated using external datasets.
CONCLUSION
The existing models showed good discrimination for identifying high-risk pulmonary nodules, but lacked external validation. Deep learning algorithms are increasingly being used with good performance. More researches are required to improve the quality of deep learning models, particularly for the Asian population.
Topics: Early Detection of Cancer; Humans; Lung; Lung Neoplasms; Multiple Pulmonary Nodules; Retrospective Studies
PubMed: 35137543
DOI: 10.1111/1759-7714.14333 -
European Journal of Nuclear Medicine... Jul 2022Studies based on machine learning-based quantitative imaging techniques have gained much interest in cancer research. The aim of this review is to critically appraise... (Review)
Review
Methodological quality of machine learning-based quantitative imaging analysis studies in esophageal cancer: a systematic review of clinical outcome prediction after concurrent chemoradiotherapy.
PURPOSE
Studies based on machine learning-based quantitative imaging techniques have gained much interest in cancer research. The aim of this review is to critically appraise the existing machine learning-based quantitative imaging analysis studies predicting outcomes of esophageal cancer after concurrent chemoradiotherapy in accordance with PRISMA guidelines.
METHODS
A systematic review was conducted in accordance with PRISMA guidelines. The citation search was performed via PubMed and Embase Ovid databases for literature published before April 2021. From each full-text article, study characteristics and model information were summarized. We proposed an appraisal matrix with 13 items to assess the methodological quality of each study based on recommended best-practices pertaining to quality.
RESULTS
Out of 244 identified records, 37 studies met the inclusion criteria. Study endpoints included prognosis, treatment response, and toxicity after concurrent chemoradiotherapy with reported discrimination metrics in validation datasets between 0.6 and 0.9, with wide variation in quality. A total of 30 studies published within the last 5 years were evaluated for methodological quality and we found 11 studies with at least 6 "good" item ratings.
CONCLUSION
A substantial number of studies lacked prospective registration, external validation, model calibration, and support for use in clinic. To further improve the predictive power of machine learning-based models and translate into real clinical applications in cancer research, appropriate methodologies, prospective registration, and multi-institution validation are recommended.
Topics: Chemoradiotherapy; Esophageal Neoplasms; Humans; Machine Learning; Prognosis; Prospective Studies
PubMed: 34939174
DOI: 10.1007/s00259-021-05658-9