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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 -
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 -
Frontiers in Neuroscience 2019Reduced sensation is experienced by one in two individuals following stroke, impacting both the ability to function independently and overall quality of life....
Reduced sensation is experienced by one in two individuals following stroke, impacting both the ability to function independently and overall quality of life. Repetitive activation of sensory input using active and passive sensory-based interventions have been shown to enhance adaptive motor cortical plasticity, indicating a potential mechanism which may mediate recovery. However, rehabilitation specifically focusing on somatosensory function receives little attention. To investigate sensory-based interventions reported in the literature and determine the effectiveness to improve sensation and sensorimotor function of individuals following stroke. Electronic databases and trial registries were searched from inception until November 2018, in addition to hand searching systematic reviews. Study selection included randomized controlled trials for adults of any stroke type with an upper and/or lower limb sensorimotor impairment. Participants all received a sensory-based intervention designed to improve activity levels or impairment, which could be compared with usual care, sham, or another intervention. The primary outcomes were change in activity levels related to sensorimotor function. Secondary outcomes were measures of impairment, participation or quality of life. A total of 38 study trials were included ( = 1,093 participants); 29 explored passive sensory training (somatosensory; peripheral nerve; afferent; thermal; sensory amplitude electrical stimulation), 6 active (sensory discrimination; perceptual learning; sensory retraining) and 3 hybrid (haptic-based augmented reality; sensory-based feedback devices). Meta-analyses (13 comparisons; 385 participants) demonstrated a moderate effect in favor of passive sensory training on improving a range of upper and lower limb activity measures following stroke. Narrative syntheses were completed for studies unable to be pooled due to heterogeneity of measures or insufficient data, evidence for active sensory training is limited however does show promise in improving sensorimotor function following stroke. Findings from the meta-analyses and single studies highlight some support for the effectiveness of passive sensory training in relation to sensory impairment and motor function. However, evidence for active sensory training continues to be limited. Further high-quality research with rigorous methods (adequately powered with consistent outcome measures) is required to determine the effectiveness of sensory retraining in stroke rehabilitation, particularly for active sensory training.
PubMed: 31114472
DOI: 10.3389/fnins.2019.00402 -
Journal of Medical Internet Research Sep 2020Helicobacter pylori plays a central role in the development of gastric cancer, and prediction of H pylori infection by visual inspection of the gastric mucosa is an... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Helicobacter pylori plays a central role in the development of gastric cancer, and prediction of H pylori infection by visual inspection of the gastric mucosa is an important function of endoscopy. However, there are currently no established methods of optical diagnosis of H pylori infection using endoscopic images. Definitive diagnosis requires endoscopic biopsy. Artificial intelligence (AI) has been increasingly adopted in clinical practice, especially for image recognition and classification.
OBJECTIVE
This study aimed to evaluate the diagnostic test accuracy of AI for the prediction of H pylori infection using endoscopic images.
METHODS
Two independent evaluators searched core databases. The inclusion criteria included studies with endoscopic images of H pylori infection and with application of AI for the prediction of H pylori infection presenting diagnostic performance. Systematic review and diagnostic test accuracy meta-analysis were performed.
RESULTS
Ultimately, 8 studies were identified. Pooled sensitivity, specificity, diagnostic odds ratio, and area under the curve of AI for the prediction of H pylori infection were 0.87 (95% CI 0.72-0.94), 0.86 (95% CI 0.77-0.92), 40 (95% CI 15-112), and 0.92 (95% CI 0.90-0.94), respectively, in the 1719 patients (385 patients with H pylori infection vs 1334 controls). Meta-regression showed methodological quality and included the number of patients in each study for the purpose of heterogeneity. There was no evidence of publication bias. The accuracy of the AI algorithm reached 82% for discrimination between noninfected images and posteradication images.
CONCLUSIONS
An AI algorithm is a reliable tool for endoscopic diagnosis of H pylori infection. The limitations of lacking external validation performance and being conducted only in Asia should be overcome.
TRIAL REGISTRATION
PROSPERO CRD42020175957; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=175957.
Topics: Artificial Intelligence; Diagnostic Tests, Routine; Endoscopy; Helicobacter Infections; Helicobacter pylori; Humans
PubMed: 32936088
DOI: 10.2196/21983 -
Journal of the American Academy of... Nov 2021Although instrumental learning deficits are, among other deficits, assumed to contribute to attention-deficit/hyperactivity disorder (ADHD), no comprehensive systematic...
OBJECTIVE
Although instrumental learning deficits are, among other deficits, assumed to contribute to attention-deficit/hyperactivity disorder (ADHD), no comprehensive systematic review of instrumental learning deficits in ADHD exists. This review examines differences between ADHD and typically developing (TD) children in basic instrumental learning and the effects of reinforcement form, magnitude, schedule, and complexity, as well as effects of medication, on instrumental learning in children with ADHD.
METHOD
A systematic search of PubMed, PsyINFO, CINAHL, EMBASE+EMBASE CLASSIC, ERIC, and Web of Science was conducted for articles up to March 16, 2020. Experimental studies comparing instrumental learning between groups (ADHD versus TD) or a manipulation of reinforcement/medication within an ADHD sample were included. Quality of studies was assessed with an adapted version of the Hombrados and Waddington criteria to assess risk of bias in (quasi-) experimental studies.
RESULTS
A total of 19 studies from among 3,384 non-duplicate screened articles were included. No difference in basic instrumental learning was found between children with ADHD and TD children, nor effects of form or magnitude of reinforcement. Results regarding reinforcement schedule and reversal learning were mixed, but children with ADHD seemed to show deficits in conditional discrimination learning compared to TD children. Methylphenidate improved instrumental learning in children with ADHD. Quality assessment showed poor quality of studies with respect to sample sizes and outcome and missing data reporting.
CONCLUSION
The review identified very few and highly heterogenous studies, with inconsistent findings. No clear deficit was found in instrumental learning under laboratory conditions. Children with ADHD do show deficits in complex forms of learning, that is, conditional discrimination learning. Clearly more research is needed, using more similar task designs and manipulations.
Topics: Attention Deficit Disorder with Hyperactivity; Child; Conditioning, Operant; Humans; Learning; Methylphenidate
PubMed: 33862167
DOI: 10.1016/j.jaac.2021.03.009 -
Frontiers in Immunology 2023Prophylaxis of postoperative recurrence is an intractable problem for clinicians and patients with Crohn's disease. Prognostic models are effective tools for patient...
BACKGROUND AND AIMS
Prophylaxis of postoperative recurrence is an intractable problem for clinicians and patients with Crohn's disease. Prognostic models are effective tools for patient stratification and personalised management. This systematic review aimed to provide an overview and critically appraise the existing models for predicting postoperative recurrence of Crohn's disease.
METHODS
Systematic retrieval was performed using PubMed and Web of Science in January 2022. Original articles on prognostic models for predicting postoperative recurrence of Crohn's disease were included in the analysis. The risk of bias was assessed using the Prediction Model Risk of Bias Assessment (PROBAST) tool. This study was registered with the International Prospective Register of Systematic Reviews (PROSPERO; number CRD42022311737).
RESULTS
In total, 1948 articles were screened, of which 15 were ultimately considered. Twelve studies developed 15 new prognostic models for Crohn's disease and the other three validated the performance of three existing models. Seven models utilised regression algorithms, six utilised scoring indices, and five utilised machine learning. The area under the receiver operating characteristic curve of the models ranged from 0.51 to 0.97. Six models showed good discrimination, with an area under the receiver operating characteristic curve of >0.80. All models were determined to have a high risk of bias in modelling or analysis, while they were at low risk of applicability concerns.
CONCLUSIONS
Prognostic models have great potential for facilitating the assessment of postoperative recurrence risk in patients with Crohn's disease. Existing prognostic models require further validation regarding their reliability and applicability.
SYSTEMATIC REVIEW REGISTRATION
https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022311737.
Topics: Humans; Crohn Disease; Prognosis; Reproducibility of Results
PubMed: 37457731
DOI: 10.3389/fimmu.2023.1215116 -
Journal of Clinical Medicine Aug 2023The use of radiomics and artificial intelligence applied for the diagnosis and monitoring of Alzheimer's disease has developed in recent years. However, this approach is... (Review)
Review
The use of radiomics and artificial intelligence applied for the diagnosis and monitoring of Alzheimer's disease has developed in recent years. However, this approach is not yet completely applicable in clinical practice. The aim of this paper is to provide a systematic analysis of the studies that have included the use of radiomics from different imaging techniques and artificial intelligence for the diagnosis and monitoring of Alzheimer's disease in order to improve the clinical outcomes and quality of life of older patients. A systematic review of the literature was conducted in February 2023, analyzing manuscripts and articles of the last 5 years from the PubMed, Scopus and Embase databases. All studies concerning discrimination among Alzheimer's disease, Mild Cognitive Impairment and healthy older people performing radiomics analysis through machine and deep learning were included. A total of 15 papers were included. The results showed a very good performance of this approach in the differentiating Alzheimer's disease patients-both at the dementia and pre-dementia phases of the disease-from healthy older people. In summary, radiomics and AI can be valuable tools for diagnosing and monitoring the progression of Alzheimer's disease, potentially leading to earlier and more accurate diagnosis and treatment. However, the results reported by this review should be read with great caution, keeping in mind that imaging alone is not enough to identify dementia due to Alzheimer's.
PubMed: 37629474
DOI: 10.3390/jcm12165432 -
Neuroscience and Biobehavioral Reviews Oct 2021Laboratory experiments using fear conditioning and extinction protocols help lay the groundwork for designing, testing, and optimizing innovative treatments for... (Review)
Review
Laboratory experiments using fear conditioning and extinction protocols help lay the groundwork for designing, testing, and optimizing innovative treatments for anxiety-related disorders. Yet, there is limited basic research on fear conditioning and extinction in obsessive-compulsive disorder (OCD). This is surprising because exposure-based treatments based on associative learning principles are among the most popular and effective treatment options for OCD. Here, we systematically review and critically assess existing aversive conditioning and extinction studies of OCD. Across 12 studies, there was moderate evidence that OCD is associated with abnormal acquisition of conditioned responses that differ from comparison groups. There was relatively stronger evidence of OCD's association with impaired extinction processes. This included multiple studies finding elevated conditioned responses during extinction learning and poorer threat/safety discrimination during recall, although a minority of studies yielded results inconsistent with this conclusion. Overall, the conditioning model holds value for OCD research, but more work is necessary to clarify emerging patterns of results and increase clinical translational utility to the level seen in other anxiety-related disorders. We detail limitations in the literature and suggest next steps, including modeling OCD with more complex conditioning methodology (e.g., semantic/conceptual generalization, avoidance) and improving individual-differences assessment with dimensional techniques.
Topics: Conditioning, Classical; Conditioning, Psychological; Extinction, Psychological; Fear; Humans; Obsessive-Compulsive Disorder
PubMed: 34314751
DOI: 10.1016/j.neubiorev.2021.07.026 -
The Journal of Thoracic and... Jun 2022Interest in the usefulness of machine learning (ML) methods for outcomes prediction has continued to increase in recent years. However, the advantage of advanced ML... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Interest in the usefulness of machine learning (ML) methods for outcomes prediction has continued to increase in recent years. However, the advantage of advanced ML model over traditional logistic regression (LR) remains controversial. We performed a systematic review and meta-analysis of studies comparing the discrimination accuracy between ML models versus LR in predicting operative mortality following cardiac surgery.
METHODS
The present systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis statement. Discrimination ability was assessed using the C-statistic. Pooled C-statistics and its 95% credibility interval for ML models and LR were obtained were obtained using a Bayesian framework. Pooled estimates for ML models and LR were compared to inform on difference between the 2 approaches.
RESULTS
We identified 459 published citations of which 15 studies met inclusion criteria and were used for the quantitative and qualitative analysis. When the best ML model from individual study was used, meta-analytic estimates showed that ML were associated with a significantly higher C-statistic (ML, 0.88; 95% credibility interval, 0.83-0.93 vs LR, 0.81; 95% credibility interval, 0.77-0.85; P = .03). When individual ML algorithms were instead selected, we found a nonsignificant trend toward better prediction with each of ML algorithms. We found no evidence of publication bias (P = .70).
CONCLUSIONS
The present findings suggest that when compared with LR, ML models provide better discrimination in mortality prediction after cardiac surgery. However, the magnitude and clinical influence of such an improvement remains uncertain.
Topics: Bayes Theorem; Cardiac Surgical Procedures; Humans; Logistic Models; Machine Learning; Prognosis
PubMed: 32900480
DOI: 10.1016/j.jtcvs.2020.07.105 -
Cancers Mar 2021Radiomics may increase the diagnostic accuracy of medical imaging for localized and metastatic RCC (mRCC). A systematic review and meta-analysis was performed. Doing so,... (Review)
Review
Radiomics may increase the diagnostic accuracy of medical imaging for localized and metastatic RCC (mRCC). A systematic review and meta-analysis was performed. Doing so, we comprehensively searched literature databases until May 2020. Studies investigating the diagnostic value of radiomics in differentiation of localized renal tumors and assessment of treatment response to ST in mRCC were included and assessed with respect to their quality using the radiomics quality score (RQS). A total of 113 out of 1098 identified studies met the criteria and were included in qualitative synthesis. Median RQS of all studies was 13.9% (5.0 points, IQR 0.25-7.0 points), and RQS increased over time. Thirty studies were included into the quantitative synthesis: For distinguishing angiomyolipoma, oncocytoma or unspecified benign tumors from RCC, the random effects model showed a log odds ratio (OR) of 2.89 (95%-CI 2.40-3.39, < 0.001), 3.08 (95%-CI 2.09-4.06, < 0.001) and 3.57 (95%-CI 2.69-4.45, < 0.001), respectively. For the general discrimination of benign tumors from RCC log OR was 3.17 (95%-CI 2.73-3.62, < 0.001). Inhomogeneity of the available studies assessing treatment response in mRCC prevented any meaningful meta-analysis. The application of radiomics seems promising for discrimination of renal tumor dignity. Shared data and open science may assist in improving reproducibility of future studies.
PubMed: 33802699
DOI: 10.3390/cancers13061348