Detecting Disease in Radiographs with Intuitive Confidence.
Ontology highlight
ABSTRACT:
SUBMITTER: Jaeger S
PROVIDER: S-EPMC4606081 | biostudies-other | 2015
REPOSITORIES: biostudies-other
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Project description:MOTIVATION:Cancers arise as the result of somatically acquired changes in the DNA of cancer cells. However, in addition to the mutations that confer a growth advantage, cancer genomes accumulate a large number of somatic mutations resulting from normal DNA damage and repair processes as well as carcinogenic exposures or cancer related aberrations of DNA maintenance machinery. These mutagenic processes often produce characteristic mutational patterns called mutational signatures. The decomposition of a cancer genome's mutation catalog into mutations consistent with such signatures can provide valuable information about cancer etiology. However, the results from different decomposition methods are not always consistent. Hence, one needs to be able to not only decompose a patient's mutational profile into signatures but also establish the accuracy of such decomposition. RESULTS:We proposed two complementary ways of measuring confidence and stability of decomposition results and applied them to analyze mutational signatures in breast cancer genomes. We identified both very stable and highly unstable signatures, as well as signatures that previously have not been associated with breast cancer. We also provided additional support for the novel signatures. Our results emphasize the importance of assessing the confidence and stability of inferred signature contributions. AVAILABILITY AND IMPLEMENTATION:All tools developed in this paper have been implemented in an R package, called SignatureEstimation, which is available from https://www.ncbi.nlm.nih.gov/CBBresearch/Przytycka/index.cgi\#signatureestimation. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
| S-EPMC5860213 | biostudies-literature
Project description:Aim:To assess the diagnostic performance of lateral radiograph of the neck for supraglottitis in adults and children. Methods:Electronic database searches (including PubMed, EMBASE, CINAHL, Web of Science, and WHO International Clinical Trials Registry Platform) were carried out through July 2014. Citations of included studies and recent narrative reviews were searched. Studies that compared lateral radiograph of the neck with a reference standard of direct/indirect laryngoscopy were included. Two reviewers independently assessed the methodological quality of included studies by Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Results:Of 2,088 potentially relevant citations, two single-gate (cohort) studies and six two-gate (case-control) studies were identified. These included studies of design are at the moderate or high risk of bias in QUADAS-2. The pooled sensitivity from bivariate random-effects regression was 92.9% (95% confidence interval [CI], 88.5-95.9%) and the pooled specificity was 89.2% (95% CI, 85.9-91.9%), but the diagnostic value would be overestimated because of selection bias in the six two-gate studies. The sensitivity and specificity of the single-gate studies were 100.0% (95% CI, 92.2-100.0%) and 30.6% (95% CI, 15.5-35.6%) in children and 81.0% (95% CI, 78.2-93.2%) and 85.7% specificity (95% CI, 78.2-93.2%) in adults. Conclusion:This study determines that there are insufficient studies of lateral neck radiograph for detecting supraglottitis. Lateral radiograph of the neck seems to have moderate accuracy for detecting supraglottitis. Further approximately unbiased studies are needed to obtain more valid and reliable estimates of test accuracy.
| S-EPMC5667265 | biostudies-literature
Project description:Purpose: Manual interpretation of chest radiographs is a challenging task and is prone to errors. An automated system capable of categorizing chest radiographs based on the pathologies identified could aid in the timely and efficient diagnosis of chest pathologies. Method: For this retrospective study, 4476 chest radiographs were collected between January and April 2021 from two tertiary care hospitals. Three expert radiologists established the ground truth, and all radiographs were analyzed using a deep-learning AI model to detect suspicious ROIs in the lungs, pleura, and cardiac regions. Three test readers (different from the radiologists who established the ground truth) independently reviewed all radiographs in two sessions (unaided and AI-aided mode) with a washout period of one month. Results: The model demonstrated an aggregate AUROC of 91.2% and a sensitivity of 88.4% in detecting suspicious ROIs in the lungs, pleura, and cardiac regions. These results outperform unaided human readers, who achieved an aggregate AUROC of 84.2% and sensitivity of 74.5% for the same task. When using AI, the aided readers obtained an aggregate AUROC of 87.9% and a sensitivity of 85.1%. The average time taken by the test readers to read a chest radiograph decreased by 21% (p < 0.01) when using AI. Conclusion: The model outperformed all three human readers and demonstrated high AUROC and sensitivity across two independent datasets. When compared to unaided interpretations, AI-aided interpretations were associated with significant improvements in reader performance and chest radiograph interpretation time.
| S-EPMC9914339 | biostudies-literature
Project description:The European Quality of Government Index (EQI) measures the perceived level of government quality by European Union citizens, combining surveys on corruption, impartiality and quality of provided services. It is, thus, an index based on individual subjective evaluations. Understanding the most relevant objective factors affecting the EQI outcomes is important for both evaluators and policy makers, especially in view of the fact that perception of government integrity contributes to determine the level of civic engagement. In our research, we employ methods of Artificial Intelligence and complex systems physics to measure the impact on the perceived government quality of multifaceted variables, describing territorial development and citizen well-being, from an economic, social and environmental viewpoint. Our study, focused on a set of regions in European Union at a subnational scale, leads to identifying the territorial and demographic drivers of citizens' confidence in government institutions. In particular, we find that the 2021 EQI values are significantly related to two indicators: the first one is the difference between female and male labour participation rates, and the second one is a proxy of wealth and welfare such as the average number of rooms per inhabitant. This result corroborates the idea of a central role played by labour gender equity and housing policies in government confidence building. In particular, the relevance of the former indicator in EQI prediction results from a combination of positive conditions such as equal job opportunities, vital labour market, welfare and availability of income sources, while the role of the latter is possibly amplified by the lockdown policies related to the COVID-19 pandemics. The analysis is based on combining regression, to predict EQI from a set of publicly available indicators, with the eXplainable Artificial Intelligence approach, that quantifies the impact of each indicator on the prediction. Such a procedure does not require any ad-hoc hypotheses on the functional dependence of EQI on the indicators used to predict it. Finally, using network science methods concerning community detection, we investigate how the impact of relevant indicators on EQI prediction changes throughout European regions. Thus, the proposed approach enables to identify the objective factors at the basis of government quality perception by citizens in different territorial contexts, providing the methodological basis for the development of a quantitative tool for policy design.
| S-EPMC9841965 | biostudies-literature
Project description:ObjectiveThe patellofemoral joint is frequently affected by osteoarthritis (PFOA) and is incompletely imaged on radiographs (XR). Weight-Bearing CT (WBCT) could offer advantages for visualization. This study determined the sensitivity, specificity, and accuracy of axial WBCT and lateral XR for detection of PFOA features in comparison with cartilage damage on MRI.DesignA convenience sample of 60 right knees from the MOST cohort were analyzed. WBCT and XR were read for OARSI JSN score and MRI for MOAKS cartilage score by two experienced musculoskeletal radiologists blinded to participant. Using MOAKS scoring on MRI (referent standard), the sensitivity, specificity and accuracy of patellofemoral OARSI JSN scores based on WBCT and XR were compared.ResultsThe mean ± SD age and BMI for the participants included (66.7% women) were 67.6 ± 9.8 years and 30.0 ± 5.3 kg/m2 respectively. WBCT demonstrated significantly greater sensitivity (0.85-0.97 on WBCT vs 0.47-0.57 on XR) and accuracy (0.85-0.92 on WBCT vs 0.48-0.57 on XR) for all parameters except lateral full-thickness cartilage loss (McNemar's test p-values all <0.001). There was moderate-to-strong and low-to-moderate agreement between PFOA findings on WBCT and XR, respectively, and semi-quantitative scores of PF cartilage on MRI. Inter-rater reliability for XR JSN [weighted kappa = 0.83 (0.64, 1.0)], WBCT JSN [kappa = 0.60 (0.48, 0.72)] and MRI MOAKS-CM [kappa = 0.70 (0.61, 0.79)] readings were good.ConclusionWBCT demonstrates significantly greater sensitivity and accuracy than radiographs for identification of PFOA. Given the same Relative Radiation Level as XR and improved visualization, WBCT holds promise to improve understanding of the weight-bearing patellofemoral joint.
| S-EPMC8542599 | biostudies-literature
Project description:BackgroundA number of radiographic signs have been previously demonstrated to be associated with degenerative rotator cuff tears. An ability to predict the presence of a tear by radiography would permit the early commencement of appropriate treatment and the avoidance of unnecessary invasive investigations. The aim of the present study was to determine the accuracy of using radiographic signs to predict the presence of a cuff tear on arthroscopy.MethodsFifty consecutive patients who had undergone shoulder arthroscopy and had pre-operative plain radiographs were included. Pre-operative radiographs were reviewed by a consultant shoulder surgeon, a consultant radiologist and a senior clinical fellow for the following signs: acromial spur; subjective reduction of subacromial space; sourcil sign; acromial acetabularization; os acromiale; greater tuberosity cortical irregularity; greater tuberosity sclerosis; humeral head rounding; cyst; and reduction in acromiohumeral head distance.ResultsThe presence of tuberosity sclerosis (p < 0.0001), tuberosity irregularities (p < 0.0001), tuberosity cyst (p = 0.004) and sourcil sign (p = 0.019) was associated with the presence of a rotator cuff tear. The combined sensitivity of prediction of tear by the observers following radiographic review was 91.7%, with a combined negative predictive value of 80%.ConclusionsThe assessment of radiographs by senior clinicians is a useful tool for confirming the absence of a rotator cuff tear.
| S-EPMC6463379 | biostudies-literature
Project description:When dentists see pediatric patients with more complex tooth development than adults during tooth replacement, they need to manually determine the patient's disease with the help of preoperative dental panoramic radiographs. To the best of our knowledge, there is no international public dataset for children's teeth and only a few datasets for adults' teeth, which limits the development of deep learning algorithms for segmenting teeth and automatically analyzing diseases. Therefore, we collected dental panoramic radiographs and cases from 106 pediatric patients aged 2 to 13 years old, and with the help of the efficient and intelligent interactive segmentation annotation software EISeg (Efficient Interactive Segmentation) and the image annotation software LabelMe. We propose the world's first dataset of children's dental panoramic radiographs for caries segmentation and dental disease detection by segmenting and detecting annotations. In addition, another 93 dental panoramic radiographs of pediatric patients, together with our three internationally published adult dental datasets with a total of 2,692 images, were collected and made into a segmentation dataset suitable for deep learning.
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Project description:Offspring of individuals with psychoses sometimes display an abnormal development of cognition, language, motor performance, social adaptation, and emotional functions. The aim of this study was to investigate the ability of children of mothers with schizophrenia (n = 28) and bipolar disorder (n = 23) to understand mental states of others using the Eyes Test (folk psychology or "theory of mind") and physical causal interactions of inanimate objects (folk physics). Compared with healthy controls (n = 29), the children of mothers with schizophrenia displayed significantly impaired performances on the Eyes Test but not on the folk physics test when corrected for IQ. The children of mothers with bipolar disorder did not differ from the controls. The folk physics test showed a significant covariance with IQ, whereas the Eyes Test did not exhibit such covariance. These results suggest that the attribution of mental states, but not the interpretation of causal interaction of objects, is impaired in offspring of individuals with schizophrenia, which may contribute to social dysfunctions.
| S-EPMC3976113 | biostudies-literature
Project description:Confidence ratings (CR) are one of the most frequently used measures in psychological research. However, recent evidence has suggested that eliciting CR from participants may result in changes to cognitive performance, so called reactivity. Here, we examine whether reactivity to CR can be better explained by added task-relevant introspection, or, alternatively, the unintentional priming of confidence-related beliefs. First, we compare participants' performance in a group making CR with a group making a task-irrelevant control rating, and a second group who made the same task-irrelevant rating, but with the word 'confident' included in the rating's wording. The results suggest that reactivity is driven by the presentation of the word 'confident', and reactivity does not require task-relevant introspection. Additionally, we show that rephrasing CR to remove the word 'confident' neutralises reactivity. This suggests that reactivity may represent a significant problem for researchers using CR, but rephrasing CR may remedy these concerns in relatively simple fashion.
| S-EPMC6557865 | biostudies-other
Project description:BackgroundIn addition to single-locus (main) effects of disease variants, there is a growing consensus that gene-gene and gene-environment interactions may play important roles in disease etiology. However, for the very large numbers of genetic markers currently in use, it has proven difficult to develop suitable and efficient approaches for detecting effects other than main effects due to single variants.ResultsWe developed a method for jointly detecting disease-causing single-locus effects and gene-gene interactions. Our method is based on finding differences of genotype pattern frequencies between case and control individuals. Those single-nucleotide polymorphism markers with largest single-locus association test statistics are included in a pattern. For a logistic regression model comprising three disease variants exerting main and epistatic interaction effects, we demonstrate that our method is vastly superior to the traditional approach of looking for single-locus effects. In addition, our method is suitable for estimating the number of disease variants in a dataset. We successfully apply our approach to data on Parkinson Disease and heroin addiction.ConclusionOur approach is suitable and powerful for detecting disease susceptibility variants with potentially small main effects and strong interaction effects. It can be applied to large numbers of genetic markers.
| S-EPMC2648768 | biostudies-literature