Man versus machine? Self-reports versus algorithmic measurement of publications.
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ABSTRACT:
SUBMITTER: Jiang X
PROVIDER: S-EPMC8480886 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background and purposeReproducible segmentation of brain tumors on magnetic resonance images is an important clinical need. This study was designed to evaluate the reliability of a novel fully automated segmentation tool for brain tumor image analysis in comparison to manually defined tumor segmentations.MethodsWe prospectively evaluated preoperative MR Images from 25 glioblastoma patients. Two independent expert raters performed manual segmentations. Automatic segmentations were performed using the Brain Tumor Image Analysis software (BraTumIA). In order to study the different tumor compartments, the complete tumor volume TV (enhancing part plus non-enhancing part plus necrotic core of the tumor), the TV+ (TV plus edema) and the contrast enhancing tumor volume CETV were identified. We quantified the overlap between manual and automated segmentation by calculation of diameter measurements as well as the Dice coefficients, the positive predictive values, sensitivity, relative volume error and absolute volume error.ResultsComparison of automated versus manual extraction of 2-dimensional diameter measurements showed no significant difference (p = 0.29). Comparison of automated versus manual segmentation of volumetric segmentations showed significant differences for TV+ and TV (p<0.05) but no significant differences for CETV (p>0.05) with regard to the Dice overlap coefficients. Spearman's rank correlation coefficients (?) of TV+, TV and CETV showed highly significant correlations between automatic and manual segmentations. Tumor localization did not influence the accuracy of segmentation.ConclusionsIn summary, we demonstrated that BraTumIA supports radiologists and clinicians by providing accurate measures of cross-sectional diameter-based tumor extensions. The automated volume measurements were comparable to manual tumor delineation for CETV tumor volumes, and outperformed inter-rater variability for overlap and sensitivity.
| S-EPMC4013039 | biostudies-literature
Project description:There has been recent promising experimental and theoretical evidence that quantum computational tools might enhance the precision and efficiency of physical experiments. However, a systematic treatment and comprehensive framework are missing. Here we initiate the systematic study of experimental quantum physics from the perspective of computational complexity. To this end, we define the framework of quantum algorithmic measurements (QUALMs), a hybrid of black box quantum algorithms and interactive protocols. We use the QUALM framework to study two important experimental problems in quantum many-body physics: determining whether a system's Hamiltonian is time-independent or time-dependent, and determining the symmetry class of the dynamics of the system. We study abstractions of these problems and show for both cases that if the experimentalist can use her experimental samples coherently (in both space and time), a provable exponential speedup is achieved compared to the standard situation in which each experimental sample is accessed separately. Our work suggests that quantum computers can provide a new type of exponential advantage: exponential savings in resources in quantum experiments.
| S-EPMC8850572 | biostudies-literature
Project description: Not available
| S-EPMC4353284 | biostudies-literature
Project description:Self-assembly has emerged as a paradigm for highly parallel fabrication of complex three-dimensional structures. However, there are few principles that guide a priori design, yield, and defect tolerance of self-assembling structures. We examine with experiment and theory the geometric principles that underlie self-folding of submillimeter-scale higher polyhedra from two-dimensional nets. In particular, we computationally search for nets within a large set of possibilities and then test these nets experimentally. Our main findings are that (i) compactness is a simple and effective design principle for maximizing the yield of self-folding polyhedra; and (ii) shortest paths from 2D nets to 3D polyhedra in the configuration space are important for rationalizing experimentally observed folding pathways. Our work provides a model problem amenable to experimental and theoretical analysis of design principles and pathways in self-assembly.
| S-EPMC3250184 | biostudies-literature
Project description:BackgroundMuch of the epidemiologic research on risk factors for fibroids, the leading indication for hysterectomy, relies on self-reported outcome. Self-report is subject to misclassification because many women with fibroids are undiagnosed. The purpose of this analysis was to quantify the extent of misclassification and identify associated factors.MethodsSelf-reported fibroid status was compared to ultrasound screening from 2046 women in Right From The Start (RFTS) and 869 women in the Uterine Fibroid Study (UFS). Log-binomial regression was used to estimate sensitivity (Se) and specificity (Sp) and examine differences by ethnicity, age, education, body mass index, parity, and miscarriage history.ResultsOverall sensitivity was ?0.50. Sensitivity was higher in blacks than whites (RFTS: 0.34 vs. 0.23; UFS: 0.58 vs. 0.32) and increased with age. Parous women had higher sensitivity than nulliparae, especially in RFTS whites (Se ratio=2.90; 95% confidence interval [CI]: 1.51, 5.60). Specificity was 0.98 in RFTS and 0.86 in UFS. Modest ethnic differences were seen in UFS (Sp ratio, black vs. white=0.90; 95% CI: 0.81, 0.99). Parity was inversely associated with specificity, especially among UFS black women (Sp ratio=0.84; 95% CI: 0.73, 0.97). Among women who reported a previous diagnosis, a shorter time interval between diagnosis and ultrasound was associated with increased agreement between the two measures.ConclusionsMisclassification of fibroid status can differ by factors of etiologic interest. These findings are useful for assessing (and correcting) bias in studies using self-reported clinical diagnosis as the outcome measure.
| S-EPMC3298676 | biostudies-literature
Project description:BackgroundGoogle Translate offers free Web-based translation, but it is unknown whether its translation accuracy is sufficient to use in systematic reviews to mitigate concerns about language bias.MethodsWe compared data extraction from non-English language studies with extraction from translations by Google Translate of 10 studies in each of five languages (Chinese, French, German, Japanese and Spanish). Fluent speakers double-extracted original-language articles. Researchers who did not speak the given language double-extracted translated articles along with 10 additional English language trials. Using the original language extractions as a gold standard, we estimated the probability and odds ratio of correctly extracting items from translated articles compared with English, adjusting for reviewer and language.ResultsTranslation required about 30 minutes per article and extraction of translated articles required additional extraction time. The likelihood of correct extractions was greater for study design and intervention domain items than for outcome descriptions and, particularly, study results. Translated Spanish articles yielded the highest percentage of items (93%) that were correctly extracted more than half the time (followed by German and Japanese 89%, French 85%, and Chinese 78%) but Chinese articles yielded the highest percentage of items (41%) that were correctly extracted >98% of the time (followed by Spanish 30%, French 26%, German 22%, and Japanese 19%). In general, extractors' confidence in translations was not associated with their accuracy.ConclusionsTranslation by Google Translate generally required few resources. Based on our analysis of translations from five languages, using machine translation has the potential to reduce language bias in systematic reviews; however, pending additional empirical data, reviewers should be cautious about using translated data. There remains a trade-off between completeness of systematic reviews (including all available studies) and risk of error (due to poor translation).
| S-EPMC4226266 | biostudies-literature
Project description:Algorithms and information, fundamental to technological and biological organization, are also an essential aspect of many elementary physical phenomena, such as molecular self-assembly. Here we report the molecular realization, using two-dimensional self-assembly of DNA tiles, of a cellular automaton whose update rule computes the binary function XOR and thus fabricates a fractal pattern--a Sierpinski triangle--as it grows. To achieve this, abstract tiles were translated into DNA tiles based on double-crossover motifs. Serving as input for the computation, long single-stranded DNA molecules were used to nucleate growth of tiles into algorithmic crystals. For both of two independent molecular realizations, atomic force microscopy revealed recognizable Sierpinski triangles containing 100-200 correct tiles. Error rates during assembly appear to range from 1% to 10%. Although imperfect, the growth of Sierpinski triangles demonstrates all the necessary mechanisms for the molecular implementation of arbitrary cellular automata. This shows that engineered DNA self-assembly can be treated as a Turing-universal biomolecular system, capable of implementing any desired algorithm for computation or construction tasks.
| S-EPMC534809 | biostudies-other
Project description:ImportancePredictive models using machine learning techniques have potential to improve early detection and management of Alzheimer disease (AD). However, these models potentially have biases and may perpetuate or exacerbate existing disparities.ObjectiveTo characterize the algorithmic fairness of longitudinal prediction models for AD progression.Design, setting, and participantsThis prognostic study investigated the algorithmic fairness of logistic regression, support vector machines, and recurrent neural networks for predicting progression to mild cognitive impairment (MCI) and AD using data from participants in the Alzheimer Disease Neuroimaging Initiative evaluated at 57 sites in the US and Canada. Participants aged 54 to 91 years who contributed data on at least 2 visits between September 2005 and May 2017 were included. Data were analyzed in October 2022.ExposuresFairness was quantified across sex, ethnicity, and race groups. Neuropsychological test scores, anatomical features from T1 magnetic resonance imaging, measures extracted from positron emission tomography, and cerebrospinal fluid biomarkers were included as predictors.Main outcomes and measuresOutcome measures quantified fairness of prediction models (logistic regression [LR], support vector machine [SVM], and recurrent neural network [RNN] models), including equal opportunity, equalized odds, and demographic parity. Specifically, if the model exhibited equal sensitivity for all groups, it aligned with the principle of equal opportunity, indicating fairness in predictive performance.ResultsA total of 1730 participants in the cohort (mean [SD] age, 73.81 [6.92] years; 776 females [44.9%]; 69 Hispanic [4.0%] and 1661 non-Hispanic [96.0%]; 29 Asian [1.7%], 77 Black [4.5%], 1599 White [92.4%], and 25 other race [1.4%]) were included. Sensitivity for predicting progression to MCI and AD was lower for Hispanic participants compared with non-Hispanic participants; the difference (SD) in true positive rate ranged from 20.9% (5.5%) for the RNN model to 27.8% (9.8%) for the SVM model in MCI and 24.1% (5.4%) for the RNN model to 48.2% (17.3%) for the LR model in AD. Sensitivity was similarly lower for Black and Asian participants compared with non-Hispanic White participants; for example, the difference (SD) in AD true positive rate was 14.5% (51.6%) in the LR model, 12.3% (35.1%) in the SVM model, and 28.4% (16.8%) in the RNN model for Black vs White participants, and the difference (SD) in MCI true positive rate was 25.6% (13.1%) in the LR model, 24.3% (13.1%) in the SVM model, and 6.8% (18.7%) in the RNN model for Asian vs White participants. Models generally satisfied metrics of fairness with respect to sex, with no significant differences by group, except for cognitively normal (CN)-MCI and MCI-AD transitions (eg, an absolute increase [SD] in the true positive rate of CN-MCI transitions of 10.3% [27.8%] for the LR model).Conclusions and relevanceIn this study, models were accurate in aggregate but failed to satisfy fairness metrics. These findings suggest that fairness should be considered in the development and use of machine learning models for AD progression.
| S-EPMC10630899 | biostudies-literature
Project description:Reconstructing force fields (FFs) from atomistic simulation data is a challenge since accurate data can be highly expensive. Here, machine learning (ML) models can help to be data economic as they can be successfully constrained using the underlying symmetry and conservation laws of physics. However, so far, every descriptor newly proposed for an ML model has required a cumbersome and mathematically tedious remodeling. We therefore propose using modern techniques from algorithmic differentiation within the ML modeling process, effectively enabling the usage of novel descriptors or models fully automatically at an order of magnitude higher computational efficiency. This paradigmatic approach enables not only a versatile usage of novel representations and the efficient computation of larger systems─all of high value to the FF community─but also the simple inclusion of further physical knowledge, such as higher-order information (e.g., Hessians, more complex partial differential equations constraints etc.), even beyond the presented FF domain.
| S-EPMC9639201 | biostudies-literature
Project description:BackgroundSimulation based medical education is efficient for the acquisition of flexible bronchoscopy navigational skills and the knowledge of the tracheobronchial anatomy. However, bronchoscopy simulator training is not routinely integrated into pneumologic fellowship programs or undergraduate medical education for time and/or cost reasons. Our study compares the effect of self-guided bronchoscopy simulator training versus tutor guided training on the acquisition of navigational skills and knowledge of the bronchial anatomy.MethodsThird-year undergraduate medical students were randomized to either a tutor- or simulator guided bronchoscopy simulator training focusing on the acquisition of navigational skills and the knowledge of the tracheobronchial anatomy. Every student performed a baseline bronchoscopy followed by a structured bronchoscopy simulator training and finally an assessment bronchoscopy at the end of the training program. Groups were compared by means of a repeated measurement ANOVA and effect sizes calculated as Cohens' d.ResultsFifty-four eligible students participated in the study. Knowledge of the tracheobronchial anatomy significantly increased from pre- to post training (all p < 0.001; all d > 2), navigational skills significantly decreased (all p < 0.005; all d < 1). There were no significant differences between groups. Instruction by the simulator as well as by the tutor was rated as helpful by the students. Twenty-two (84.6%) of the participants of the simulator guided group would have appreciated an additional instruction by a tutor.ConclusionShort-time simulator guided bronchoscopy training improves knowledge of the tracheobronchial anatomy in novice bronchoscopists as much as tutor guided training, but navigational skills seem to worsen in both groups. Further studies assessing transfer to clinical practice are needed to find the optimal teaching method for basic flexible bronchoscopy.
| S-EPMC7898762 | biostudies-literature