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Intelligence Testing

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61. Comparing artificial intelligence algorithms to 157 German dermatologists: the melanoma classification benchmark. (PubMed)

Comparing artificial intelligence algorithms to 157 German dermatologists: the melanoma classification benchmark. Several recent publications have demonstrated the use of convolutional neural networks to classify images of melanoma at par with board-certified dermatologists. However, the non-availability of a public human benchmark restricts the comparability of the performance of these algorithms and thereby the technical progress in this field.An electronic questionnaire was sent (...) the patient). Main outcome measures were sensitivity, specificity and the receiver operating characteristics (ROC).Total 157 dermatologists assessed all 100 dermoscopic images with an overall sensitivity of 74.1%, specificity of 60.0% and an ROC of 0.67 (range = 0.538-0.769); 145 dermatologists assessed all 100 clinical images with an overall sensitivity of 89.4%, specificity of 64.4% and an ROC of 0.769 (range = 0.613-0.9). Results between test-sets were significantly different (P < 0.05) confirming

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2019 European Journal of Cancer

62. Social cognition in bipolar disorder: the role of sociodemographic, clinical and neurocognitive variables in emotional intelligence. (PubMed)

HC. EI was evaluated using the Mayer Salovey Caruso Emotional Intelligence Test (MSCEIT). The sociodemographic, clinical and neurocognitive variables that showed a significant association with EI were entered into hierarchical multiple regression analysis.BD patients obtained significantly lower scores compared to HC in the Emotional Intelligence quotient (EIQ) and in the Understanding Emotions branch score. The best fitting model for the variables associated with EI in the patients group (...) Social cognition in bipolar disorder: the role of sociodemographic, clinical and neurocognitive variables in emotional intelligence. The main aims of this study were to examine the differences in the Emotional Intelligence (EI), the emotional domain of social cognition (SC), between euthymic patients with bipolar disorder (BD) and healthy controls (HC) and to evaluate the contribution of sociodemographic, clinical and neuropsychological variables to EI.We recruited 202 patients with BD and 50

2019 Acta Psychiatrica Scandinavica

63. Crystallized and fluid intelligence of university students with intellectual disability who are fully integrated versus those who studied in adapted enrichment courses. (PubMed)

: (a) separate adapted enrichment model: students with ID study in separate enrichment courses adapted to their level; (b) full inclusion model: students with ID are included in undergraduate courses, receive academic credits and are expected to accumulate the amount of credits for a B.A.(a) To examine whether crystallized and fluid intelligence and cognitive tests can serve as screening tests for determining the appropriate placement of students with ID for the adapted enrichment model versus the full (...) inclusion model. (b) To examine the attitudes towards the program of students with ID in the inclusion model.The sample included 31 adults with ID: students with ID who were fully included (N = 10) and students with ID who participated in the adapted enrichment model (N = 21). Crystallized and fluid intelligence were examined (WAIS-III, Wechsler, 1997) and Hebrew abstract verbal tests (Glanz, 1989). Semi-structured interviews were conducted in order to examine the attitudes of students in the inclusion

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2018 PLoS ONE

64. Will artificial intelligence be able to have this conversation?

Will artificial intelligence be able to have this conversation? Will artificial intelligence be able to have this conversation? Will artificial intelligence be able to have this conversation? | | February 5, 2019 14 Shares Transformation of health care is underway: The landscape is filled with innovation, and the horizon is dotted with technological possibilities of “Star Trek” ilk. In a recent New England Journal of Medicine Catalyst article, there was a compelling argument for how artificial (...) intelligence (AI) of the future will help deliver us from a high cost, high variability, poorly resourced state and help deliver the IHI triple aim quest we in health care have been on this past decade. At a recent national Health Analytics Summit, the plenary speaker Dr. Daniel Kraft sparked our imaginations and whetted our appetites with advancements that have already been created but not commercially available. Recently I saw one of my “regular” octogenarian patients for a blood pressure check she

2019 KevinMD blog

65. What bridges the gap between emotional intelligence and suicide risk in victims of bullying? A moderated mediation study. (PubMed)

What bridges the gap between emotional intelligence and suicide risk in victims of bullying? A moderated mediation study. Recent research has demonstrated that victims of bullying report serious problems, such as depression, which heighten the risk of suicide. Emotional intelligence (EI) has been increasingly recognized as a protective factor, but little is known about the mechanisms underlying the relation between EI and suicide risk in victims. This study aimed to test a theoretical model

2019 Journal of Affective Disorders

66. Application of Artificial Intelligence for Preoperative Diagnostic and Prognostic Prediction in Epithelial Ovarian Cancer Based on Blood Biomarkers. (PubMed)

Application of Artificial Intelligence for Preoperative Diagnostic and Prognostic Prediction in Epithelial Ovarian Cancer Based on Blood Biomarkers. Purpose: We aimed to develop an ovarian cancer-specific predictive framework for clinical stage, histotype, residual tumor burden, and prognosis using machine learning methods based on multiple biomarkers.Experimental Design: Overall, 334 patients with epithelial ovarian cancer (EOC) and 101 patients with benign ovarian tumors were randomly (...) assigned to "training" and "test" cohorts. Seven supervised machine learning classifiers, including Gradient Boosting Machine (GBM), Support Vector Machine, Random Forest (RF), Conditional RF (CRF), Naïve Bayes, Neural Network, and Elastic Net, were used to derive diagnostic and prognostic information from 32 parameters commonly available from pretreatment peripheral blood tests and age.Results: Machine learning techniques were superior to conventional regression-based analyses in predicting multiple

2019 Clinical Cancer Research

67. Intelligence moderates the relationship between age and inter-connectivity of resting state networks in older adults. (PubMed)

state functional magnetic resonance imaging data. Global inter-connectivity between DMN and 9 other resting state networks was calculated using a novel computational framework based on machine learning. Intellectual functioning (intelligence) was estimated using the Wechsler Test of Adult Reading. A significant, positive relationship was found between age and global inter-network connectivity (r = 0.31, p = 0.029). Moderation analyses yielded a significant age × intelligence interaction term (p (...) Intelligence moderates the relationship between age and inter-connectivity of resting state networks in older adults. Age-related changes in the interactive behavior of default mode network (DMN) with other resting state networks are poorly understood. We hypothesized that age would positively correlate with inter-network connectivity in late life and intellectual functioning was expected to moderate this relationship. The sample consisted of 48 community-dwelling older adults with resting

2019 Neurobiology of Aging

68. Artificial intelligence detection of distal radius fractures: a comparison between the convolutional neural network and professional assessments. (PubMed)

Artificial intelligence detection of distal radius fractures: a comparison between the convolutional neural network and professional assessments. Background and purpose - Artificial intelligence has rapidly become a powerful method in image analysis with the use of convolutional neural networks (CNNs). We assessed the ability of a CNN, with a fast object detection algorithm previously identifying the regions of interest, to detect distal radius fractures (DRFs) on anterior-posterior (AP) wrist (...) groups. Results - The object detection model achieved a high average IOU, and none of the IOUs had a value less than 0.5. The AUC of the CNN for this test was 0.96. The network had better performance in distinguishing images with DRFs from normal images compared with a group of radiologists in terms of the accuracy, sensitivity, specificity, and Youden Index. The network presented a similar diagnostic performance to that of the orthopedists in terms of these variables. Interpretation - The network

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2019 Acta Orthopaedica

69. Feasibility of a Real-Time Clinical Augmented Reality and Artificial Intelligence Framework for Pain Detection and Localization From the Brain. (PubMed)

Feasibility of a Real-Time Clinical Augmented Reality and Artificial Intelligence Framework for Pain Detection and Localization From the Brain. For many years, clinicians have been seeking for objective pain assessment solutions via neuroimaging techniques, focusing on the brain to detect human pain. Unfortunately, most of those techniques are not applicable in the clinical environment or lack accuracy.This study aimed to test the feasibility of a mobile neuroimaging-based clinical augmented (...) reality (AR) and artificial intelligence (AI) framework, CLARAi, for objective pain detection and also localization direct from the patient's brain in real time.Clinical dental pain was triggered in 21 patients by hypersensitive tooth stimulation with 20 consecutive descending cold stimulations (32°C-0°C). We used a portable optical neuroimaging technology, functional near-infrared spectroscopy, to gauge their cortical activity during evoked acute clinical pain. The data were decoded using a neural

2019 Journal of medical Internet research

70. Using Artificial Intelligence to Revise ACR TI-RADS Risk Stratification of Thyroid Nodules: Diagnostic Accuracy and Utility. (PubMed)

Using Artificial Intelligence to Revise ACR TI-RADS Risk Stratification of Thyroid Nodules: Diagnostic Accuracy and Utility. Background Risk stratification systems for thyroid nodules are often complicated and affected by low specificity. Continual improvement of these systems is necessary to reduce the number of unnecessary thyroid biopsies. Purpose To use artificial intelligence (AI) to optimize the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS (...) of the systems was compared by using a test set of the final 100 nodules with interpretations from the expert reader, eight nonexpert readers, and an expert panel. Initial performance of AI TI-RADS was calculated by using a test for differences between binomial proportions. Additional comparisons across readers were conducted by using bootstrapping; diagnostic performance was assessed by using area under the receiver operating curve. Results AI TI-RADS assigned new point values for eight ACR TI-RADS features

2019 Radiology

71. Assessing the effectiveness of artificial intelligence methods for melanoma:A retrospective review. (PubMed)

Assessing the effectiveness of artificial intelligence methods for melanoma:A retrospective review. [Artificial intelligence methods for the classification of melanoma have been studied extensively. However, few studies compare these methods under the same standards.] OBJECTIVE: [Seek the best artificial intelligence method for diagnosis of melanoma.] METHODS: [A total of 2200 dermoscopy images were used for contrast test. Image segmentations, feature extractions and classifications were

2019 Journal of American Academy of Dermatology

72. Artificial intelligence based discovery of the association between depression and chronic fatigue syndrome. (PubMed)

Zung Self-Rating Depression Scales and Chalder Fatigue Scales, and meanwhile, to donate plasma and urine samples for 1H NMR-metabolic profiling. Artificial intelligence methods was used to analysis the underlying association between DD and CFS. Principal components analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to analyze the metabolic profiles with respect to gender and age. Variable importance in projection and t-test were employed in conjunction with the PLS-DA (...) Artificial intelligence based discovery of the association between depression and chronic fatigue syndrome. Both of the modern medicine and the traditional Chinese medicine classify depressive disorder (DD) and chronic fatigue syndrome (CFS) to one type of disease. Unveiling the association between depressive and the fatigue diseases provides a great opportunity to bridge the modern medicine with the traditional Chinese medicine.In this work, 295 general participants were recruited to complete

2019 Journal of Affective Disorders

73. Impact of gestational age on child intelligence, attention and executive function at age 5: a cohort study. (PubMed)

in a population of Danish children aged 5 years.Population-based prospective cohort study.Denmark 2003-2008.A cohort of 1776 children and their mothers sampled from the Danish National Birth Cohort with information on GA, family and background factors and completed neuropsychological assessment at age 5.Wechsler Preschool and Primary Scale of Intelligence-Revised, Test of Everyday Attention for Children at Five and Behaviour Rating Inventory of Executive Function scores.For preterm birth <34 weeks GA (n=8 (...) Impact of gestational age on child intelligence, attention and executive function at age 5: a cohort study. Preterm birth can affect cognition, but other factors including parental education and intelligence may also play a role, but few studies have adjusted for these potential confounders. We aimed to assess the impact of gestational age (GA), late preterm birth (34 to <37 weeks GA) and very to moderately preterm birth (<34 weeks GA) on intelligence, attention and executive function

2019 BMJ open

74. Intelligence in young adulthood and alcohol use disorders in a prospective cohort study of Danish men: the role of psychiatric disorders and parental psychiatric history. (PubMed)

of intelligence test scores from draft board examinations and register data on AUD diagnoses during 36 years of follow-up.Denmark.3287 Danish men from the Copenhagen Perinatal Cohort (born 1959-1961) who appeared before the draft board at a mean age of 18.7 years.First registration with AUD during follow-up was the primary outcome. Information on AUD was based on diagnoses retrieved from national hospital and outpatient treatment registers, defined according to the International Classification of Diseases.361 (...) Intelligence in young adulthood and alcohol use disorders in a prospective cohort study of Danish men: the role of psychiatric disorders and parental psychiatric history. The aims were to estimate the association between intelligence measured in young adulthood and risk of alcohol use disorders (AUD) in men and to investigate the potential modification of this association by psychiatric disorders, parental AUD and parental psychiatric disorders.Prospective cohort study based on a linkage

2019 BMJ open

75. Age and Sex Estimation Using Artificial Intelligence From Standard 12-Lead ECGs. (PubMed)

Age and Sex Estimation Using Artificial Intelligence From Standard 12-Lead ECGs. Sex and age have long been known to affect the ECG. Several biologic variables and anatomic factors may contribute to sex and age-related differences on the ECG. We hypothesized that a convolutional neural network (CNN) could be trained through a process called deep learning to predict a person's age and self-reported sex using only 12-lead ECG signals. We further hypothesized that discrepancies between CNN (...) -predicted age and chronological age may serve as a physiological measure of health.We trained CNNs using 10-second samples of 12-lead ECG signals from 499 727 patients to predict sex and age. The networks were tested on a separate cohort of 275 056 patients. Subsequently, 100 randomly selected patients with multiple ECGs over the course of decades were identified to assess within-individual accuracy of CNN age estimation.Of 275 056 patients tested, 52% were males and mean age was 58.6±16.2 years

2019 Circulation. Arrhythmia and electrophysiology

76. Artificial Intelligence for Aortic Pressure Waveform Analysis During Coronary Angiography: Machine Learning for Patient Safety. (PubMed)

Artificial Intelligence for Aortic Pressure Waveform Analysis During Coronary Angiography: Machine Learning for Patient Safety. The study developed a neural network to perform automated pressure waveform analysis and allow real-time accurate identification of damping.Damping of aortic pressure during coronary angiography must be identified to avoid serious complications and make accurate coronary physiology measurements. There are currently no automated methods to do this, and so identification (...) of damping requires constant monitoring, which is prone to human error.The neural network was trained and tested versus core laboratory expert opinions derived from 2 separate datasets. A total of 5,709 aortic pressure waveforms of individual heart beats were extracted and classified. The study developed a recurrent convolutional neural network to classify beats as either normal, showing damping, or artifactual. Accuracies were reported using the opinions of 2 independent core laboratories.The neural

2019 JACC. Cardiovascular interventions

77. Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study. (PubMed)

Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study. Upper gastrointestinal cancers (including oesophageal cancer and gastric cancer) are the most common cancers worldwide. Artificial intelligence platforms using deep learning algorithms have made remarkable progress in medical imaging but their application in upper gastrointestinal cancers has been limited. We aimed to develop and validate (...) the Gastrointestinal Artificial Intelligence Diagnostic System (GRAIDS) for the diagnosis of upper gastrointestinal cancers through analysis of imaging data from clinical endoscopies.This multicentre, case-control, diagnostic study was done in six hospitals of different tiers (ie, municipal, provincial, and national) in China. The images of consecutive participants, aged 18 years or older, who had not had a previous endoscopy were retrieved from all participating hospitals. All patients with upper gastrointestinal

2019 Lancet Oncology

78. Building the case for actionable ethics in digital health research supported by artificial intelligence. (PubMed)

data gathered using these technologies is unprecedented, and is increasingly leveraged to inform personalized health promotion and disease treatment interventions. The use of artificial intelligence in the health sector is also increasing. Although rich with potential, the digital health ecosystem presents new ethical challenges for those making decisions about the selection, testing, implementation and evaluation of technologies for use in healthcare. As the 'Wild West' of digital health research (...) Building the case for actionable ethics in digital health research supported by artificial intelligence. The digital revolution is disrupting the ways in which health research is conducted, and subsequently, changing healthcare. Direct-to-consumer wellness products and mobile apps, pervasive sensor technologies and access to social network data offer exciting opportunities for researchers to passively observe and/or track patients 'in the wild' and 24/7. The volume of granular personal health

2019 BMC Medicine

79. The effectiveness of cognitive rehabilitation program on auditory perception and verbal intelligibility of deaf children. (PubMed)

The effectiveness of cognitive rehabilitation program on auditory perception and verbal intelligibility of deaf children. Linguistic information and cognitive rehabilitation has more related with auditory perception and verbal intelligibility. The aim of the present study was to assessment of the effectiveness of cognitive rehabilitation program on the auditory perception and verbal intelligibility of deaf children. This study was a quasi-experimental study with pre-test, post-test and control (...) of Auditory Performance (CAP) and Speech Intelligibility Rating (SIR). The data were analyzed using multivariate analysis of covariance (MANCOVA) in 24th version of SPSS. The results of MANCOVA showed that cognitive rehabilitation program had significant effect on the auditory perception and verbal intelligibility in the experimental group at post intervention stage (P < 0/0001). There was a positive and significant increase in auditory perception and verbal intelligibility of experimental group. Our

2019 American Journal of Otolaryngology

80. The association of striatal volume and positive schizotypy in healthy subjects: intelligence as a moderating factor. (PubMed)

vulnerability to environmental insults, beneficial factors like general intelligence might buffer their effect.To further investigate the role of fronto-striatal networks in schizotypy, we examined the relationship of voxel- and surface-based brain morphometry and a measure of schizotypal traits (Schizotypal Personality Questionnaire, with subscores Cognitive-Perceptual, Interpersonal, Disorganised) in 115 healthy participants [54 female, mean age (s.d.) = 27.57(8.02)]. We tested intelligence (MWT-B (...) The association of striatal volume and positive schizotypy in healthy subjects: intelligence as a moderating factor. Schizotypy, a putative schizophrenia endophenotype, has been associated with brain-structural variations partly overlapping with those in psychotic disorders. Variations in precuneus structure have been repeatedly reported, whereas the involvement of fronto-striatal networks - as in schizophrenia - is less clear. While shared genetic architecture is thought to increase

2019 Psychological Medicine

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