How to Trip Rapid Review

Step 1: Select articles relevant to your search (remember the system is only optimised for single intervention studies)

Step 2: press

Step 3: review the result, and maybe amend the or if you know better! If we're unsure of the overall sentiment of the trial we will display the conclusion under the article title. We then require you to tell us what the correct sentiment is.

6,719 results for

Intelligence Testing

by
...
Latest & greatest
Alerts

Export results

Use check boxes to select individual results below

SmartSearch available

Trip's SmartSearch engine has discovered connected searches & results. Click to show

41. Development of an intelligent decision support system for ischemic stroke risk assessment in a population-based electronic health record database. Full Text available with Trip Pro

Development of an intelligent decision support system for ischemic stroke risk assessment in a population-based electronic health record database. Intelligent decision support systems (IDSS) have been applied to tasks of disease management. Deep neural networks (DNNs) are artificial intelligent techniques to achieve high modeling power. The application of DNNs to large-scale data for estimating stroke risk needs to be assessed and validated. This study aims to apply a DNN for deriving a stroke (...) predictive model using a big electronic health record database.The Taiwan National Health Insurance Research Database was used to conduct a retrospective population-based study. The database was divided into one development dataset for model training (~70% of total patients for training and ~10% for parameter tuning) and two testing datasets (each ~10%). A total of 11,192,916 claim records from 840,487 patients were used. The primary outcome was defined as any ischemic stroke in inpatient records within

2019 PLoS ONE

42. Artificial intelligence-guided tissue analysis combined with immune infiltrate assessment predicts stage III colon cancer outcomes in PETACC08 study. Full Text available with Trip Pro

Artificial intelligence-guided tissue analysis combined with immune infiltrate assessment predicts stage III colon cancer outcomes in PETACC08 study. Diagnostic tests, such as Immunoscore, predict prognosis in patients with colon cancer. However, additional prognostic markers could be detected on pathological slides using artificial intelligence tools.We have developed a software to detect colon tumour, healthy mucosa, stroma and immune cells on CD3 and CD8 stained slides. The lymphocyte (...) and clinical variables identified a group of patients with less than 10% relapse risk and another group with a 50% relapse risk.These findings suggest that artificial intelligence can potentially improve patient care by assisting pathologists in better defining stage III colon cancer patients' prognosis.© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

2020 Gut

43. Novel and conventional embryo parameters as input data for artificial neural networks: an artificial intelligence model applied for prediction of the implantation potential. (Abstract)

Novel and conventional embryo parameters as input data for artificial neural networks: an artificial intelligence model applied for prediction of the implantation potential. To describe novel embryo features capable of predicting implantation potential as input data for an artificial neural network (ANN) model.Retrospective cohort study.University-affiliated private IVF center.This study included 637 patients from the oocyte donation program who underwent single-blastocyst transfer during two (...) with the use of the area under the receiver operating characteristic curve (AUC).Out of the five novel described parameters, blastocyst expanded diameter and trophectoderm cell cycle length had statistically different values in implanted and nonimplanted embryos. After the ANN models were trained and validated using fivefold cross-validation, they were capable of predicting implantation on testing data with AUCs of 0.64 for ANN1 (conventional morphokinetics), 0.73 for ANN2 (novel morphodynamics), 0.77

2020 Fertility and Sterility

44. Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF. Full Text available with Trip Pro

Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF. Can an artificial intelligence (AI)-based model predict human embryo viability using images captured by optical light microscopy?We have combined computer vision image processing methods and deep learning techniques to create the non-invasive Life Whisperer AI model for robust prediction of embryo viability, as measured (...) a specificity of 60.5% for non-viable embryos across three independent blind test sets from different clinics. The weighted overall accuracy in each blind test set was >63%, with a combined accuracy of 64.3% across both viable and non-viable embryos, demonstrating model robustness and generalizability beyond the result expected from chance. Distributions of predictions showed clear separation of correctly and incorrectly classified embryos. Binary comparison of viable/non-viable embryo classification

2020 Human Reproduction

45. Intelligent difficulty scoring and assistance system for endoscopic extraction of common bile duct stones based on deep learning: multicentre study. (Abstract)

Intelligent difficulty scoring and assistance system for endoscopic extraction of common bile duct stones based on deep learning: multicentre study. The study aimed to construct an intelligent difficulty scoring and assistance system (DSAS) for endoscopic retrograde cholangiopancreatography (ERCP) treatment of common bile duct (CBD) stones.From three hospitals, 1954 cholangiograms were collected for training and testing the DSAS. The D-LinkNet34 and U-Net were adopted to segment the CBD, stones (...) clearance rates and more frequent EPLBD.In this study, an intelligent DSAS based on deep learning was developed.The DSAS can automatically remind endoscopists of technical difficulties for CBD stone extraction, help them optimise therapeutic approaches and choose proper accessories during the process of ERCP.Thieme. All rights reserved.

2020 Endoscopy

46. Stanford Accelerated Intelligent Neuromodulation Therapy for Treatment-Resistant Depression. (Abstract)

Stanford Accelerated Intelligent Neuromodulation Therapy for Treatment-Resistant Depression. New antidepressant treatments are needed that are effective, rapid acting, safe, and tolerable. Intermittent theta-burst stimulation (iTBS) is a noninvasive brain stimulation treatment that has been approved by the U.S. Food and Drug Administration for treatment-resistant depression. Recent methodological advances suggest that the current iTBS protocol might be improved through 1) treating patients (...) with multiple sessions per day at optimally spaced intervals, 2) applying a higher overall pulse dose of stimulation, and 3) precision targeting of the left dorsolateral prefrontal cortex (DLPFC) to subgenual anterior cingulate cortex (sgACC) circuit. The authors examined the feasibility, tolerability, and preliminary efficacy of Stanford Accelerated Intelligent Neuromodulation Therapy (SAINT), an accelerated, high-dose resting-state functional connectivity MRI (fcMRI)-guided iTBS protocol for treatment

2020 American Journal of Psychiatry

47. Effects of Age at Auditory Brainstem Implantation: Impact on Auditory Perception, Language Development, Speech Intelligibility. (Abstract)

Auditory Perception Test Battery. We used a closed-set pattern perception subtest, a closed-set word identification subtest, and an open-set sentence recognition subtest. Language performance was assessed with the Test of Early Language Development and Speech Intelligibility Rating, which was administered in a quiet room.In this study, the results demonstrated that the Early Group's auditory perception performance was better than the Late Group after 5 years of ABI use, when children had no additional (...) Effects of Age at Auditory Brainstem Implantation: Impact on Auditory Perception, Language Development, Speech Intelligibility. To study the effect of age at auditory brainstem implant (ABI) surgery on auditory perception, language, and speech intelligibility.Retrospective single cohort design.Tertiary referral center.In this study, 30 pediatric ABI users with no significant developmental issues were included. Participants were divided into two groups, according to age at surgery (Early Group

2020 Otology and Neurotology

48. The effect of synchronized linked band selection on speech intelligibility of bilateral cochlear implant users. Full Text available with Trip Pro

linked band selection, and ideal binary masks (IdBMs) have on the ability of 10 BiCIs to understand speech in background noise. The performance was assessed through a sentence-based speech intelligibility test, in a scenario where the speech signal was presented from the front and the interfering noise from one side. The linked band selection relies on the most favorable signal-to-noise-ratio (SNR) ear, which will select the bands to be stimulated for both CIs. Speech perception results show (...) The effect of synchronized linked band selection on speech intelligibility of bilateral cochlear implant users. Normal-hearing (NH) listeners have the ability to combine the audio input perceived by each ear to extract target information in challenging listening scenarios. Bilateral cochlear implant (BiCI) users, however, do not benefit as much as NH listeners do from a bilateral input. In this study, we investigate the effect that bilaterally synchronized electrical stimulation, bilaterally

2020 Hearing Research

49. The Use of Artificial Intelligence to Program Cochlear Implants. (Abstract)

The Use of Artificial Intelligence to Program Cochlear Implants. Cochlear implant (CI) technology and techniques have advanced over the years. There has not been the same degree of change in programming and there remains a lack of standardization techniques. The purpose of this study is to compare performance in cochlear implant subjects using experienced clinician (EC) standard programming methods versus an Artificial Intelligence, FOX based algorithm for programming.Prospective, nonrandomized (...) , multicenter study using within-subject experimental design SETTING:: Tertiary referral centers.Fifty-five adult patients with ≥ 3 months experience with a Nucleus 5, 6, Kanso, or 7 series sound processor.Therapeutic Main Outcome Measures: CNC words and AzBio sentences in noise (+10 dB SNR) tests were administered in a soundproof booth followed by a direct connect psychoacoustic battery using the EC program. Tests were repeated 1 month later using the optimized FOX program. Subjective measures of patient

2020 Otology and Neurotology

50. An artificial intelligence algorithm that identifies middle turbinate pneumatisation (concha bullosa) on sinus computed tomography scans. (Abstract)

An artificial intelligence algorithm that identifies middle turbinate pneumatisation (concha bullosa) on sinus computed tomography scans. Convolutional neural networks are a subclass of deep learning or artificial intelligence that are predominantly used for image analysis and classification. This proof-of-concept study attempts to train a convolutional neural network algorithm that can reliably determine if the middle turbinate is pneumatised (concha bullosa) on coronal sinus computed (...) diagnostic accuracy of 81 per cent (95 per cent confidence interval: 73.0-89.0 per cent) with an area under the curve of 0.93.A trained convolutional neural network algorithm appears to successfully identify pneumatisation of the middle turbinate with high accuracy. Further studies can be pursued to test its ability in other clinically important anatomical variants in otolaryngology and rhinology.

2020 Journal of Laryngology & Otology

51. An artificial intelligence algorithm that differentiates anterior ethmoidal artery location on sinus computed tomography scans. (Abstract)

An artificial intelligence algorithm that differentiates anterior ethmoidal artery location on sinus computed tomography scans. Deep learning using convolutional neural networks represents a form of artificial intelligence where computers recognise patterns and make predictions based upon provided datasets. This study aimed to determine if a convolutional neural network could be trained to differentiate the location of the anterior ethmoidal artery as either adhered to the skull base or within (...) the convolutional neural network. A further 197 unique images were used to test the algorithm; this yielded a total accuracy of 82.7 per cent (95 per cent confidence interval = 77.7-87.8), kappa statistic of 0.62 and area under the curve of 0.86.Convolutional neural networks demonstrate promise in identifying clinically important structures in functional endoscopic sinus surgery, such as anterior ethmoidal artery location on pre-operative sinus computed tomography.

2020 Journal of Laryngology & Otology

52. Development of a system based on artificial intelligence to identify visual problems in children: study protocol of the TrackAI project. Full Text available with Trip Pro

, lack of accurate screening tools and poor collaboration from young children.Some of these limitations can be overcome by new digital tools. Implementing a system based on artificial intelligence systems avoid the challenge of interpreting visual outcomes.The objective of the TrackAI Project is to develop a system to identify children with visual disorders. The system will have two main components: a novel visual test implemented in a digital device, DIVE (Device for an Integral Visual Examination (...) Development of a system based on artificial intelligence to identify visual problems in children: study protocol of the TrackAI project. Around 70% to 80% of the 19 million visually disabled children in the world are due to a preventable or curable disease, if detected early enough. Vision screening in childhood is an evidence-based and cost-effective way to detect visual disorders. However, current screening programmes face several limitations: training required to perform them efficiently

2020 BMJ open

53. Diagnosis of COVID-19 Pneumonia Using Chest Radiography: Value of Artificial Intelligence. Full Text available with Trip Pro

Diagnosis of COVID-19 Pneumonia Using Chest Radiography: Value of Artificial Intelligence. Background Radiologists are proficient in differentiating between chest x-ray radiographs (CXRs) with and without symptoms of pneumonia, but have found it more challenging to differentiate CXRs with COVID-19 pneumonia symptoms from those without. Purpose To develop an artificial intelligence algorithm to differentiate COVID-19 pneumonia from other causes of CXR abnormalities. Materials and Methods (...) In this retrospective study, a deep neural network, CV19-Net, was trained, validated, and tested on CXRs from patients with and without COVID-19 pneumonia. For the COVID-19 positive CXRs, patients with reverse transcriptase polymerase chain reaction positive results for severe acute respiratory syndrome coronavirus 2 with positive pneumonia findings between February 1, 2020 and May 30, 2020 were included. For the non-COVID-19 CXRs, patients with pneumonia who underwent CXR between October 1, 2019 and December 31

2020 Radiology

54. Automated Assessment of CO-RADS and Chest CT Severity Scores in Patients with Suspected COVID-19 Using Artificial Intelligence. Full Text available with Trip Pro

Automated Assessment of CO-RADS and Chest CT Severity Scores in Patients with Suspected COVID-19 Using Artificial Intelligence. Background The COVID-19 pandemic has spread across the globe with alarming speed, morbidity and mortality. Immediate triage of suspected patients with chest infections caused by COVID-19 using chest CT may be of assistance when results from definitive viral testing are delayed. Purpose To develop and validate an artificial intelligence (AI) system to score (...) medical centers. The system was trained, validated, and tested with data from one of the centers. Data from the second center served as an external test set. Diagnostic performance and agreement with scores assigned by eight independent observers were measured using receiver operating characteristic (ROC) analysis, linearly-weighted kappa and classification accuracy. Results 105 patients (62 ± 16 years, 61 men) and 262 patients (64 ± 16 years, 154 men) were evaluated in the internal and the external

2020 Radiology

55. Artificial Intelligence Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Origin at Chest CT. Full Text available with Trip Pro

the receiver operating characteristic curve of 0.90 and area under the precision-recall curve of 0.87. Assisted by the probabilities of the model, the radiologists achieved a higher average test accuracy (90% vs 85%, Δ = 5, P < .001), sensitivity (88% vs 79%, Δ = 9, P < .001), and specificity (91% vs 88%, Δ = 3, P = .001). Conclusion Artificial intelligence assistance improved radiologists' performance in distinguishing coronavirus disease 2019 pneumonia from non-coronavirus disease 2019 pneumonia at chest (...) Artificial Intelligence Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Origin at Chest CT. Background Coronavirus disease 2019 (COVID-19) and pneumonia of other diseases share similar CT characteristics, which contributes to the challenges in differentiating them with high accuracy. Purpose To establish and evaluate an artificial intelligence (AI) system for differentiating COVID-19 and other pneumonia at chest CT and assessing radiologist performance

2020 Radiology

56. COVID-19 on Chest Radiographs: A Multireader Evaluation of an Artificial Intelligence System. Full Text available with Trip Pro

COVID-19 on Chest Radiographs: A Multireader Evaluation of an Artificial Intelligence System. Background Chest radiography may play an important role in triage for coronavirus disease 2019 (COVID-19), particularly in low-resource settings. Purpose To evaluate the performance of an artificial intelligence (AI) system for detection of COVID-19 pneumonia on chest radiographs. Materials and Methods An AI system (CAD4COVID-XRay) was trained on 24 678 chest radiographs, including 1540 used only (...) for validation while training. The test set consisted of a set of continuously acquired chest radiographs (n = 454) obtained in patients suspected of having COVID-19 pneumonia between March 4 and April 6, 2020, at one center (223 patients with positive reverse transcription polymerase chain reaction [RT-PCR] results, 231 with negative RT-PCR results). Radiographs were independently analyzed by six readers and by the AI system. Diagnostic performance was analyzed with the receiver operating characteristic

2020 Radiology

57. Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy. Full Text available with Trip Pro

Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy. Background Coronavirus disease 2019 (COVID-19) has widely spread all over the world since the beginning of 2020. It is desirable to develop automatic and accurate detection of COVID-19 using chest CT. Purpose To develop a fully automatic framework to detect COVID-19 using chest CT and evaluate its performance. Materials and Methods In this retrospective (...) and multicenter study, a deep learning model, the COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT scans for the detection of COVID-19. CT scans of community-acquired pneumonia (CAP) and other non-pneumonia abnormalities were included to test the robustness of the model. The datasets were collected from six hospitals between August 2016 and February 2020. Diagnostic performance was assessed with the area under the receiver operating characteristic

2020 Radiology

58. Artificial Intelligence System Approaching Neuroradiologist-level Differential Diagnosis Accuracy at Brain MRI. (Abstract)

Artificial Intelligence System Approaching Neuroradiologist-level Differential Diagnosis Accuracy at Brain MRI. Background Although artificial intelligence (AI) shows promise across many aspects of radiology, the use of AI to create differential diagnoses for rare and common diseases at brain MRI has not been demonstrated. Purpose To evaluate an AI system for generation of differential diagnoses at brain MRI compared with radiologists. Materials and Methods This retrospective study tested (...) features by using Bayesian inference to develop probability-ranked differential diagnoses. Quantitative feature extraction algorithms and conditional probabilities were fine-tuned on a training set of 86 patients (mean age, 49 years ± 16 [standard deviation]; 53 women). Accuracy was compared with radiology residents, general radiologists, neuroradiology fellows, and academic neuroradiologists by using accuracy of top one, top two, and top three differential diagnoses in 92 independent test set patients

2020 Radiology

59. Artificial intelligence using convolutional neural networks for real-time detection of early esophageal neoplasia in Barrett's esophagus (with video). (Abstract)

Artificial intelligence using convolutional neural networks for real-time detection of early esophageal neoplasia in Barrett's esophagus (with video). The visual detection of early esophageal neoplasia (high-grade dysplasia and T1 cancer) in Barrett's esophagus (BE) with white-light and virtual chromoendoscopy still remains challenging. The aim of this study was to assess whether a convolutional neural artificial intelligence network can aid in the recognition of early esophageal neoplasia (...) ." We developed an object detection algorithm that drew localization boxes around regions classified as dysplasia.The CNN analyzed 458 test images (225 dysplasia and 233 nondysplasia) and correctly detected early neoplasia with sensitivity of 96.4%, specificity of 94.2%, and accuracy of 95.4%. With regard to the object detection algorithm for all images in the validation set, the system was able to achieve a mean average precision of .7533 at an intersection over union of .3 CONCLUSIONS

2020 Gastrointestinal endoscopy

60. Development and validation of an explainable artificial intelligence-based decision-supporting tool for prostate biopsy. (Abstract)

Development and validation of an explainable artificial intelligence-based decision-supporting tool for prostate biopsy. To develop and validate a risk calculator for prostate cancer (PCa) and clinically significant PCa (csPCa) using explainable artificial intelligence (XAI).We used data of 3791 patients to develop and validate the risk calculator. We initially divided the data into development and validation sets. An extreme gradient-boosting algorithm was applied to the development calculator (...) as a test set. We selected the variables for each PCa and csPCa risk calculation according to the least absolute shrinkage and selection operator regression. The AUC of the final PCa model was 0.869 (95% confidence interval [CI] 0.844-0.893), whereas that of the csPCa model was 0.945 (95% CI 0.927-0.963). The prostate-specific antigen (PSA) level, free PSA level, age, prostate volume (both the transitional zone and total), hypoechoic lesions on ultrasonography, and testosterone level were found

2020 BJU international

To help you find the content you need quickly, you can filter your results via the categories on the right-hand side >>>>