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61. Artificial intelligence accuracy in detecting pathological breath sounds in children using digital stethoscopes. Full Text available with Trip Pro

Artificial intelligence accuracy in detecting pathological breath sounds in children using digital stethoscopes. Manual auscultation to detect abnormal breath sounds has poor inter-observer reliability. Digital stethoscopes with artificial intelligence (AI) could improve reliable detection of these sounds. We aimed to independently test the abilities of AI developed for this purpose.One hundred and ninety two auscultation recordings collected from children using two different digital

2020 Respiratory research

62. Artificial intelligence for the detection of esophageal and esophagogastric junctional adenocarcinoma. (Abstract)

Artificial intelligence for the detection of esophageal and esophagogastric junctional adenocarcinoma. Conventional endoscopy for the early detection of esophageal and esophagogastric junctional adenocarcinoma (E/J cancer) is limited because early lesions are asymptomatic, and the associated changes in the mucosa are subtle. There are no reports on artificial intelligence (AI) diagnosis for E/J cancer from Asian countries. Therefore, we aimed to develop a computerized image analysis system (...) using deep learning for the detection of E/J cancers.A total of 1172 images from 166 pathologically proven superficial E/J cancer cases and 2271 images of normal mucosa in esophagogastric junctional from 219 cases were used as the training image data. A total of 232 images from 36 cancer cases and 43 non-cancerous cases were used as the validation test data. The same validation test data were diagnosed by 15 board-certified specialists (experts).The sensitivity, specificity, and accuracy of the AI

2020 Journal of gastroenterology and hepatology

63. Lung involvement in macrophage activation syndrome and severe COVID-19: results from a cross-sectional study to assess clinical, laboratory and artificial intelligence-radiological differences. Full Text available with Trip Pro

Lung involvement in macrophage activation syndrome and severe COVID-19: results from a cross-sectional study to assess clinical, laboratory and artificial intelligence-radiological differences. To evaluate the clinical pictures, laboratory tests and imaging of patients with lung involvement, either from severe COVID-19 or macrophage activation syndrome (MAS), in order to assess how similar these two diseases are.The present work has been designed as a cross-sectional single-centre study (...) to compare characteristics of patients with lung involvement either from MAS or severe COVID-19. Chest CT scans were assessed by using an artificial intelligence (AI)-based software.Ten patients with MAS and 47 patients with severe COVID-19 with lung involvement were assessed. Although all patients showed fever and dyspnoea, patients with MAS were characterised by thrombocytopaenia, whereas patients with severe COVID-19 were characterised by lymphopaenia and neutrophilia. Higher values of H-score

2020 Annals of the rheumatic diseases

64. Artificial intelligence-assisted cytology for detection of cervical intraepithelial neoplasia or invasive cancer: A multicenter, clinical-based, observational study. (Abstract)

Artificial intelligence-assisted cytology for detection of cervical intraepithelial neoplasia or invasive cancer: A multicenter, clinical-based, observational study. Artificial intelligence (AI) could automatedly detect abnormalities in digital cytological images, however, the effect in cervical cancer screening is inconclusive. We aim to evaluate the performance of AI-assisted cytology for the detection of histologically cervical intraepithelial lesions (CIN) or cancer.We trained a supervised (...) deep learning algorithm based on 188,542 digital cytological images. Between Mar 13, 2017, and Oct 20, 2018, 2145 referral women from organized screening were enrolled in a multicenter, clinical-based, observational study. Cervical specimen was sampled to generate two liquid-based slides: one random slide was allocated to AI-assisted reading, and the other to manual reading conducted by skilled cytologists from senior hospital and cytology doctors from primary hospitals. HPV testing and colposcopy

2020 Gynecologic Oncology

65. Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study. Full Text available with Trip Pro

Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study. An increasing volume of prostate biopsies and a worldwide shortage of urological pathologists puts a strain on pathology departments. Additionally, the high intra-observer and inter-observer variability in grading can result in overtreatment and undertreatment of prostate cancer. To alleviate these problems, we aimed to develop an artificial intelligence (AI) system (...) by predicting the presence, extent, and Gleason grade of malignant tissue for an independent test dataset comprising 1631 biopsies from 246 men from STHLM3 and an external validation dataset of 330 biopsies from 73 men. We also evaluated grading performance on 87 biopsies individually graded by 23 experienced urological pathologists from the International Society of Urological Pathology. We assessed discriminatory performance by receiver operating characteristics and tumour extent predictions by correlating

2020 Lancet Oncology

66. Formalizing video documentation of the Critical View of Safety in laparoscopic cholecystectomy: a step towards artificial intelligence assistance to improve surgical safety. (Abstract)

Formalizing video documentation of the Critical View of Safety in laparoscopic cholecystectomy: a step towards artificial intelligence assistance to improve surgical safety. In laparoscopic cholecystectomy (LC), achievement of the Critical View of Safety (CVS) is commonly advocated to prevent bile duct injuries (BDI). However, BDI rates remain stable, probably due to inconsistent application or a poor understanding of CVS as well as unreliable reporting. Objective video reporting could serve (...) for quality auditing and help generate consistent datasets for deep learning models aimed at intraoperative assistance. In this study, we develop and test a method to report CVS using videos.LC videos performed at our institution were retrieved and the video segments starting 60 s prior to the division of cystic structures were edited. Two independent reviewers assessed CVS using an adaptation of the doublet view 6-point scale and a novel binary method in which each criterion is considered either achieved

2020 Surgical endoscopy

67. Early detection of ST-segment elevated myocardial infarction by artificial intelligence with 12-lead electrocardiogram. (Abstract)

Early detection of ST-segment elevated myocardial infarction by artificial intelligence with 12-lead electrocardiogram. Patient delay is a worldwide unsolved problem in ST-segment elevated myocardial infarction (STEMI). An accurate warning system based on electrocardiogram (ECG) may be a solution for this problem, and artificial intelligence (AI) may offer a path to improve its accuracy and efficiency. In the present study, an AI-based STEMI autodiagnosis algorithm was developed using a dataset (...) of 667 STEMI ECGs and 7571 control ECGs. The algorithm for detecting STEMI proposed in the present study achieved an area under the receiver operating curve (AUC) of 0.9954 (95% CI, 0.9885 to 1) with sensitivity (recall), specificity, accuracy, precision and F1 scores of 96.75%, 99.20%, 99.01%, 90.86% and 0.9372 respectively, in the external evaluation. In a comparative test with cardiologists, the algorithm had an AUC of 0.9740 (95% CI, 0.9419 to 1), and its sensitivity (recall), specificity

2020 International journal of cardiology

68. Study on intelligent syndrome differentiation neural network model of stomachache in traditional Chinese medicine based on the real world. Full Text available with Trip Pro

on Matlab, respectively. Finally, the differentiation performance of the 2 models was tested and analyzed.The testing results showed that the improved third-order convergence LM algorithm model has better average prediction and diagnosis accuracy, especially in predicting "liver-stomach disharmony" and "stomach yang deficiency", is above 95%.By effectively using the self-learning and auto-update ability of the BP neural network, the intelligent syndrome differentiation model of TCM can fully approach (...) Study on intelligent syndrome differentiation neural network model of stomachache in traditional Chinese medicine based on the real world. Stomachache is not only disease name of Traditional Chinese medicine (TCM) but also the clinical symptom. It is a common and multiple diseases. TCM has its particular advantage in clinical treatment of stomachache. Syndrome differentiation is an important concept in TCM practice. The therapeutic process is virtually a nonlinear mapping process from clinical

2020 Medicine

69. Analysis of Collective Human Intelligence for Diagnosis of Pigmented Skin Lesions Harnessed by Gamification Via a Web-Based Training Platform: Simulation Reader Study. Full Text available with Trip Pro

images to 1 of the 7 disease categories. The readers could repeat the test with different lesions at their own discretion. Collective human intelligence was achieved by sampling answers from multiple readers. The disease category with most votes was regarded as the collective vote per image.We collected 111,019 single ratings, with a mean of 25.2 (SD 18.5) ratings per image. As single raters, nonexperts achieved a lower mean accuracy (58.6%) than experts (68.4%; mean difference=-9.4%; 95% CI -10.74 (...) Analysis of Collective Human Intelligence for Diagnosis of Pigmented Skin Lesions Harnessed by Gamification Via a Web-Based Training Platform: Simulation Reader Study. The diagnosis of pigmented skin lesion is error prone and requires domain-specific expertise, which is not readily available in many parts of the world. Collective intelligence could potentially decrease the error rates of nonexperts.The aim of this study was to evaluate the feasibility and impact of collective intelligence

2020 Journal of medical Internet research

70. An Innovative Artificial Intelligence-Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study. Full Text available with Trip Pro

An Innovative Artificial Intelligence-Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study. Gestational diabetes mellitus (GDM) can cause adverse consequences to both mothers and their newborns. However, pregnant women living in low- and middle-income areas or countries often fail to receive early clinical interventions at local medical facilities due to restricted availability of GDM diagnosis. The outstanding performance of artificial intelligence (AI (...) ) in disease diagnosis in previous studies demonstrates its promising applications in GDM diagnosis.This study aims to investigate the implementation of a well-performing AI algorithm in GDM diagnosis in a setting, which requires fewer medical equipment and staff and to establish an app based on the AI algorithm. This study also explores possible progress if our app is widely used.An AI model that included 9 algorithms was trained on 12,304 pregnant outpatients with their consent who received a test

2020 Journal of medical Internet research

71. The development an artificial intelligence algorithm for early sepsis diagnosis in the intensive care unit. Full Text available with Trip Pro

The development an artificial intelligence algorithm for early sepsis diagnosis in the intensive care unit. Severe sepsis and septic shock are still the leading causes of death in Intensive Care Units (ICUs), and timely diagnosis is crucial for treatment outcomes. The progression of electronic medical records (EMR) offers the possibility of storing a large quantity of clinical data that can facilitate the development of artificial intelligence (AI) in medicine. However, several difficulties (...) was 13.8. The SEPSIS cohort had a higher SOFA score and increased use of organ support treatment. The AI algorithm was developed with a shuffle method using 80% of the instances for training and 20% for testing. The established AI algorithm achieved the following: accuracy = 82% ± 1%; sensitivity = 65% ± 5%; specificity = 88% ± 2%; precision = 67% ± 3%; and F1 = 0.66 ± 0.02. The area under the receiver operating characteristic curve (AUROC) was approximately 0.89. The SOFA score was used on the same

2020 International journal of medical informatics

72. The application of artificial intelligence (AI) techniques to identify frailty within a residential aged care administrative data set. (Abstract)

The application of artificial intelligence (AI) techniques to identify frailty within a residential aged care administrative data set. Research has shown that frailty, a geriatric syndrome associated with an increased risk of negative outcomes for older people, is highly prevalent among residents of residential aged care facilities (also called long term care facilities or nursing homes). However, progress on effective identification of frailty within residential care remains at an early stage (...) , necessitating the development of new methods for accurate and efficient screening.We aimed to determine the effectiveness of artificial intelligence (AI) algorithms in accurately identifying frailty among residents aged 75 years and over in comparison with a calculated electronic Frailty Index (eFI) based on a routinely-collected residential aged care administrative data set drawn from 10 residential care facilities located in Queensland, Australia. A secondary objective included the identification of best

2020 International journal of medical informatics

73. Prognostic Modeling of COVID-19 Using Artificial Intelligence in the United Kingdom: Model Development and Validation. Full Text available with Trip Pro

Prognostic Modeling of COVID-19 Using Artificial Intelligence in the United Kingdom: Model Development and Validation. The current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak is a public health emergency and the case fatality rate in the United Kingdom is significant. Although there appear to be several early predictors of outcome, there are no currently validated prognostic models or scoring systems applicable specifically to patients with confirmed SARS-CoV-2.We aim (...) and validated on data extracted from 398 patients admitted to hospital with a positive real-time reverse transcription polymerase chain reaction (RT-PCR) test for SARS-CoV-2.Patient-specific mortality was predicted with 86.25% accuracy, with a sensitivity of 87.50% (95% CI 61.65%-98.45%) and specificity of 85.94% (95% CI 74.98%-93.36%). The positive predictive value was 60.87% (95% CI 45.23%-74.56%), and the negative predictive value was 96.49% (95% CI 88.23%-99.02%). The area under the receiver operating

2020 Journal of medical Internet research

74. Position statement on priorities for artificial intelligence in GI endoscopy: a report by the ASGE Task Force. Full Text available with Trip Pro

Position statement on priorities for artificial intelligence in GI endoscopy: a report by the ASGE Task Force. Artificial intelligence (AI) in GI endoscopy holds tremendous promise to augment clinical performance, establish better treatment plans, and improve patient outcomes. Although there are promising initial applications and preliminary clinical data for AI in gastroenterology, the field is still in a very early phase, with limited clinical use. The American Society for Gastrointestinal (...) Endoscopy has convened an AI Task Force to develop guidance around clinical implementation, testing/validating algorithms, and building pathways for successful implementation of AI in GI endoscopy. This White Paper focuses on 3 areas: (1) priority use cases for development of AI algorithms in GI, both for specific clinical scenarios and for streamlining clinical workflows, quality reporting, and practice management; (2) data science priorities, including development of image libraries

2020 Gastrointestinal endoscopy

75. Highly accurate artificial intelligence systems to predict the invasion depth of gastric cancer: efficacy of conventional white-light imaging, nonmagnifying narrow-band imaging, and indigo-carmine dye contrast imaging. (Abstract)

of this study was to test the efficacy of novel artificial intelligence (AI) systems in predicting the invasion depth of GC.A total of 16,557 images from 1084 cases of GC for which endoscopic resection or surgery was performed between January 2013 and June 2019 were extracted. Cases were randomly assigned to training and test datasets at a ratio of 4:1. Through transfer learning leveraging a convolutional neural network architecture, ResNet50, 3 independent AI systems were developed. Each system was trained (...) Highly accurate artificial intelligence systems to predict the invasion depth of gastric cancer: efficacy of conventional white-light imaging, nonmagnifying narrow-band imaging, and indigo-carmine dye contrast imaging. Diagnosing the invasion depth of gastric cancer (GC) is necessary to determine the optimal method of treatment. Although the efficacy of evaluating macroscopic features and EUS has been reported, there is a need for more accurate and objective methods. The primary aim

2020 Gastrointestinal endoscopy

76. New insights on missed colonic lesions during colonoscopy through artificial intelligence-assisted real-time detection (with video). Full Text available with Trip Pro

New insights on missed colonic lesions during colonoscopy through artificial intelligence-assisted real-time detection (with video). Meta-analysis shows that up to 26% of adenomas could be missed during colonoscopy. We investigated whether the use of artificial intelligence (AI)-assisted real-time detection could provide new insights into mechanisms underlying missed lesions during colonoscopy.A validated real-time deep-learning AI model for the detection of colonic polyps was first tested

2020 Gastrointestinal endoscopy

77. Use of artificial intelligence for detection of gastric lesions by magnetically controlled capsule endoscopy. Full Text available with Trip Pro

Use of artificial intelligence for detection of gastric lesions by magnetically controlled capsule endoscopy. Magnetically controlled capsule endoscopy (MCE) has become an efficient diagnostic modality for gastric diseases. We developed a novel automatic gastric lesion detection system to assist in diagnosis and reduce inter-physician variations. This study aimed to evaluate the diagnostic capability of the computer-aided detection system for MCE images.We developed a novel automatic gastric (...) lesion detection system based on a convolutional neural network (CNN) and faster region-based convolutional neural network (RCNN). A total of 1,023,955 MCE images from 797 patients were used to train and test the system. These images were divided into 7 categories (erosion, polyp, ulcer, submucosal tumor, xanthoma, normal mucosa, and invalid images). The primary endpoint was the sensitivity of the system.The system detected gastric focal lesions with 96.2% sensitivity (95% confidence interval (CI

2020 Gastrointestinal endoscopy

78. 3D Printing, Computational Modeling, and Artificial Intelligence for Structural Heart Disease. (Abstract)

in clinical care and procedural planning has demonstrated a reduction in early-operator learning curve for transcatheter interventions. Integration of computation modeling to 3D printing has accelerated research and development understanding of fluid mechanics within device testing. Application of 3D printing, computational modeling, and ultimately incorporation of artificial intelligence is changing the landscape of physician training and delivery of patient-centric care. Transcatheter structural heart (...) 3D Printing, Computational Modeling, and Artificial Intelligence for Structural Heart Disease. Structural heart disease (SHD) is a new field within cardiovascular medicine. Traditional imaging modalities fall short in supporting the needs of SHD interventions, as they have been constructed around the concept of disease diagnosis. SHD interventions disrupt traditional concepts of imaging in requiring imaging to plan, simulate, and predict intraprocedural outcomes. In transcatheter SHD

2020 JACC. Cardiovascular imaging

79. Assessing and Mitigating Bias in Medical Artificial Intelligence: The Effects of Race and Ethnicity on a Deep Learning Model for ECG Analysis. (Abstract)

Assessing and Mitigating Bias in Medical Artificial Intelligence: The Effects of Race and Ethnicity on a Deep Learning Model for ECG Analysis. Deep learning algorithms derived in homogeneous populations may be poorly generalizable and have the potential to reflect, perpetuate, and even exacerbate racial/ethnic disparities in health and health care. In this study, we aimed to (1) assess whether the performance of a deep learning algorithm designed to detect low left ventricular ejection fraction (...) using the 12-lead ECG varies by race/ethnicity and to (2) determine whether its performance is determined by the derivation population or by racial variation in the ECG.We performed a retrospective cohort analysis that included 97 829 patients with paired ECGs and echocardiograms. We tested the model performance by race/ethnicity for convolutional neural network designed to identify patients with a left ventricular ejection fraction ≤35% from the 12-lead ECG.The convolutional neural network

2020 Circulation. Arrhythmia and electrophysiology

80. Artificial Intelligence-Enabled ECG Algorithm to Identify Patients With Left Ventricular Systolic Dysfunction Presenting to the Emergency Department With Dyspnea. (Abstract)

Artificial Intelligence-Enabled ECG Algorithm to Identify Patients With Left Ventricular Systolic Dysfunction Presenting to the Emergency Department With Dyspnea. Identification of systolic heart failure among patients presenting to the emergency department (ED) with acute dyspnea is challenging. The reasons for dyspnea are often multifactorial. A focused physical evaluation and diagnostic testing can lack sensitivity and specificity. The objective of this study was to assess the accuracy (...) (95% CI, 0.76-0.84).The ECG is an inexpensive, ubiquitous, painless test which can be quickly obtained in the ED. It effectively identifies LVSD in selected patients presenting to the ED with dyspnea when analyzed with artificial intelligence and outperforms NT-proBNP. Graphic Abstract: A graphic abstract is available for this article.

2020 Circulation. Arrhythmia and electrophysiology

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