AIMC Topic: Observer Variation

Clear Filters Showing 191 to 200 of 326 articles

The impact of iterative reconstruction algorithms on machine learning-based coronary CT angiography-derived fractional flow reserve (CT-FFR) values.

The international journal of cardiovascular imaging Mar 4, 2020
To evaluate the impact of an iterative reconstruction (IR) algorithm (advanced modeled iterative reconstruction, ADMIRE) on machine learning-based coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) measurements compared...
Fractional Flow Reserve, Myocardial Male Plaque, Atherosclerotic Middle Aged Severity of Illness Index Vascular Calcification Predictive Value of Tests Computed Tomography Angiography Machine Learning Coronary Stenosis Coronary Angiography Radiographic Image Interpretation, Computer-Assisted Retrospective Studies Coronary Artery Disease Female Humans Observer Variation Reproducibility of Results Coronary Vessels Aged
View on PubMed DOI

Comparing machine and human reviewers to evaluate the risk of bias in randomized controlled trials.

Research synthesis methods Mar 3, 2020
BACKGROUND: Evidence from new health technologies is growing, along with demands for evidence to inform policy decisions, creating challenges in completing health technology assessments (HTAs)/systematic reviews (SRs) in a timely manner. Software can...
Humans Machine Learning Software Reproducibility of Results Bias Outcome Assessment, Health Care Observer Variation Pattern Recognition, Automated Sensitivity and Specificity Risk Risk Assessment Health Policy Randomized Controlled Trials as Topic
View on PubMed DOI

Improved Accuracy in Optical Diagnosis of Colorectal Polyps Using Convolutional Neural Networks with Visual Explanations.

Gastroenterology Feb 29, 2020
BACKGROUND & AIMS: Narrow-band imaging (NBI) can be used to determine whether colorectal polyps are adenomatous or hyperplastic. We investigated whether an artificial intelligence (AI) system can increase the accuracy of characterizations of polyps b...
Humans Reproducibility of Results Colorectal Neoplasms Colonoscopy Diagnosis, Computer-Assisted Colonic Polyps Image Interpretation, Computer-Assisted Observer Variation Hyperplasia Visual Perception Deep Learning Clinical Competence Predictive Value of Tests Workflow Seoul Adenomatous Polyps Narrow Band Imaging
View on PubMed DOI

Automated segmentation of the left ventricle from MR cine imaging based on deep learning architecture.

Biomedical physics & engineering express Feb 18, 2020
BACKGROUND: Magnetic resonance cine imaging is the accepted standard for cardiac functional assessment. Left ventricular (LV) segmentation plays a key role in volumetric functional quantification of the heart. Conventional manual analysis is time-con...
Magnetic Resonance Imaging, Cine Heart Ventricles Heart Diseases Observer Variation Automation Algorithms Ventricular Function, Left Deep Learning Image Processing, Computer-Assisted Humans Neural Networks, Computer
View on PubMed DOI

Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study.

The Lancet. Digital health Feb 6, 2020
BACKGROUND: Mammography is the current standard for breast cancer screening. This study aimed to develop an artificial intelligence (AI) algorithm for diagnosis of breast cancer in mammography, and explore whether it could benefit radiologists by imp...
Mammography Radiology Observer Variation False Positive Reactions Humans Artificial Intelligence Retrospective Studies Female Adult Breast Neoplasms Middle Aged Early Detection of Cancer
View on PubMed DOI

Observer-independent assessment of psoriasis-affected area using machine learning.

Journal of the European Academy of Dermatology and Venereology : JEADV Jan 23, 2020
BACKGROUND: Assessment of psoriasis severity is strongly observer-dependent, and objective assessment tools are largely missing. The increasing number of patients receiving highly expensive therapies that are reimbursed only for moderate-to-severe ps...
Adult Middle Aged Retrospective Studies Severity of Illness Index Reproducibility of Results Adolescent Psoriasis Aged Aged, 80 and over Observer Variation Humans Young Adult Photography Neural Networks, Computer Machine Learning
View on PubMed DOI

Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study.

The lancet. Gastroenterology & hepatology Jan 22, 2020
BACKGROUND: Colonoscopy with computer-aided detection (CADe) has been shown in non-blinded trials to improve detection of colon polyps and adenomas by providing visual alarms during the procedure. We aimed to assess the effectiveness of a CADe system...
Female Double-Blind Method Adult China Colonic Polyps Colorectal Neoplasms Incidence Observer Variation Male Mass Screening Early Diagnosis Adenoma Middle Aged Clinical Alarms Humans Colonoscopy Case-Control Studies Diagnosis, Computer-Assisted Deep Learning
View on PubMed DOI

Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study.

The Lancet. Oncology Jan 8, 2020
BACKGROUND: The Gleason score is the strongest correlating predictor of recurrence for prostate cancer, but has substantial inter-observer variability, limiting its usefulness for individual patients. Specialised urological pathologists have greater ...
Neoplasm Grading Biopsy Image Interpretation, Computer-Assisted Observer Variation Male Diagnosis, Computer-Assisted Deep Learning Humans Predictive Value of Tests Prostatic Neoplasms Automation, Laboratory Retrospective Studies Reproducibility of Results
View on PubMed DOI

Segmentation of organs-at-risk in cervical cancer CT images with a convolutional neural network.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) Jan 6, 2020
PURPOSE: We introduced and evaluated an end-to-end organs-at-risk (OARs) segmentation model that can provide accurate and consistent OARs segmentation results in much less time.
Reproducibility of Results Adult Uterine Cervical Neoplasms Middle Aged Deep Learning Image Processing, Computer-Assisted Radiation Oncology Organs at Risk Radiotherapy Planning, Computer-Assisted Humans Neural Networks, Computer Tomography, X-Ray Computed Female Observer Variation
View on PubMed DOI

Assessment of individual tumor buds using keratin immunohistochemistry: moderate interobserver agreement suggests a role for machine learning.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc Dec 16, 2019
Tumor budding is a promising and cost-effective biomarker with strong prognostic value in colorectal cancer. However, challenges related to interobserver variability persist. Such variability may be reduced by immunohistochemistry and computer-aided ...
Observer Variation Humans Immunohistochemistry Machine Learning Colorectal Neoplasms Keratins Biomarkers, Tumor
View on PubMed DOI
Popular Topics
Recent Journals