Evidence
AI software leverages information in DBT to help identify cancers with limited or no visibility on FFDM
Brittaney Everitt, MS
An AI-driven peer review quality-assurance process to improve cancer detection: Real world experience in a community practice
Mireille Aujero, MD
Categorical artificial intelligence helps general radiologists and breast imaging specialists improve cancer detection performance on DBT mammograms for women of all races
Jiye G. Kim, PhD
Radiologists using categorical AI for screening mammography improve more than double reading alone
Hyunkwang Lee, PhD
Real world evidence supports robustness of AI categories for screening mammography
Bryan Haslam, PhD
Artificial Intelligence for Neuroimaging and Musculoskeletal Radiology: Overview of Current Commercial Algorithms
Elisa R. Berson, Mariam S. Aboian, Ajay Malhotra, Seyedmehdi Payabvash
Combining semi‑quantitative rating and automated brain volumetry in MRI evaluation of patients with probable behavioural variant of frontotemporal dementia: an added value for clinical practise?
Sonia Francesca Calloni, Paolo Quintiliano Vezzulli, Antonella Castellano, Riccardo Leone, Silvia Basaia, Almar von Loon, Edoardo Gioele Spinelli, Giuseppe Magnani, Francesca Caso, Federica Agosta, Massimo Filippi & Andrea Falini
Bridging the experience gap in prostate multiparametric magnetic resonance imaging using artificial intelligence: A prospective multi-reader comparison study on inter-reader agreement in PI-RADS v2.1, image quality and reporting time between novice and expert readers.
Ali Forookhi, Ludovica Laschena, Martina Pecoraro, Antonella Borrelli, Michele Massaro, Ailin Dehghanpour, Stefano Cipollari, Carlo Catalano, Valeria Panebianco
"I’m doing twice as much CT reporting": The impact of the lung solution at Salisbury NHS
Twenty-four hour blood pressure variability and the prevalence and the progression of cerebral white matter hyperintensities
Naomi LP Starmans et al.
Higher Agreement Between Readers with Deep Learning CAD Software for Reporting Pulmonary Nodules on CT
European Journal of Radiology Open
Hempel et al. Aug 2022
This retrospective study evaluated the impact of CAD software on the pulmonary nodule management recommendations of radiologists in a cohort of patients with incidentally detected nodules on CT. Two radiologists independently assessed 50 chest CT cases twice, unaided and aided by CAD. Readers showed a better agreement with the aid of CAD with and a significant reduction of reading time, suggesting that a dedicated CAD system for aiding in pulmonary nodule reporting may help improve the uniformity of management recommendations.
Quantib® Prostate Compared to an Expert Radiologist for the Diagnosis of Prostate Cancer on mpMRI: A Single-Center Preliminary Study
Tomography
Faiella et al. Aug 2022
This study evaluated the clinical utility of an Artificial Intelligence (AI) radiology solution for prostate cancer (PCa) lesions detection on multiparametric Magnetic Resonance Images (mpMRI). Prostate mpMRI exams of 108 patients were retrospectively studied. The diagnostic performance of an expert radiologist and of an inexperienced radiologist aided by AI were compared. The results demonstrated that the AI software is a very sensitive tool to use, specifically in high-risk patients (those with high PIRADS and high Gleason score).
Validation of a Deep Learning Computer Aided System for CT Based Lung Nodule Detection, Classification, and Growth Rate Estimation in a Routine Clinical Population
PLoS One
Murchison et al. May 2022
The deep learning algorithm of a commercially available computer assisted diagnosis system (CAD) was trained with a lung cancer screening cohort and developed for detection, classification, quantification, and growth of actionable pulmonary nodules on chest CT scans. In a retrospective cohort of a routine clinical population, the applied CAD significantly increased radiologist’s detection of actionable nodules while minimally increasing the false positive rate, suggesting it can assist in pulmonary nodule detection and management.
Robust Breast Cancer Detection in Mammography and Digital Breast Tomosynthesis Using an Annotation-Efficient Deep Learning Approach
Nature Medicine
Lotter et al. Jan 2021
DeepHealth’s deep-learning algorithm achieves state of the art performance in mammogram classification. The AI model was compared to 5 readers in a reader study of 131 index cancers and 154 confirmed negatives. The model showed robust and generalizable performance, reporting an Area Under the Curve (AUC) of 0.945 and outperforming all radiologists with a sensitivity 14% higher than the average radiologist sensitivity.
Validation, Comparison, and Combination of Algorithms for Automatic Detection of Pulmonary Nodules in Computed Tomography Images: The LUNA16 Challenge
Medical Image Analysis
Setio et al. Dec 2017
The LUNA16 challenge is an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans. Participants developed algorithms focused on 1) the complete nodule detection track or 2) the false positive reduction track. The impact of combined systems was also investigated. The combination of these solutions achieved a sensitivity of over 95% at fewer than 1.0 false positives per scan, highlighting the potential to improve detection performance.