Publications by authors named "Bruno Astuto"
Objective: To demonstrate and test the capabilities of the American College of Radiology (ACR) Connect and AI-LAB software platform by implementing multi-institutional artificial intelligence (AI) training and validation for breast density classification.
Methods: In this proof-of-concept study, six U.S.
Objective: Our study aimed to investigate the association between time to incidence of radiographic osteoarthritis (OA) and magnetic resonance imaging (MRI)-based structural phenotypes proposed by the Rapid Osteoarthritis MRI Eligibility Score (ROAMES).
Methods: A retrospective cohort of 2,328 participants without radiographic OA at baseline were selected from the Osteoarthritis Initiative study. Utilizing a deep-learning model, we automatically assessed the presence of inflammatory, meniscus/cartilage, subchondral bone, and hypertrophic phenotypes from MRIs acquired at baseline and 12-, 24-, 36-, 48-, 72-, and 96-month follow-up visits.
Purpose: To test the hypothesis that artificial intelligence (AI) techniques can aid in identifying and assessing lesion severity in the cartilage, bone marrow, meniscus, and anterior cruciate ligament (ACL) in the knee, improving overall MRI interreader agreement.
Materials And Methods: This retrospective study was conducted on 1435 knee MRI studies ( = 294 patients; mean age, 43 years ± 15 [standard deviation]; 153 women) collected within three previous studies (from 2011 to 2014). All MRI studies were acquired using high-spatial-resolution three-dimensional fast-spin-echo CUBE sequence.
Article Synopsis
- * This study used convolutional neural networks to analyze knee MRIs and classify them into different phenotypes, providing insights into their relationship with the development of OA and the need for knee replacements over time.
- * Findings indicate that specific phenotypes, particularly bone and hypertrophy, significantly increase the chances of developing structural OA and symptomatic OA, suggesting that AI can improve patient stratification for targeted therapeutic trials.
Purpose: To evaluate the diagnostic utility of two convolutional neural networks (CNNs) for severity staging of anterior cruciate ligament (ACL) injuries.
Materials And Methods: In this retrospective study, 1243 knee MR images (1008 intact, 18 partially torn, 77 fully torn, and 140 reconstructed ACLs) from 224 patients (mean age, 47 years ± 14 [standard deviation]; 54% women) were analyzed. The MRI examinations were performed between 2011 and 2014.