The augmented radiologist: artificial intelligence in the practice of radiology

E Sorantin, MG Grasser, A Hemmelmayr… - Pediatric …, 2021 - Springer
In medicine, particularly in radiology, there are great expectations in artificial intelligence
(AI), which can “see” more than human radiologists in regard to, for example, tumor size …

Current and emerging artificial intelligence applications for pediatric musculoskeletal radiology

AC Offiah - Pediatric radiology, 2022 - Springer
Artificial intelligence (AI) is playing an ever-increasing role in radiology (more so in the adult
world than in pediatrics), to the extent that there are unfounded fears it will completely take …

Toward generalizability in the deployment of artificial intelligence in radiology: role of computation stress testing to overcome underspecification

T Eche, LH Schwartz, FZ Mokrane… - Radiology: Artificial …, 2021 - pubs.rsna.org
The clinical deployment of artificial intelligence (AI) applications in medical imaging is
perhaps the greatest challenge facing radiology in the next decade. One of the main …

Deep learning phenotype automation and cohort analyses of 1,946 knees using the coronal plane alignment of the knee classification

JR Steele, SJ Jang, ZR Brilliant, DJ Mayman… - The Journal of …, 2023 - Elsevier
Abstract Background The Coronal Plane Alignment of the Knee (CPAK) classification allows
for knee phenotyping which can be used in preoperative planning prior to total knee …

Improved productivity using deep learning–assisted reporting for lumbar spine MRI

DSW Lim, A Makmur, L Zhu, W Zhang, AJL Cheng… - Radiology, 2022 - pubs.rsna.org
Background Lumbar spine MRI studies are widely used for back pain assessment.
Interpretation involves grading lumbar spinal stenosis, which is repetitive and time …

Fully automated deep learning for knee alignment assessment in lower extremity radiographs: a cross-sectional diagnostic study

S Simon, GM Schwarz, A Aichmair, BJH Frank… - Skeletal Radiology, 2022 - Springer
Objectives Accurate assessment of knee alignment and leg length discrepancy is currently
measured manually from standing long-leg radiographs (LLR), a process that is both time …

Automated analysis of alignment in long-leg radiographs by using a fully automated support system based on artificial intelligence

J Schock, D Truhn, DB Abrar, D Merhof… - Radiology: Artificial …, 2020 - pubs.rsna.org
Purpose To develop and validate a deep learning–based method for automatic quantitative
analysis of lower-extremity alignment. Materials and Methods In this retrospective study …

A deep-learning model for identifying fresh vertebral compression fractures on digital radiography

W Chen, X Liu, K Li, Y Luo, S Bai, J Wu, W Chen… - European …, 2022 - Springer
Objectives To develop a deep-learning (DL) model for identifying fresh VCFs from digital
radiography (DR), with magnetic resonance imaging (MRI) as the reference standard …

A deep learning approach for fully automated measurements of lower extremity alignment in radiographic images

KR Moon, BD Lee, MS Lee - Scientific Reports, 2023 - nature.com
During clinical evaluation of patients and planning orthopedic treatments, the periodic
assessment of lower limb alignment is critical. Currently, physicians use physical tools and …

A practical guide to the development and deployment of deep learning models for the Orthopedic surgeon: part I

JF Oeding, RJ Williams, BU Nwachukwu… - Knee Surgery, Sports …, 2023 - Springer
Deep learning has a profound impact on daily life. As Orthopedics makes use of this rapid
escalation in technology, Orthopedic surgeons will need to take leadership roles on deep …