This year’s trip along the Road to RSNA for digital x-ray features mileposts mostly set by AI research. Models will be proposed for applications ranging from predicting bone density on chest x-rays to generating complete reports on anterior cruciate ligament (ACL) tears.
Yet several presentations to be given at the meeting reminded us that issues concerning basic elements of x-ray technology remain highly important, as well as whether imaging access is equitable in the "real-world."
In one, a group at the University of Washington in St. Louis asked, “How much ionizing radiation are neonatal patients exposed to during interventional procedures?” The findings have implications for future risks of malignancy in these fragile patients, according to the researchers.
In another, Temple University researchers in Philadelphia, PA, will provide evidence suggesting that disparities exist across racial and ethnic groups in the use of dual-energy x-ray absorptiometry (DEXA) scans around the time of hip fractures.
Nonetheless, AI is poised to take top headlines. Exciting presentations will cover a generative AI model that shows potential for making surgical planning for total hip arthroplasty (THA) more efficient and a deep-learning model developed to identify individuals at high risk of chronic obstructive pulmonary disease (COPD).
Here, we’ve highlighted just a small sample of digital x-ray presentations scheduled for RSNA 2023. You can also view the complete 2022 scientific and educational program on the RSNA 2023 website.
Sunday, November 26 | 1:00 p.m.-1:10 p.m. | S4-SSMK02-2 | Room E353C
Generative AI technology shows potential for making surgical planning for total hip arthroplasty (THA) more efficient, according to researchers from the Mayo Clinic.
Sunday, November 26 | 1:50 p.m.-2:00 p.m. | S4-SSMK02-5 | Room E353C
In this session, evidence will be presented that suggests disparities exist across racial and ethnic groups in the use of dual-energy x-ray absorptiometry (DEXA) scans around the time of hip fractures.
Monday, November 27 | 11:30 a.m.-11:40 a.m. | M4-SSMK03-4 | Room E450A
A deep-learning AI model will be presented in this session that can predict bone mineral density T-scores from chest x-rays.
Monday, November 27 | 3:10 p.m.-3:20 p.m. | M7-SSPH05-2 | Room N229
Findings will be presented in this Monday afternoon presentation on organ-specific ionizing radiation doses in neonatal patients who undergo interventional procedures for congenital heart disease (CHD).
Tuesday, November 28 | 8:30 a.m.-8:40 a.m. | T1-SSPD03-3 | Room E353B
In this session on pediatric imaging, a deep-learning model will be presented that is designed to assess bone age by visualizing features of the olecranon on lateral elbow x-rays.
Tuesday, November 28 | 9:50 a.m.-10:00 a.m. | T3-SSER01-3 | Room E451A
A tool that prioritizes x-ray exams when it detects fractures yields “tremendous reductions” in report turnaround times, according to a study to be presented in this session.
Wednesday, November 29 | 9:40 a.m.-9:50 a.m. | W3-SSMK08 | Room 5E450A
In this session on musculoskeletal imaging, a deep-learning AI algorithm will be presented for measuring shoulder kinematics using dynamic digital radiography (DDR) images.
Thursday, November 30 | 8:20 a.m.-8:30 a.m. | R1-SSCH09-3 | Room E352
This scientific presentation will present external validation results for a deep-learning model in identifying individuals at high risk of incident chronic obstructive pulmonary disease (COPD) on routine outpatient chest x-rays (CXR).
Thursday, November 30 | 9:10 a.m.-9:20 a.m. | R3-SSIN07-2 | Room N227B
In this session, an AI chatbot called MedVisGPT will be introduced that can help diagnose anterior cruciate ligament (ACL) tears on knee x-rays and produce detailed medical reports in seconds based on conversations with users.
Thursday, November 30 | 1:40 p.m.-1:50 p.m. | R6-SSPH15-2 | Room S501
During this session, early results will be presented of an ongoing pilot study comparing dynamic digital radiography (DDR) with perfusion assessment and CT pulmonary angiography (CTPA) in evaluating suspected pulmonary thromboembolism.