None listed
Conditions
Brief summary
The prospective portion of this study will evaluate this deep learning AI system for opportunistic detection of osteoporosis on chest radiographs in a community setting. This study will recruit participants 50 years of age or over, who have undergone a chest x-ray in the preceding 6 weeks. Osteoporosis affects one million Australians and is associated with an increased risk of minimal trauma (“fragility”) fractures. There are effective medications for treating osteoporosis, and timely intervention can reduce the risk of future fractures by up to 70% and mortality by 11%. However, a significant treatment gap in osteoporosis exists, and the majority of patients that present to hospital with a minimal trauma fracture are neither assessed nor appropriately managed for osteoporosis. The gold-standard for diagnosing osteoporosis is by measuring bone mineral density (BMD) using dual energy X-ray absorptiometry (DEXA) or bone densitometry. Access to DEXA scanners depends on the limited availability of equipment and Medicare rebate restrictions. Therefore, opportunistic screening for osteoporosis using Artificial Intelligence (AI) technology represents an approach for identifying patients at higher risk of osteoporosis and more likely to benefit from having a DEXA BMD study. The retrospective portion of this study will continue the development of AI technology for analysing chest X-rays to estimate BMD. The prospective portion of this study will evaluate this deep learning AI system for opportunistic detection of osteoporosis on chest radiographs in a community setting. This study will recruit participants 50 years of age or over, who have undergone a chest x-ray in the preceding 6 weeks.
Interventions
This study is about using Artificial Intelligence (AI) to predict bone health from chest X-rays. Normally, osteoporosis is diagnosed using a DEXA scan, which measures bone mineral density (BMD). However, DEXA scans are not always available to everyone, so researchers are testing whether an AI model can estimate bone density just from a chest X-ray. Researchers will use Deep Learning AI model designed for image classification. The AI model will be trained using ~80,000 X-rays and ~15,000 DEXA scans from real patients to find patterns in X-rays that relate to bone strength. The model was tested on a retrospective subset of X-rays that were not used during training with 80% sensitivity and 80% specificity for diagnosis of osteoporosis. The goal is to see how close the AI's predictions are to the real bone density measurements. The chest x-ray and BMD scans will be delivered by accredited radiographers. BMD scan radiation doses are significantly less than a typical chest x-ray and around 1% of the radiation dose of a chest x-ray. The processing of the deep learning model could be done on-premises (local-server within the hospital) or cloud-based AI system. Correlating BMD scan to the chest x-ray will only be done once and will be within a period of 3 months. The setting for this intervention will occur within accredited Radiology practices with Flinders University staff ensuring that BMD appointments occurs within the stipulated 3 months as defined in the protocol. The prospective portion of this study will evaluate this deep learning AI system for opportunistic detection of osteoporosis on chest radiographs in a community setting. Patients referred to a radiology clinic for a chest-x ray will be screened for eligibility. At regular intervals, representatives from the clinic will provide the investigator and delegated staff with a listing of patients who have had chest x-rays performed within the last 2 weeks. Patients will then be contacted by the research team via phone and assessed if they are eligible and willing to participate in the study. Patient consent will be obtained at the time of performing the DEXA scan. Patients flagged for a follow-up DEXA study will be contacted by designated research staff and referred to an external imaging provider to have the study performed. Patients attending a radiology clinic for a chest x-ray study will be provided with a short pamphlet or brochure by the attending health professional or clerical staff that outlines the nature of the study and contact details of the research team. Patients would contact the research team to express their interest and would be assessed if they are eligible to participate in the study. Patients flagged for a follow-up DEXA study will be contacted by designated research staff and referred to an external imaging provider to have the study performed. Recruitment will continue until approximately 1307 DEXA BMD studies have been performed.
Sponsors
Study design
Eligibility
Inclusion criteria
Inclusion criteria for prospective participants. Patient is 50 years of age or older Patient has independent mobility and is capable of transferring to a DEXA scanner table Patient is willing and able to provide informed consent Patient has undergone a chest X-ray with both frontal and lateral views Patient is willing and able to undergo a subsequent DEXA BMD scan within 6 weeks of their initial chest x-ray Patient must weigh 227kg or less
Exclusion criteria
Patient has implants, hardware, foreign material or other devices in the lumbar spine or hips Patient has severe degenerative changes or a fracture deformity in the lumbar spine or hips Patient is pregnant Patient has had a previous radiological or nuclear medicine investigation in the 7 days prior to the planned DEXA scan Any other condition that prevents the proper positioning of the patient to be able to obtain accurate BMD values. Patient is unable to remain motionless for the duration of the scan