AI Article Synopsis
- Osteoporotic vertebral fractures (OVFs) often go undetected in chest radiographs, especially in post-menopausal women over 60, necessitating an effective detection tool.
- The study evaluated the AI tool Ofeye 1.0's performance in identifying OVFs on lateral chest x-rays, finding that it detected missed fractures in 28.8% of cases and had improved sensitivity compared to original radiologist reports.
- While Ofeye 1.0 showed high specificity (92.8%) and moderate overall accuracy (80.3%), its lower sensitivity (49%) indicates it should be used as a complementary tool alongside traditional radiology.
Article Abstract
Osteoporotic vertebral fractures (OVFs) are often not reported by radiologists on routine chest radiographs. This study aims to investigate the clinical value of a newly developed artificial intelligence (AI) tool, Ofeye 1.0, for automated detection of OVFs on lateral chest radiographs in post-menopausal women (>60 years) who were referred to undergo chest x-rays for other reasons. A total of 510 de-identified lateral chest radiographs from three clinical sites were retrieved and analysed using the Ofeye 1.0 tool. These images were then reviewed by a consultant radiologist with findings serving as the reference standard for determining the diagnostic performance of the AI tool for the detection of OVFs. Of all the original radiologist reports, missed OVFs were found in 28.8% of images but were detected using the AI tool. The AI tool demonstrated high specificity of 92.8% (95% CI: 89.6, 95.2%), moderate accuracy of 80.3% (95% CI: 76.3, 80.4%), positive predictive value (PPV) of 73.7% (95% CI: 65.2, 80.8%), and negative predictive value (NPV) of 81.5% (95% CI: 79, 83.8%), but low sensitivity of 49% (95% CI: 40.7, 57.3%). The AI tool showed improved sensitivity compared with the original radiologist reports, which was 20.8% (95% CI: 14.5, 28.4). The new AI tool can be used as a complementary tool in routine diagnostic reports for the reduction in missed OVFs in elderly women.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10743975 | PMC |
| http://dx.doi.org/10.3390/jcm12247730 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Radiographic findings in dogs from an endemic area for heartworm disease in the state of Rio de Janeiro.
Braz J Vet Med
January 2025
Veterinarian, DSc, DMCV, IV, UFRRJ, Seropédica RJ, Brazil.
Canine heartworm disease causes significant pulmonary abnormalities, even in asymptomatic cases. This study aimed to compare the chest radiographs of cases infected with with those of heartworm-negative dogs. Fifteen animals treated at a private veterinary clinic in the municipality of Maricá, Rio de Janeiro, underwent chest radiography, regardless of their serological status, for the presence of antigen, and none of them used preventive measures.
View Article and Find Full Text PDFA review of imaging in the diagnosis and management of complicated paediatric pneumonia.
Paediatr Respir Rev
December 2024
Faculty of Medicine, University of Wollongong, Wollongong, NSW, Australia; Department of Respiratory Medicine, The Children's Hospital at Westmead, Westmead, NSW, Australia. Electronic address:
Paediatric pneumonia and its complications present substantial health and economic challenges. While chest radiographs are commonly used as the initial imaging modality for diagnosing uncomplicated pneumonia, they are less effective for complicated cases. In response, various imaging techniques, such as lung ultrasound, computed tomography (CT), and chest MRI, have been integrated into clinical practice to enhance diagnosis and guide management decisions.
View Article and Find Full Text PDFPhantom evaluation of feasibility and applicability of artificial intelligence based pulmonary nodule detection in chest radiographs.
Medicine (Baltimore)
November 2024
Department of Radiology, Kantonsspital Baden, affiliated Hospital for Research and Teaching of the Faculty of Medicine of the University of Zurich, Baden, Switzerland.
The aim of our study was to evaluate the specific performance of an artificial intelligence (AI) algorithm for lung nodule detection in chest radiography for a larger number of nodules of different sizes and densities using a standardized phantom approach. A total of 450 nodules with varying density (d1 to d3) and size (3, 5, 8, 10 and 12 mm) were inserted in a Lungman phantom at various locations. Radiographic images with varying projections were acquired and processed using the AI algorithm for nodule detection.
View Article and Find Full Text PDFA Single-Institution Analysis of Surveillance Practice for Low-Risk Neuroblastic Tumors.
J Surg Res
January 2025
Division of Pediatric Surgery, Nationwide Children's Hospital, Department of Surgery, The Ohio State University College of Medicine, Columbus, Ohio. Electronic address:
Introduction: Currently, few prospective guidelines exist for the surveillance of children with low-risk neuroblastic tumors (LRNBTs), including ganglioneuroma or ganglioneuroblastoma intermixed. This study aims to describe our institutional approach to LRNBT surveillance following surgical resection or nonoperative management. We hypothesize that length of surveillance can be reduced due to low recurrence risk.
View Article and Find Full Text PDFSkeletal surveys for suspected infant abuse: patient-specific radiation dose estimation using a hybrid computational phantom.
Pediatr Radiol
January 2025
Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, 02115, USA.
Background: Radiographic skeletal survey plays an important role in the diagnosis of infant abuse. Some practitioners have expressed concerns about the radiation exposure from this examination.
Objective: To utilize state-of-the-art hybrid computational phantoms to more accurately estimate radiation doses of skeletal surveys performed for suspected infant abuse.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!