Automatic Detection of Post-Operative Clips in Mammography Using a U-Net Convolutional Neural Network.
Tician SchnitzlerCarlotta RuppertPatryk HejdukKarol BorkowskiJonas KajüterCristina RossiAlexander CiritsisAnna LandsmannHasan ZaytounAndreas BossSebastian SchinderaFelice BurnPublished in: Journal of imaging (2024)
With this study, we show that surgery clips can adequately be identified by an AI technique. A potential application of the proposed technique is patient triage as well as the automatic exclusion of post-operative cases from PGMI (Perfect, Good, Moderate, Inadequate) evaluation, thus improving the quality management workflow.
Keyphrases
- deep learning
- convolutional neural network
- artificial intelligence
- machine learning
- minimally invasive
- coronary artery bypass
- case report
- contrast enhanced
- high intensity
- quality improvement
- loop mediated isothermal amplification
- climate change
- risk assessment
- computed tomography
- magnetic resonance
- image quality
- human health
- real time pcr
- surgical site infection
- sensitive detection