A novel tool to provide predictable alignment data irrespective of source and image quality acquired on mobile phones: what engineers can offer clinicians.
Teng ZhangChuang ZhuQiaoyun LuJun LiuAshish D DiwanPrudence Wing Hang CheungPublished in: European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society (2020)
This is the first study using fine-tuned Mask R-CNN to predict vertebral locations on optical images of X-rays accurately and automatically. We provide a novel alignment detection method that has a significant application on teleradiology aiding out-of-hospital consultations. These slides can be retrieved under Electronic Supplementary Material.
Keyphrases
- image quality
- convolutional neural network
- computed tomography
- deep learning
- air pollution
- electronic health record
- healthcare
- dual energy
- high resolution
- palliative care
- big data
- bone mineral density
- optical coherence tomography
- loop mediated isothermal amplification
- label free
- adverse drug
- general practice
- magnetic resonance imaging
- machine learning
- postmenopausal women
- primary care
- artificial intelligence
- positive airway pressure
- sleep apnea
- quantum dots