Detection of Imperceptible Intervertebral Disc Fissures in Conventional MRI-An AI Strategy for Improved Diagnostics.
Christian WaldenbergStefanie ErikssonHelena BrisbyHanna HebelkaKerstin Magdalena LagerstrandPublished in: Journal of clinical medicine (2022)
Annular fissures in the intervertebral discs are believed to be closely related to back pain. However, no sensitive non-invasive method exists to detect annular fissures. This study aimed to propose and test a method capable of detecting the presence and position of annular fissures in conventional magnetic resonance (MR) images non-invasively. The method utilizes textural features calculated from conventional MR images combined with attention mapping and artificial intelligence (AI)-based classification models. As ground truth, reference standard computed tomography (CT) discography was used. One hundred twenty-three intervertebral discs in 43 patients were examined with MR imaging followed by discography and CT. The fissure classification model determined the presence of fissures with 100% sensitivity and 97% specificity. Moreover, the true position of the fissures was correctly determined in 90 (87%) of the analyzed discs. Additionally, the proposed method was significantly more accurate at identifying fissures than the conventional radiological high-intensity zone marker. In conclusion, the findings suggest that the proposed method is a promising diagnostic tool to detect annular fissures of importance for back pain and might aid in clinical practice and allow for new non-invasive research related to the presence and position of individual fissures.
Keyphrases
- contrast enhanced
- artificial intelligence
- deep learning
- computed tomography
- magnetic resonance
- machine learning
- high intensity
- magnetic resonance imaging
- big data
- positron emission tomography
- end stage renal disease
- convolutional neural network
- clinical practice
- high resolution
- image quality
- chronic kidney disease
- newly diagnosed
- optical coherence tomography
- mass spectrometry
- real time pcr
- resistance training
- body composition
- patient reported outcomes