Artificial Intelligence Algorithm-Based Lumbar and Spinal MRI for Evaluation of Efficacy of Chinkuei Shin Chewan Decoction on Lumbar Spinal Stenosis

The study aimed to explore the application value of lumbar Magnetic Resonance Imaging (MRI) images processed by artificial intelligence algorithms in evaluating the efficacy of chinkuei shin chewan decoction (a traditional Chinese medicine to nourish the kidney) in the treatment of lumbar spinal stenosis (LSS). Specifically, 110 LSS patients admitted to the hospital were selected as the research subjects. They were randomly divided into the control group (n = 55) and experimental group (n = 55) according to different treatment methods. The control group was treated with traditional medicine, and the experimental group additionally took chinkuei shin chewan decoction on its basis. Based on the traditional U-net algorithm, a U-net registration algorithm based on artificial intelligence was designed by introducing the information entropy theory, and the algorithm was applied to the lumbar MRI image evaluation of LSS patients. Compared with the traditional U-net algorithm, the artificial intelligence-based U-net registration algorithm had a decreased noise level P < 0.05 , the Jaccard (J) value (0.84) and the Dice value (0.93) increased significantly versus the traditional algorithm (J = 0.63, Dice = 0.81), and the characteristics of the image were more accurate. Before treatment, the Oswestry Disability Index (ODI) scores of the experimental group and the control group were 44.32 ± 6.45 and 43.32 ± 5.45, respectively. After treatment, the ODI scores of the two groups were 10.21 ± 5.05 and 17.09 ± 5.23, respectively. Both showed significant improvement, while the improvement of the experimental group was more obvious than that of the control group P < 0.05 . The overall effective rates of the two groups of patients were 96.44% and 82.47%, respectively, and the experimental group was significantly higher than the control group P < 0.05 . Under the U-net registration algorithm based on artificial intelligence, the diagnostic accuracy of lumbar MRI in the experimental group was 94.45%, significantly higher than 67.5% before the introduction of the algorithm P < 0.05 . In conclusion, chinkuei shin chewan decoction are effective for the treatment of LSS, and lumbar MRI based on the artificial intelligence U-net registration algorithm can evaluate the efficacy of LSS well and is worthy of promotion.

Appropriateness of Referrals From Primary Care for Lumbar MRI

Background: A number of papers highlight the extent to which low back pain (LBP) is generally mismanaged, especially regarding overuse of magnetic resonance imaging (MRI). International guidelines do not recommend routine imaging, including MRI, and seek to guide clinicians only to refer for imaging based on specific indications. Despite this, several studies show an increase in the use of MRI among patients with LBP and an imbalance between appropriate versus inappropriate use of MRI for LBP. This study aimed to investigate to what extent referrals from general practice for lumbar MRI complied with clinical guideline recommendations in a Danish setting.Materials and methods: From 2014-2018, all referrals for lumbar MRI were included from general practitioners in the Central Denmark Region for diagnostic imaging at a public regional hospital. A modified version of the American College of Radiology Imaging Appropriateness Criteria for LBP was used to classify referrals as appropriate or inappropriate, based on the unstructured text in the GPs’ referrals. Appropriate referrals included fractures, cancer, symptoms persisting for more than 6 weeks of non-surgical treatment, previous surgery, candidate for surgery or suspicion of cauda equina. Inappropriate referrals were sub-classified as lacking information about previous non-surgical treatment and duration. Results: Of the 3,772 retrieved referrals for MRI of the lumbar spine, 55% were selected and a total of 2,051 referrals were categorised. Approximately one quarter (24.5%) were categorised as appropriate, and 75.5% were deemed inappropriate. 51% of the inappropriate referrals lacked information about previous non-surgical treatment, and 49% had no information about the duration of non-surgical treatment. Apart from minor yearly fluctuations, there was no change in the distribution of appropriate and inappropriate MRI referrals from 2014 to 2018.Conclusion:The majority of lumbar MRI referrals (75.5%) from general practitioners for lumbar MRI did not fulfil the ACR Imaging Appropriateness Criteria for LBP based on the unstructured text of their referrals. There is a need for referrers to include all guideline-relevant information in referrals for imaging. More research is needed to determine whether this is due to patients not fulfilling guideline recommendations or simply the content of the referrals.

An interactive system for muscle and fat tissue identification of the lumbar spine using semantic segmentation

We introduce a system that allows the immediate identification and inspection of fat and muscle structures around the lumbar spine as a means of orthopaedic diagnostics before surgical treatment. The system comprises a backend component that accepts MRI data from a web-based interactive frontend as REST requests. The MRI data is passed through a U-net model, fine-tuned on lumbar MRI images, to generate segmentation masks of fat and muscle areas. The result is sent back to the frontend that functions as an inspection tool. For the model training, 4000 MRI images from 108 patients were used in a k-fold cross-validation study with k = 10. The model training was performed over 25-30 epochs. We applied shift, scale, and rotation operations as well as elastic deformation and distortion functions for image augmentation and a combined objective function using Dice and Focal loss. The trained models reached a mean dice score of 0.83 and 0.52 and a mean area error tissue of 0.1 and 0.3 for muscle and fat tissue, respectively. The interactive webbased frontend as an inspection tool was evaluated by clinicians to be suitable for the exploration of patient data as well as the assessment of segmentation results. We developed a system that uses semantic segmentation to identify fat and muscle tissue areas in MRI images of the lumbar spine. Further improvements should focus on the segmentation accuracy of fat tissue, as it is a determining factor in surgical decisionmaking. To our knowledge, this is the first system that automatically provides semantic information of the respective lumbar tissues.

Gastric balloon: A rare cause of lumbar radiculopathy

Background: Factors that are known to cause lumbar epidural venous plexus (EVP) engorgement include inferior vena cava (IVC) obstruction, portal hypertension, vascular agenesis, morbid obesity, and/or hypercoagulable states. Here, we present a 32-year-old female admitted with the new onset of lumbar radiculopathy attributed to a gastric balloon causing compression of the IVC and engorgement of the EVP. Case Description: A 32-year-old female was admitted with a left L5 radiculopathy. She had a history of morbid obesity and had undergone intragastric balloon insertion 4 months ago. The abdominal/pelvic CT documented an intragastric balloon producing a voluminous gastric mass with resultant compression of the IVC. The lumbar MRI showed the resultant marked multilevel engorgement of the lumbar EVP. Here, following balloon removal, the patient was immediately symptom free and remained asymptomatic over the next postoperative year. Conclusion: An intragastric balloon can produce a voluminous gastric mass that can result in IVC occlusion and engorgement of the EVP, leading to lumbar radiculopathy. Removal of the balloon results in immediate and permanent resolution of the compressive symptoms.

The ozone: a therapeutic mediator in MODIC processes.

MODIC changes of signal in lumbar MRI offered a new vision of the spine degenerations process. Several theories try to explain this phenomenom. After a critical apraisal and based on our own experience, we propose ozone intradiscal injection as an effective and safe treatment for disc degeneration disease.

Image Quality Assessment to Emulate Experts’ Perception in Lumbar MRI Using Machine Learning

Medical image quality is crucial to obtaining reliable diagnostics. Most quality controls rely on routine tests using phantoms, which do not reflect closely the reality of images obtained on patients and do not reflect directly the quality perceived by radiologists. The purpose of this work is to develop a method that classifies the image quality perceived by radiologists in MR images. The focus was set on lumbar images as they are widely used with different challenges. Three neuroradiologists evaluated the image quality of a dataset that included T1-weighting images in axial and sagittal orientation, and sagittal T2-weighting. In parallel, we introduced the computational assessment using a wide range of features extracted from the images, then fed them into a classifier system. A total of 95 exams were used, from our local hospital and a public database, and part of the images was manipulated to broaden the distribution quality of the dataset. Good recall of 82% and an area under curve (AUC) of 77% were obtained on average in testing condition, using a Support Vector Machine. Even though the actual implementation still relies on user interaction to extract features, the results are promising with respect to a potential implementation for monitoring image quality online with the acquisition process.