Ankylosing spondylitis (AS) is a systemic, chronic, and inflammatory autoimmune disease associated with the disorder of intestinal microbiota. Unfortunately, effective therapies for AS are lacking. Recent evidence has indicated that indole-3-acetic acid (IAA), an important microbial tryptophan metabolite, can modulate intestinal homeostasis and suppress inflammatory responses. However, reports have not examined the in vivo protective effects of IAA against AS. In this study, we investigated the protective effects and underlying mechanisms through which IAA acts against AS. We constructed a proteoglycan (PG)-induced AS mouse model and administered IAA (50 mg/kg body weight) by intraperitoneal injection daily for 4 weeks. The effects of IAA on AS mice were evaluated by examining disease severity, intestinal barrier function, aryl hydrocarbon receptor (AhR) pathway, T-helper 17 (Th17)/T regulatory (Treg) balance, and inflammatory cytokine levels. The intestinal microbiota compositions were profiled through whole-genome sequencing. We observed that IAA decreased the incidence and severity of AS in mice, inhibited the production of pro-inflammatory cytokines (tumor necrosis factor α [TNF-α], interleukin [IL]-6, IL-17A, and IL-23), promoted the production of the anti-inflammatory cytokine IL-10, and reduced the ratios of pro-/anti- inflammatory cytokines. IAA ameliorated pathological changes in the ileum and improved intestinal mucosal barrier function. IAA also activated the AhR pathway, upregulated the transcription factor forehead box protein P3 (FoxP3) and increased Treg cells, and downregulated the transcription factors retinoic acid receptor–related orphan receptor gamma t (RORγt) and signal transducer and activator of transcription 3 (STAT3) and decreased Th17 cells. Furthermore, IAA altered the composition of the intestinal microbiota composition by increasing Bacteroides and decreasing Proteobacteria and Firmicutes, in addition to increasing the abundances of Bifidobacterium pseudolongum and Mucispirillum schaedleri. In conclusion, IAA exerted several protective effects against PG-induced AS in mice, which was mediated by the restoration of balance among the intestinal microbial community, activating the AhR pathway, and inhibiting inflammation. IAA might represent a novel therapeutic approach for AS.

Aim: Accurate severity grading of lumbar spine disease by magnetic resonance images (MRIs) plays an important role in selecting appropriate treatment for the disease. However, interpreting these complex MRIs is a repetitive and time-consuming workload for clinicians, especially radiologists. Here, we aim to develop a multi-task classification model based on artificial intelligence for automated grading of lumbar disc herniation (LDH), lumbar central canal stenosis (LCCS) and lumbar nerve roots compression (LNRC) at lumbar axial MRIs. Methods: Total 15254 lumbar axial T2W MRIs as the internal dataset obtained from the Fifth Affiliated Hospital of Sun Yat-sen University from January 2015 to May 2019 and 1273 axial T2W MRIs as the external test dataset obtained from the Third Affiliated Hospital of Southern Medical University from June 2016 to December 2017 were analyzed in this retrospective study. Two clinicians annotated and graded all MRIs using the three international classification systems. In agreement, these results served as the reference standard; In disagreement, outcomes were adjudicated by an expert surgeon to establish the reference standard. The internal dataset was randomly split into an internal training set (70%), validation set (15%) and test set (15%). The multi-task classification model based on ResNet-50 consists of a backbone network for feature extraction and three fully-connected (FC) networks for classification and performs the classification tasks of LDH, LCCS, and LNRC at lumbar MRIs. Precision, accuracy, sensitivity, specificity, F1 scores, confusion matrices, receiver-operating characteristics and interrater agreement (Gwet k) were utilized to assess the model’s performance on the internal test dataset and external test datasets. Results: A total of 1115 patients, including 1015 patients from the internal dataset and 100 patients from the external test dataset [mean age, 49 years ± 15 (standard deviation); 543 women], were evaluated in this study. The overall accuracies of grading for LDH, LCCS and LNRC were 84.17% (74.16%), 86.99% (79.65%) and 81.21% (74.16%) respectively on the internal (external) test dataset. Internal and external testing of three spinal diseases showed substantial to the almost perfect agreement (k, 0.67 - 0.85) for the multi-task classification model. Conclusion: The multi-task classification model has achieved promising performance in the automated grading of LDH, LCCS and LNRC at lumbar axial T2W MRIs.

Background: The severity assessment of lumbar disc herniation (LDH) on MR images is crucial for selecting suitable surgical candidates. However, the interpretation of MR images is time‐consuming and requires repetitive work. This study aims to develop and evaluate a deep learning‐based diagnostic model for automated LDH detection and classification on lumbar axial T2‐weighted MR images.Methods: A total of 1115 patients were analyzed in this retrospective study; both a development dataset (1015 patients, 15 249 images) and an external test dataset (100 patients, 1273 images) were utilized. According to the Michigan State University (MSU) classification criterion, experts labeled all images with consensus, and the final labeled results were regarded as the reference standard. The automated diagnostic model comprised Faster R‐CNN and ResNeXt101 as the detection and classification network, respectively. The deep learning‐based diagnostic performance was evaluated by calculating mean intersection over union (IoU), accuracy, precision, sensitivity, specificity, F1 score, the area under the receiver operating characteristics curve (AUC), and intraclass correlation coefficient (ICC) with 95% confidence intervals (CIs).Results: High detection consistency was obtained in the internal test dataset (mean IoU = 0.82, precision = 98.4%, sensitivity = 99.4%) and external test dataset (mean IoU = 0.70, precision = 96.3%, sensitivity = 97.8%). Overall accuracy for LDH classification was 87.70% (95% CI: 86.59%–88.86%) and 74.23% (95% CI: 71.83%–76.75%) in the internal and external test datasets, respectively. For internal testing, the proposed model achieved a high agreement in classification (ICC = 0.87, 95% CI: 0.86–0.88, P < 0.001), which was higher than that of external testing (ICC = 0.79, 95% CI: 0.76–0.81, P < 0.001). The AUC for model classification was 0.965 (95% CI: 0.962–0.968) and 0.916 (95% CI: 0.908–0.925) in the internal and external test datasets, respectively.Conclusions: The automated diagnostic model achieved high performance in detecting and classifying LDH and exhibited considerable consistency with experts' classification.

Objective: 3D reconstruction of lumbar intervertebral foramen (LIVF) has been beneficial in evaluating surgical trajectory. Still, the current methods of reconstructing the 3D LIVF model are mainly based on manual segmentation, which is laborious and time‐consuming. This study aims to explore the feasibility of automatically segmenting lumbar spinal structures and increasing the speed and accuracy of 3D lumbar intervertebral foramen (LIVF) reconstruction on magnetic resonance image (MRI) at the L4‐5 level.Methods: A total of 100 participants (mean age: 42.2 ± 14.0 years; 52 males and 48 females; mean body mass index, 22.7 ± 3.2 kg/m²), were enrolled in this prospective study between March and July 2020. All participants were scanned on L4‐5 level with a 3T MR unit using 3D T2‐weighted sampling perfection with application‐optimized contrast with various flip‐angle evolutions (SPACE) sequences. The lumbar spine's vertebra bone structures (VBS) and intervertebral discs (IVD) were manually segmented by skilled surgeons according to their anatomical outlines from MRI. Then all manual segmentation were saved and used for training. An automated segmentation method based on a 3D U‐shaped architecture network (3D‐UNet) was introduced for the automated segmentation of lumbar spinal structures. A number of quantitative metrics, including dice similarity coefficient (DSC), precision, and recall, were used to evaluate the performance of the automated segmentation method on MRI. Wilcoxon signed‐rank test was applied to compare morphometric parameters, including foraminal area, height and width of 3D LIVF models between automatic and manual segmentation. The intra‐class correlation coefficient was used to assess the test‐retest reliability and inter‐observer reliability of multiple measurements for these morphometric parameters of 3D LIVF models.Results: The automatic segmentation performance of all spinal structures (VBS and IVD) was found to be 0.918 (healthy levels: 0.922; unhealthy levels: 0.916) for the mean DSC, 0.922 (healthy levels: 0.927; unhealthy levels: 0.920) for the mean precision, and 0.917 (healthy levels: 0.918; unhealthy levels: 0.917) for the mean recall in the test dataset. It took approximately 2.5 s to achieve each automated segmentation, far less than the 240 min for manual segmentation. Furthermore, no significant differences were observed in the foraminal area, height and width of the 3D LIVF models between manual and automatic segmentation images (P > 0.05).Conclusion: A method of automated MRI segmentation based on deep learning algorithms was capable of rapidly generating accurate segmentation of spinal structures and can be used to construct 3D LIVF models from MRI at the L4‐5 level.