Dataset Information

Artificial intelligence improves the diagnosis of human leukocyte antigen (HLA)-B27-negative axial spondyloarthritis based on multi-sequence magnetic resonance imaging and clinical features.

ABSTRACT:

Background

Axial spondyloarthritis (axSpA) is frequently diagnosed late, particularly in human leukocyte antigen (HLA)-B27-negative patients, resulting in a missed opportunity for optimal treatment. This study aimed to develop an artificial intelligence (AI) tool, termed NegSpA-AI, using sacroiliac joint (SIJ) magnetic resonance imaging (MRI) and clinical SpA features to improve the diagnosis of axSpA in HLA-B27-negative patients.

Methods

We retrospectively included 454 HLA-B27-negative patients with rheumatologist-diagnosed axSpA or other diseases (non-axSpA) from the Third Affiliated Hospital of Southern Medical University and Nanhai Hospital between January 2010 and August 2021. They were divided into a training set (n=328) for 5-fold cross-validation, an internal test set (n=72), and an independent external test set (n=54). To construct a prospective test set, we further enrolled 87 patients between September 2021 and August 2023 from the Third Affiliated Hospital of Southern Medical University. MRI techniques employed included T1-weighted (T1W), T2-weighted (T2W), and fat-suppressed (FS) sequences. We developed NegSpA-AI using a deep learning (DL) network to differentiate between axSpA and non-axSpA at admission. Furthermore, we conducted a reader study involving 4 radiologists and 2 rheumatologists to evaluate and compare the performance of independent and AI-assisted clinicians.

Results

NegSpA-AI demonstrated superior performance compared to the independent junior rheumatologist (≤5 years of experience), achieving areas under the curve (AUCs) of 0.878 [95% confidence interval (CI): 0.786-0.971], 0.870 (95% CI: 0.771-0.970), and 0.815 (95% CI: 0.714-0.915) on the internal, external, and prospective test sets, respectively. The assistance of NegSpA-AI promoted discriminating accuracy, sensitivity, and specificity of independent junior radiologists by 7.4-11.5%, 1.0-13.3%, and 7.4-20.6% across the 3 test sets (all P<0.05). On the prospective test set, AI assistance also improved the diagnostic accuracy, sensitivity, and specificity of independent junior rheumatologists by 7.7%, 7.7%, and 6.9%, respectively (all P<0.01).

Conclusions

The proposed NegSpA-AI effectively improves radiologists' interpretations of SIJ MRI and rheumatologists' diagnoses of HLA-B27-negative axSpA.

SUBMITTER: Lu Z

PROVIDER: S-EPMC11320510 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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Publications

Artificial intelligence improves the diagnosis of human leukocyte antigen (HLA)-B27-negative axial spondyloarthritis based on multi-sequence magnetic resonance imaging and clinical features.

Lu Zixiao Z Zou Qingqing Q Wang Menghong M Han Xinai X Shi Xingliang X Wu Shufan S Xie Zhuoyao Z Ye Qiang Q Song Liwen L He Yi Y Feng Qianjin Q Zhao Yinghua Y

Quantitative imaging in medicine and surgery 20240730 8

<h4>Background</h4>Axial spondyloarthritis (axSpA) is frequently diagnosed late, particularly in human leukocyte antigen (HLA)-B27-negative patients, resulting in a missed opportunity for optimal treatment. This study aimed to develop an artificial intelligence (AI) tool, termed NegSpA-AI, using sacroiliac joint (SIJ) magnetic resonance imaging (MRI) and clinical SpA features to improve the diagnosis of axSpA in HLA-B27-negative patients.<h4>Methods</h4>We retrospectively included 454 HLA-B27-ne ...[more]

PMID: 39144059

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Project description:BackgroundMagnetic resonance imaging (MRI) is important for the early detection of axial spondyloarthritis (axSpA). We developed an artificial intelligence (AI) model for detecting sacroiliitis in patients with axSpA using MRI.MethodsThis study included MRI examinations of patients who underwent semi-coronal MRI scans of the sacroiliac joints owing to chronic back pain with short tau inversion recovery (STIR) sequences between January 2010 and December 2021. Sacroiliitis was defined as a positive MRI finding according to the ASAS classification criteria for axSpA. We developed a two-stage framework. First, the Faster R-CNN network extracted regions of interest (ROIs) to localize the sacroiliac joints. Maximum intensity projection (MIP) of three consecutive slices was used to mimic the reading of two adjacent slices. Second, the VGG-19 network determined the presence of sacroiliitis in localized ROIs. We augmented the positive dataset six-fold. The sacroiliitis classification performance was measured using the sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). The prediction models were evaluated using three-round three-fold cross-validation.ResultsA total of 296 participants with 4,746 MRI slices were included in the study. Sacroiliitis was identified in 864 MRI slices of 119 participants. The mean sensitivity, specificity, and AUROC for the detection of sacroiliitis were 0.725 (95% CI, 0.705-0.745), 0.936 (95% CI, 0.924-0.947), and 0.830 (95%CI, 0.792-0.868), respectively, at the image level and 0.947 (95% CI, 0.912-0.982), 0.691 (95% CI, 0.603-0.779), and 0.816 (95% CI, 0.776-0.856), respectively, at the patient level. In the original model, without using MIP and dataset augmentation, the mean sensitivity, specificity, and AUROC were 0.517 (95% CI, 0.493-0.780), 0.944 (95% CI, 0.933-0.955), and 0.731 (95% CI, 0.681-0.780), respectively, at the image level and 0.806 (95% CI, 0.729-0.883), 0.617 (95% CI, 0.523-0.711), and 0.711 (95% CI, 0.660-0.763), respectively, at the patient level. The performance was improved by MIP techniques and data augmentation.ConclusionAn AI model was developed for the detection of sacroiliitis using MRI, compatible with the ASAS criteria for axSpA, with the potential to aid MRI application in a wider clinical setting.

Project description:ObjectivesTo analyse the predictive values of inflammatory back pain (IBP), positive HLA B27 antigen, increased C-reactive protein (CRP), Spondyloarthritis (SpA) features, familial history (FH), magnetic resonance sacroiliac joints (MRI-SIJ) imaging and its weight in early SpA diagnosis.Methods133 patients with back pain, aged <50, duration of the pain <2 years were included. Data such as IBP, HLA B27, increased CRP, SpA features, FH, SIJ´s radiography and MRI were collected for each patient. STIR sequences were classified as strongly positive bone morrow oedema (SPBME ≥2), clearly present and easily recognisable as positive according to the ASAS criterion, weakly positive (WPBME ≥2), suggestive, but not easily recognisable and, clearly negative none of those features. T1-weighted sequences were assessed as positive/negative for erosion, fat metaplasia, backfill and sclerosis, if ≥1, for each lesion was present. MRI images were read by three blinded readers.ResultsThe average age was 38.9 years. 47 (35.3%) patients received SpA diagnosis according to the clinical opinion. IBP was highly specific, 0.81 and sensitive, 0.83. HLA B27 was positive in a half of the SpA patients. SPBME ≥2 provided a great specificity, 0.94 and an acceptable sensitivity, 0.79. Erosion was significantly more frequent in SpA patients (72% vs 7%), specificity 0.93. The addition of erosion ≥1 to the WPBME ≥2 noticeably improved specificity, 0.98, although slightly decreased sensitivity, 0.64. Fat metaplasia and backfill were highly specific, but poorly sensitive. Factors forecasting positive diagnosis were IBP, followed by SpA features and increased CRP.ConclusionsAt the onset, IBP might be a good marker for selecting patients with suspicion of SpA. The addition of erosion to the ASAS criterion might be helpful for early diagnosis, especially in patients with doubtful STIR imaging where BME is present but it is hard to determinate whether the ASAS "highly suggestive" criterion is met.

Dataset Information

Artificial intelligence improves the diagnosis of human leukocyte antigen (HLA)-B27-negative axial spondyloarthritis based on multi-sequence magnetic resonance imaging and clinical features.

Background

Methods

Results

Conclusions

Publications

Artificial intelligence improves the diagnosis of human leukocyte antigen (HLA)-B27-negative axial spondyloarthritis based on multi-sequence magnetic resonance imaging and clinical features.

Similar Datasets

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