Computer-Aided Diagnosis of Spinal Tuberculosis From CT Images Based on Deep Learning With Multimodal Feature Fusion

The Application of Artificial Intelligence in Spine Surgery: A Scoping Review

BACKGROUND

A comprehensive review on the application of artificial intelligence (AI) within spine surgery as a specialty remains lacking.

METHODS

This scoping review was conducted upon PubMed and EMBASE databases according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Our analysis focused on publications from January 1, 2020, to March 31, 2024, with a specific focus on AI in the field of spine surgery. Review articles and articles predominantly concerning secondary validation of algorithms, medical physics, electronic devices, biomechanics, preclinical, and with a lack of clinical emphasis were excluded.

RESULTS

One hundred five studies were included after our inclusion/exclusion criteria were applied. Most studies (n = 100) were conducted through supervised learning upon prelabeled data sets. Overall, 38 studies used conventional machine learning methods upon predefined features, whereas 67 used deep learning methods, predominantly for medical image analyses. Only 25.7% of studies (27/105) collected data from more than 1,000 patients for model development and validation. Data originated from only a single center in 72 studies. The most common application was prognostication (38/105), followed by diagnosis (35/105), medical image processing (29/105), and surgical assistance (3/105).

CONCLUSION

The application of AI within the domain of spine surgery has significant potential to advance patient-specific diagnosis, management, and surgical execution.

A systematic review of deep learning-based spinal bone lesion detection in medical images

Spinal bone lesions encompass a wide array of pathologies, spanning from benign abnormalities to aggressive malignancies, such as diffusely localized metastases. Early detection and accurate differentiation of the underlying diseases is crucial for every patient's clinical treatment and outcome, with radiological imaging being a core element in the diagnostic pathway. Across numerous pathologies and imaging techniques, deep learning (DL) models are progressively considered a valuable resource in the clinical setting. This review describes not only the diagnostic performance of these models and the differing approaches in the field of spinal bone malignancy recognition, but also the lack of standardized methodology and reporting that we believe is currently hampering this newly founded area of research. In line with their established and reliable role in lesion detection, this publication focuses on both computed tomography and magnetic resonance imaging, as well as various derivative modalities (i.e. SPECT). After conducting a systematic literature search and subsequent analysis for applicability and quality using a modified QUADAS-2 scoring system, we confirmed that most of the 14 identified studies were plagued by major limitations, such as insufficient reporting of model statistics and data acquisition, a lacking external validation dataset, and potentially biased annotation. Although we experienced these limitations, we nonetheless conclude that the potential of these methods shines through in the presented results. These findings underline the need for more stringent quality controls in DL studies, as well as model development to afford increased insight and progress in this promising novel field.

Combined clinical significance of MRI and serum mannose-binding lectin in the prediction of spinal tuberculosis

BACKGROUND

Spinal tuberculosis (STB) is a local manifestation of systemic infection caused by Mycobacterium tuberculosis, accounting for a significant proportion of joint tuberculosis cases. This study aimed to explore the diagnostic value of MRI combined with mannose-binding lectin (MBL) for STB.

METHODS

124 patients suspected of having STB were collected and divided into STB and non-STB groups according to their pathological diagnosis. Serum MBL levels were measured using ELISA and a Pearson analysis was constructed to determine the correlation between MBL and STB. ROC was plotted to analyze their diagnostic value for STB. All the subjects included in the study underwent an MRI.

RESULTS

The sensitivity of MRI for the diagnosis of STB was 84.38% and specificity was 86.67%. The serum MBL levels of the patients in the STB group were significantly lower than the levels in the non-STB group. ROC analysis results indicated that serum MBL's area under the curve (AUC) for diagnosis of STB was 0.836, with a sensitivity of 82.3% and a specificity was 77.4%. The sensitivity of MRI combined with MBL diagnosis was 96.61%, and the specificity was 92.31%, indicating that combining the two diagnostic methods was more effective than using either one alone.

CONCLUSIONS

Both MRI and MBL had certain diagnostic values for STB, but their combined use resulted in a diagnostic accuracy than either one alone.

Prediction model for spinal cord injury in spinal tuberculosis patients using multiple machine learning algorithms: a multicentric study

Spinal cord injury (SCI) is a prevalent and serious complication among patients with spinal tuberculosis (STB) that can lead to motor and sensory impairment and potentially paraplegia. This research aims to identify factors associated with SCI in STB patients and to develop a clinically significant predictive model. Clinical data from STB patients at a single hospital were collected and divided into training and validation sets. Univariate analysis was employed to screen clinical indicators in the training set. Multiple machine learning (ML) algorithms were utilized to establish predictive models. Model performance was evaluated and compared using receiver operating characteristic (ROC) curves, area under the curve (AUC), calibration curve analysis, decision curve analysis (DCA), and precision-recall (PR) curves. The optimal model was determined, and a prospective cohort from two other hospitals served as a testing set to assess its accuracy. Model interpretation and variable importance ranking were conducted using the DALEX R package. The model was deployed on the web by using the Shiny app. Ten clinical characteristics were utilized for the model. The random forest (RF) model emerged as the optimal choice based on the AUC, PRs, calibration curve analysis, and DCA, achieving a test set AUC of 0.816. Additionally, MONO was identified as the primary predictor of SCI in STB patients through variable importance ranking. The RF predictive model provides an efficient and swift approach for predicting SCI in STB patients.

Explainable machine learning for early predicting treatment failure risk among patients with TB-diabetes comorbidity

The present study aims to assess the treatment outcome of patients with diabetes and tuberculosis (TB-DM) at an early stage using machine learning (ML) based on electronic medical records (EMRs). A total of 429 patients were included at Chongqing Public Health Medical Center. The random-forest-based Boruta algorithm was employed to select the essential variables, and four models with a fivefold cross-validation scheme were used for modeling and model evaluation. Furthermore, we adopted SHapley additive explanations to interpret results from the tree-based model. 9 features out of 69 candidate features were chosen as predictors. Among these predictors, the type of resistance was the most important feature, followed by activated partial throm-boplastic time (APTT), thrombin time (TT), platelet distribution width (PDW), and prothrombin time (PT). All the models we established performed above an AUC 0.7 with good predictive performance. XGBoost, the optimal performing model, predicts the risk of treatment failure in the test set with an AUC 0.9281. This study suggests that machine learning approach (XGBoost) presented in this study identifies patients with TB-DM at higher risk of treatment failure at an early stage based on EMRs. The application of a convenient and economy EMRs based on machine learning provides new insight into TB-DM treatment strategies in low and middle-income countries.

Machine Learning in Spine Surgery: A Narrative Review

Artificial intelligence and machine learning (ML) can offer revolutionary advances in their application to the field of spine surgery. Within the past 5 years, novel applications of ML have assisted in surgical decision-making, intraoperative imaging and navigation, and optimization of clinical outcomes. ML has the capacity to address many different clinical needs and improve diagnostic and surgical techniques. This review will discuss current applications of ML in the context of spine surgery by breaking down its implementation preoperatively, intraoperatively, and postoperatively. Ethical considerations to ML and challenges in ML implementation must be addressed to maximally benefit patients, spine surgeons, and the healthcare system. Areas for future research in augmented reality and mixed reality, along with limitations in generalizability and bias, will also be highlighted.

Artificial intelligence for the detection of sacroiliitis on magnetic resonance imaging in patients with axial spondyloarthritis

Background

Magnetic 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.

Methods

This 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.

Results

A 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.

Conclusion

An 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.

Research progress on deep learning in magnetic resonance imaging-based diagnosis and treatment of prostate cancer: a review on the current status and perspectives

Multiparametric magnetic resonance imaging (mpMRI) has emerged as a first-line screening and diagnostic tool for prostate cancer, aiding in treatment selection and noninvasive radiotherapy guidance. However, the manual interpretation of MRI data is challenging and time-consuming, which may impact sensitivity and specificity. With recent technological advances, artificial intelligence (AI) in the form of computer-aided diagnosis (CAD) based on MRI data has been applied to prostate cancer diagnosis and treatment. Among AI techniques, deep learning involving convolutional neural networks contributes to detection, segmentation, scoring, grading, and prognostic evaluation of prostate cancer. CAD systems have automatic operation, rapid processing, and accuracy, incorporating multiple sequences of multiparametric MRI data of the prostate gland into the deep learning model. Thus, they have become a research direction of great interest, especially in smart healthcare. This review highlights the current progress of deep learning technology in MRI-based diagnosis and treatment of prostate cancer. The key elements of deep learning-based MRI image processing in CAD systems and radiotherapy of prostate cancer are briefly described, making it understandable not only for radiologists but also for general physicians without specialized imaging interpretation training. Deep learning technology enables lesion identification, detection, and segmentation, grading and scoring of prostate cancer, and prediction of postoperative recurrence and prognostic outcomes. The diagnostic accuracy of deep learning can be improved by optimizing models and algorithms, expanding medical database resources, and combining multi-omics data and comprehensive analysis of various morphological data. Deep learning has the potential to become the key diagnostic method in prostate cancer diagnosis and treatment in the future.

The automatic evaluation of steno-occlusive changes in time-of-flight magnetic resonance angiography of moyamoya patients using a 3D coordinate attention residual network

Background

Moyamoya disease (MMD) is a rare cerebrovascular occlusive disease with progressive stenosis of the terminal portion of internal cerebral artery (ICA) and its main branches, which can cause complications, such as high risks of disability and increased mortality. Accurate and timely diagnosis may be difficult for physicians who are unfamiliar to MMD. Therefore, this study aims to achieve a preoperative deep-learning-based evaluation of MMD by detecting steno-occlusive changes in the middle cerebral artery or distal ICA areas.

Methods

A fine-tuned deep learning model was developed using a three-dimensional (3D) coordinate attention residual network (3D CA-ResNet). This study enrolled 50 preoperative patients with MMD and 50 controls, and the corresponding time of flight magnetic resonance angiography (TOF-MRA) imaging data were acquired. The 3D CA-ResNet was trained based on sub-volumes and tested using patch-based and subject-based methods. The performance of the 3D CA-ResNet, as evaluated by the area under the curve (AUC) of receiving-operator characteristic, was compared with that of three other conventional 3D networks.

Results

With the resulting network, the patch-based test achieved an AUC value of 0.94 for the 3D CA-ResNet in 480 patches from 10 test patients and 10 test controls, which is significantly higher than the results of the others. The 3D CA-ResNet correctly classified the MMD patients and normal healthy controls, and the vascular lesion distribution in subjects with the disease was investigated by generating a stenosis probability map and 3D vascular structure segmentation.

Conclusions

The results demonstrated the reliability of the proposed 3D CA-ResNet in detecting stenotic areas on TOF-MRA imaging, and it outperformed three other models in identifying vascular steno-occlusive changes in patients with MMD.

A predictive model for early clinical diagnosis of spinal tuberculosis based on conventional laboratory indices: A multicenter real-world study

Background

Early diagnosis of spinal tuberculosis (STB) remains challenging. The aim of this study was to develop a predictive model for the early diagnosis of STB based on conventional laboratory indicators.

Method

The clinical data of patients with suspected STB in four hospitals were included, and variables were screened by Lasso regression. Eighty-five percent of the cases in the dataset were randomly selected as the training set, and the other 15% were selected as the validation set. The diagnostic prediction model was established by logistic regression in the training set, and the nomogram was drawn. The diagnostic performance of the model was verified in the validation set.

Result

A total of 206 patients were included in the study, including 105 patients with STB and 101 patients with NSTB. Twelve variables were screened by Lasso regression and modeled by logistic regression, and seven variables (TB.antibody, IGRAs, RBC, Mono%, RDW, AST, BUN) were finally included in the model. AUC of 0.9468 and 0.9188 in the training and validation cohort, respectively.

Conclusion

In this study, we developed a prediction model for the early diagnosis of STB which consisted of seven routine laboratory indicators.