Artificial Intelligence in Spinal Imaging and Patient Care: A Review of Recent Advances.

Artificial intelligence (AI) is transforming spinal imaging and patient care through automated analysis and enhanced decision-making. This review presents a clinical task-based evaluation, highlighting the specific impact of AI techniques on different aspects of spinal imaging and patient care. We first discuss how AI can potentially improve image quality through techniques like denoising or artifact reduction. We then explore how AI enables efficient quantification of anatomical measurements, spinal curvature parameters, vertebral segmentation, and disc grading. This facilitates objective, accurate interpretation and diagnosis. AI models now reliably detect key spinal pathologies, achieving expert-level performance in tasks like identifying fractures, stenosis, infections, and tumors. Beyond diagnosis, AI also assists surgical planning via synthetic computed tomography generation, augmented reality systems, and robotic guidance. Furthermore, AI image analysis combined with clinical data enables personalized predictions to guide treatment decisions, such as forecasting spine surgery outcomes. However, challenges still need to be addressed in implementing AI clinically, including model interpretability, generalizability, and data limitations. Multicenter collaboration using large, diverse datasets is critical to advance the field further. While adoption barriers persist, AI presents a transformative opportunity to revolutionize spinal imaging workflows, empowering clinicians to translate data into actionable insights for improved patient care.

Negative Screening AI in Ankle Stress Radiography to Reduce Workload.

RATIONALE AND OBJECTIVES: Interpreting ankle stress radiographs is subjective and time-consuming. We aimed to train an AI model that efficiently screens negative cases, assess the agreement with expert with and without AI-assistance, and compare the workload reduction. MATERIAL AND METHODS: We collected anterior draw test (ADT) and talar tilt test (TTT) ankle stress radiographs from Seoul St. Mary's Hospital and St. Vincent's Hospital. Patients with prior surgery, severe joint fusion, or incomplete images were excluded. Expert measurements of tibio-talar distance (TTD) and tibio-talar angle (TTA) served as reference, defining positive labels as TTD ≥ 8.3 mm or TTA ≥ 6.2°. We trained a VGG16 model on data from hospital A and tested it on three separate test sets (testset1, 2 from St. Mary's Hospital, and testset3 from St. Vincent's Hospital). Three readers (expert, reader2, and the collective reading reports) evaluated the test sets, with and without AI-assistance (focusing only on AI-predicted positive cases). We measured agreement with the expert using Cohen's weighted Kappa and assessed the hypothetical workload reduction. RESULTS: AI-assistance did not significantly affect agreement with the expert for any reader in all test sets. Reader2 showed moderate-substantial agreement for all test sets, while collective reports reached fair agreement. The AI alone demonstrated fair to moderate agreement with the expert. AI-assistance reduced the hypothetical workload by 68.8-89.2% for ADT and 58.3-70.4% for TTT. CONCLUSION: We successfully trained an AI model for ankle stress radiography, achieving an average of 70% workload reduction while maintaining agreement with expert radiologists.

Development and Validation of a Deep-Learning-Based Algorithm for Detecting and Classifying Metallic Implants in Abdominal and Spinal CT Topograms.

PURPOSE: To develop and validate a deep-learning-based algorithm (DLA) that is designed to segment and classify metallic objects in topograms of abdominal and spinal CT. METHODS: DLA training for implant segmentation and classification was based on a U-net-like architecture with 263 annotated hip implant topograms and 2127 annotated spine implant topograms. The trained DLA was validated with internal and external datasets. Two radiologists independently reviewed the external dataset consisting of 2178 abdomen anteroposterior (AP) topograms and 515 spine AP and lateral topograms, all collected in a consecutive manner. Sensitivity and specificity were calculated per pixel row and per patient. Pairwise intersection over union (IoU) was also calculated between the DLA and the two radiologists. RESULTS: The performance parameters of the DLA were consistently >95% in internal validation per pixel row and per patient. DLA can save 27.4% of reconstruction time on average in patients with metallic implants compared to the existing iMAR. The sensitivity and specificity of the DLA during external validation were greater than 90% for the detection of spine implants on three different topograms and for the detection of hip implants on abdominal AP and spinal AP topograms. The IoU was greater than 0.9 between the DLA and the radiologists. However, the DLA training could not be performed for hip implants on spine lateral topograms. CONCLUSIONS: A prototype DLA to detect metallic implants of the spine and hip on abdominal and spinal CT topograms improves the scan workflow with good performance for both spine and hip implants.

Comparative analysis of image quality and interchangeability between standard and deep learning-reconstructed T2-weighted spine MRI.

RATIONALE AND OBJECTIVES: MRI reconstruction of undersampled data using a deep learning (DL) network has been recently performed as part of accelerated imaging. Herein, we compared DL-reconstructed T2-weighted image (T2-WI) to conventional T2-WI regarding image quality and degenerative lesion detection. MATERIALS AND METHODS: Sixty-two patients underwent C-spine (n = 27) or L-spine (n = 35) MRIs, including conventional and DL-reconstructed T2-WI. Image quality was assessed with non-uniformity measurement and 4-scale grading of structural visibility. Three readers (R1, R2, R3) independently assessed the presence and types of degenerative lesions. Student t-test was used to compare non-uniformity measurements. Interprotocol and interobserver agreement of structural visibility was analyzed with Wilcoxon signed-rank test and weighted-κ values, respectively. The diagnostic equivalence of degenerative lesion detection between two protocols was assessed with interchangeability test. RESULTS: The acquisition time of DL-reconstructed images was reduced to about 21-58% compared to conventional images. Non-uniformity measurement was insignificantly different between the two images (p-value = 0.17). All readers rated DL-reconstructed images as showing the same or superior structural visibility compared to conventional images. Significantly improved visibility was observed at disk margin of C-spine (R1, p < 0.001; R2, p = 0.04) and dorsal root ganglia (R1, p = 0.03; R3, p = 0.02) and facet joint (R1, p = 0.04; R2, p < 0.001; R3, p = 0.03) of L-spine. Interobserver agreements of image quality were variable in each structure. Clinical interchangeability between two protocols for degenerative lesion detection was verified showing <5% in the upper bounds of 95% confidence intervals of agreement rate differences. CONCLUSIONS: DL-reconstructed T2-WI demonstrates comparable image quality and diagnostic performance with conventional T2-WI in spine imaging, with reduced acquisition time.

Deep learning-based k-space-to-image reconstruction and super resolution for diffusion-weighted imaging in whole-spine MRI.

PURPOSE: To assess the feasibility of deep learning (DL)-based k-space-to-image reconstruction and super resolution for whole-spine diffusion-weighted imaging (DWI). METHOD: This retrospective study included 97 consecutive patients with hematologic and/or oncologic diseases who underwent DL-processed whole-spine MRI from July 2022 to March 2023. For each patient, conventional (CONV) axial single-shot echo-planar DWI (b = 50, 800 s/mm2) was performed, followed by DL reconstruction and super resolution processing. The presence of malignant lesions and qualitative (overall image quality and diagnostic confidence) and quantitative (nonuniformity [NU], lesion contrast, signal-to-noise ratio [SNR], contrast-to-noise ratio [CNR], and ADC values) parameters were assessed for DL and CONV DWI. RESULTS: Ultimately, 67 patients (mean age, 63.0 years; 35 females) were analyzed. The proportions of vertebrae with malignant lesions for both protocols were not significantly different (P: [0.55-0.99]). The overall image quality and diagnostic confidence scores were higher for DL DWI (all P ≤ 0.002) than CONV DWI. The NU, lesion contrast, SNR, and CNR of each vertebral segment (P ≤ 0.04) but not the NU of the sacral segment (P = 0.51) showed significant differences between protocols. For DL DWI, the NU was lower, and lesion contrast, SNR, and CNR were higher than those of CONV DWI (median values of all segments; 19.8 vs. 22.2, 5.4 vs. 4.3, 7.3 vs. 5.5, and 0.8 vs. 0.7). Mean ADC values of the lesions did not significantly differ between the protocols (P: [0.16-0.89]). CONCLUSIONS: DL reconstruction can improve the image quality of whole-spine diffusion imaging.

Imaging features and clinical significance of atypical pleural plaques mimicking bone tumors: a retrospective case series.

This study aimed to examine the imaging characteristics and clinical implications of atypical pleural lesions that mimic bone tumors and form along the inner margins of consecutive ribs. This retrospective analysis included 45 atypical pleural lesions arising from 13 patients who underwent chest computed tomography (CT) between April 2021 and March 2023. The clinical features, CT findings, and radiologic diagnoses prior to pathologic identification were examined. Pathological findings were reviewed in the surgically resected case. Subgroup analysis was performed based on the presence of concurrent typical pleural plaques. The mean age of the patients was 69.3±8.4 years with a predominance of males (76.9%). The lesions primarily exhibited unilateral involvement (84.6%), being most frequently located in the right mid-level posterior region. Calcification was present in 75.6% of cases, typically seen continuously along the ribs (82.4%). Adjacent rib changes were observed in 28.9% of cases. These lesions were frequently misdiagnosed as osteochondromas or bony spurs (55.6%) by thoracic radiologists. No significant growth was observed during follow-up (n=11, 47±41 months), and the pathological findings were consistent with pleural plaques. Patients with concurrent typical pleural plaques had more atypical pleural lesions without statistical significance (P=0.071) and showed a more even distribution (P=0.039). In conclusion, atypical pleural lesions resembling bone tumors along consecutive ribs represent a distinct subset of pleural plaques. Their unique distribution and morphology should be recognized by radiologists to avoid misinterpretation and unnecessary interventions.