Semi-automatic proximal humeral trabecular bone density assessment tool: technique application and clinical validation.

PURPOSE: This study aimed to apply a newly developed semi-automatic phantom-less QCT (PL-QCT) to measure proximal humerus trabecular bone density based on chest CT and verify its accuracy and precision. METHODS: Subcutaneous fat of the shoulder joint and trapezius muscle were used as calibration references for PL-QCT BMD measurement. A self-developed algorithm based on a convolution map was utilized in PL-QCT for semi-automatic BMD measurements. CT values of ROIs used in PL-QCT measurements were directly used for phantom-based quantitative computed tomography (PB-QCT) BMD assessment. The study included 376 proximal humerus for comparison between PB-QCT and PL-QCT. Two sports medicine doctors measured the proximal humerus with PB-QCT and PL-QCT without knowing each other's results. Among them, 100 proximal humerus were included in the inter-operative and intra-operative BMD measurements for evaluating the repeatability and reproducibility of PL-QCT and PB-QCT. RESULTS: A total of 188 patients with 376 shoulders were involved in this study. The consistency analysis indicated that the average bias between proximal humerus BMDs measured by PB-QCT and PL-QCT was 1.0 mg/cc (agreement range - 9.4 to 11.4; P > 0.05, no significant difference). Regression analysis between PB-QCT and PL-QCT indicated a good correlation (R-square is 0.9723). Short-term repeatability and reproducibility of proximal humerus BMDs measured by PB-QCT (CV: 5.10% and 3.41%) were slightly better than those of PL-QCT (CV: 6.17% and 5.64%). CONCLUSIONS: We evaluated the bone quality of the proximal humeral using chest CT through the semi-automatic PL-QCT system for the first time. Comparison between it and PB-QCT indicated that it could be a reliable shoulder BMD assessment tool with acceptable accuracy and precision. This study developed and verify a semi-automatic PL-QCT for assessment of proximal humeral bone density based on CT to assist in the assessment of proximal humeral osteoporosis and development of individualized treatment plans for shoulders.

A Deep-Learning Model for Diagnosing Fresh Vertebral Fractures on Magnetic Resonance Images.

BACKGROUND: The accurate diagnosis of fresh vertebral fractures (VFs) was critical to optimizing treatment outcomes. Existing studies, however, demonstrated insufficient accuracy, sensitivity, and specificity in detecting fresh fractures using magnetic resonance imaging (MRI), and fall short in localizing the fracture sites. METHODS: This prospective study comprised 716 patients with fresh VFs. We obtained 849 Short TI Inversion Recovery (STIR) image slices for training and validation of the AI model. The AI models employed were yolov7 and resnet50, to detect fresh VFs. RESULTS: The AI model demonstrated a diagnostic accuracy of 97.6% for fresh VFs, with a sensitivity of 98% and a specificity of 97%. The performance of the model displayed a high degree of consistency when compared to the evaluations by spine surgeons. In the external testing dataset, the model exhibited a classification accuracy of 92.4%, a sensitivity of 93%, and a specificity of 92%. CONCLUSIONS: Our findings highlighted the potential of AI in diagnosing fresh VFs, offering an accurate and efficient way to aid physicians with diagnosis and treatment decisions.

Cortical Endplate Bone Density Measured by Novel Phantomless Quantitative Computed Tomography May Predict Cage Subsidence more Conveniently and Accurately.

OBJECTIVE: Previous studies have shown that bone mineral density (BMD) is a predictor of cage subsidence. Phantom-less quantitative computed tomography (PL-QCT) can measure volumetric bone mineral density (vBMD) of lumbar trabecular and cortical bone. The study of endplate vBMD (EP-vBMD) is important in predicting cage settlement after extreme lateral interbody fusion (XLIF). This study aimed to determine the risk factors for postoperative cage subsidence after XLIF, particularly focusing on the relationship between vBMD measured by automatic PL-QCT and cage subsidence. METHODS: Patients who underwent XLIF surgery from January 2018 to October 2020 with a minimum of 6 months of follow-up were retrospectively included. Cage subsidence was defined as >2 mm cage sinking on the adjacent endplate in follow-up imaging evaluation. Outcome measures were localized vBMDs included EP-vBMDs with different region of interest (ROI) heights measured by PL-QCT based on a customized muscle-fat algorithm. Shapiro-Wilk test, one-way ANOVA, Mann-Whitney test, Fisher exact test, univariable and multivariable logistic regression and receiver operating characteristic (ROC) curve analysis were executed in this study. RESULTS: One hundred and thirteen levels of 78 patients were included in the analysis. The mean age was 65 ± 7.9 years for 11 males and 67 females. Cage subsidence occurred on 45 (39.8%) surgical levels. There was no significant difference in demographics, fused levels, or preoperative radiographic parameters. 1.25-mm EP-vBMD (0.991 [0.985,0.997], p = 0.004) and P-TB-vBMD (cage-positioned trabecular volumetric bone mineral density) (0.988 [0.977-0.999], p = 0.026) were cage-subsidence relevant according to univariate analysis. Low 1.25-mm EP-vBMD (0.992 [0.985, 0.999], p = 0.029) was an independent risk factor according to multifactorial analysis. CONCLUSION: Preoperative low EP-vBMD was an independent risk factor for postoperative cage subsidence after XLIF. EP-vBMD measured by most cortex-occupied ROI may be the optimal vBMD parameter for cage subsidence prediction.

Performance evaluation of an AI-based preoperative planning software application for automatic selection of pedicle screws based on computed tomography images.

Introduction: Recent neurosurgical applications based on artificial intelligence (AI) have demonstrated its potential in surgical planning and anatomical measurement. We aimed to evaluate the performance of an AI planning software application on screw length/diameter selection and insertion accuracy in comparison with freehand surgery. Methods: A total of 45 patients with 208 pedicle screw placements on thoracolumbar segments were included in this analysis. The novel AI planning software was developed based on a deep learning model. AI-based pedicle screw placements were selected on the basis of preoperative computed tomography (CT) data, and freehand surgery screw placements were observed based on postoperative CT data. The performance of AI pedicle screw placements was evaluated on the components of screw length, diameter, and Gertzbein grade in comparison with the results achieved by freehand surgery. Results: Among 208 pedicle screw placements, the average screw length/diameters selected by the AI model and used in freehand surgery were 48.65 ± 5.99 mm/7.39 ± 0.42 mm and 44.78 ± 2.99 mm/6.1 ± 0.27 mm, respectively. Among AI screw placements, 85.1% were classified as Gertzbein Grade A (no cortical pedicle breach); among free-hand surgery placements, 64.9% were classified as Gertzbein Grade A. Conclusion: The novel AI planning software application could provide an accessible and safe pedicle screw placement strategy in comparison with traditional freehand pedicle screw placement strategies. The choices of pedicle screw dimensional parameters made by the model, including length and diameter, may provide potential inspiration for real clinical discretion.

Endplate volumetric bone mineral density biomechanically matched interbody cage.

Disc degenerative problems affect the aging population, globally, and interbody fusion is a crucial surgical treatment. The interbody cage is the critical implant in interbody fusion surgery; however, its subsidence risk becomes a remarkable clinical complication. Cage subsidence is caused due to a mismatch of material properties between the bone and implant, specifically, the higher elastic modulus of the cage relative to that of the spinal segments, inducing subsidence. Our recent observation has demonstrated that endplate volumetric bone mineral density (EP-vBMD) measured through the greatest cortex-occupied 1.25-mm height region of interest, using automatic phantomless quantitative computed tomography scanning, could be an independent cage subsidence predictor and a tool for cage selection instruction. Porous design on the metallic cage is a trend in interbody fusion devices as it provides a solution to the subsidence problem. Moreover, the superior osseointegration effect of the metallic cage, like the titanium alloy cage, is retained. Patient-specific customization of porous metallic cages based on the greatest subsidence-related EP-vBMD may be a good modification for the cage design as it can achieve biomechanical matching with the contacting bone tissue. We proposed a novel perspective on porous metallic cages by customizing the elastic modulus of porous metallic cages by modifying its porosity according to endplate elastic modulus calculated from EP-vBMD. A three-grade porosity customization strategy was introduced, and direct porosity-modulus customization was also available depending on the patient's or doctor's discretion.