Radiographic findings involved in knee osteoarthritis progression are associated with pain symptom frequency and baseline disease severity: a population-level analysis using deep learning

Survival analysis on subchondral bone length for total knee replacement

OBJECTIVE

Use subchondral bone length (SBL), a new MRI-derived measure that reflects the extent of cartilage loss and bone flattening, to predict the risk of progression to total knee replacement (TKR).

METHODS

We employed baseline MRI data from the Osteoarthritis Initiative (OAI), focusing on 760 men and 1214 women with bone marrow lesions (BMLs) and joint space narrowing (JSN) scores, to predict the progression to TKR. To minimize bias from analyzing both knees of a participant, only the knee with a higher Kellgren-Lawrence (KL) grade was considered, given its greater potential need for TKR. We utilized the Kaplan-Meier survival curves and Cox proportional hazards models, incorporating raw and normalized values of SBL, JSN, and BML as predictors. The study included subgroup analyses for different demographics and clinical characteristics, using models for raw and normalized SBL (merged, femoral, tibial), BML (merged, femoral, tibial), and JSN (medial and lateral compartments). Model performance was evaluated using the time-dependent area under the curve (AUC), Brier score, and Concordance index to gauge accuracy, calibration, and discriminatory power. Knee joint and region-level analyses were conducted to determine the effectiveness of SBL, JSN, and BML in predicting TKR risk.

RESULTS

The SBL model, incorporating data from both the femur and tibia, demonstrated a predictive capacity for TKR that closely matched the performance of the BML score and the JSN grade. The Concordance index of the SBL model was 0.764, closely mirroring the BML's 0.759 and slightly below JSN's 0.788. The Brier score for the SBL model stood at 0.069, showing comparability with BML's 0.073 and a minor difference from JSN's 0.067. Regarding the AUC, the SBL model achieved 0.803, nearly identical to BML's 0.802 and slightly lower than JSN's 0.827.

CONCLUSION

SBL's capacity to predict the risk of progression to TKR highlights its potential as an effective imaging biomarker for knee osteoarthritis.

Achieving high accuracy in meniscus tear detection using advanced deep learning models with a relatively small data set

PURPOSE

This study aims to evaluate the effectiveness of advanced deep learning models, specifically YOLOv8 and EfficientNetV2, in detecting meniscal tears on magnetic resonance imaging (MRI) using a relatively small data set.

METHOD

Our data set consisted of MRI studies from 642 knees-two orthopaedic surgeons labelled and annotated the MR images. The training pipeline included MRI scans of these knees. It was divided into two stages: initially, a deep learning algorithm called YOLO was employed to identify the meniscus location, and subsequently, the EfficientNetV2 deep learning architecture was utilized to detect meniscal tears. A concise report indicating the location and detection of a torn meniscus is provided at the end.

RESULT

The YOLOv8 model achieved mean average precision at 50% threshold (mAP@50) scores of 0.98 in the sagittal view and 0.985 in the coronal view. Similarly, the EfficientNetV2 model obtained area under the curve scores of 0.97 and 0.98 in the sagittal and coronal views, respectively. These outstanding results demonstrate exceptional performance in meniscus localization and tear detection.

CONCLUSION

Despite a relatively small data set, state-of-the-art models like YOLOv8 and EfficientNetV2 yielded promising results. This artificial intelligence system enhances meniscal injury diagnosis by generating instant structured reports, facilitating faster image interpretation and reducing physician workload.

LEVEL OF EVIDENCE

Level III.

Hip and pelvic geometry as predictors of knee osteoarthritis severity

Malalignment is one of the most critical risk factors for knee osteoarthritis (KOA). Biomechanical factors such as knee varus or valgus, hip-knee-ankle angle, and femoral anteversion affect KOA severity. In this study, we aimed to investigate KOA severity predictive factors based on hip and pelvic radiographic geometry. In this cross-sectional study, 125 patients with idiopathic KOA were enrolled. Two investigators evaluated the knee and pelvic radiographs of 125 patients, and 16 radiological parameters were measured separately. KOA severity was categorized based on the medial tibiofemoral joint space widths (JSW). Based on JSW measurements, 16% (n = 40), 8.8% (n = 22), 16.4% (n = 41), and 56.8% (n = 147) were defined as grades 0, 1, 2, 3, respectively. There were significant differences between the JSW groups with respect to hip axis length, femoral neck-axis length, acetabular width, neck shaft angle (NSA), outer pelvic diameter, midpelvis-caput distance, acetabular-acetabular distance, and femoral head to femoral head length (P < .05). Two different functions were obtained using machine learning classification and logistic regression, and the accuracy of predicting was 74.4% by using 1 and 89.6% by using both functions. Our findings revealed that some hip and pelvic geometry measurements could affect the severity of KOA. Furthermore, logistic functions using predictive factors of hip and pelvic geometry can predict the severity of KOA with acceptable accuracy, and it could be used in clinical decisions.

Deep-Learning Automation of Preoperative Radiographic Parameters Associated With Early Periprosthetic Femur Fracture After Total Hip Arthroplasty

BACKGROUND

The radiographic assessment of bone morphology impacts implant selection and fixation type in total hip arthroplasty (THA) and is important to minimize the risk of periprosthetic femur fracture (PFF). We utilized a deep-learning algorithm to automate femoral radiographic parameters and determined which automated parameters were associated with early PFF.

METHODS

Radiographs from a publicly available database and from patients undergoing primary cementless THA at a high-volume institution (2016 to 2020) were obtained. A U-Net algorithm was trained to segment femoral landmarks for bone morphology parameter automation. Automated parameters were compared against that of a fellowship-trained surgeon and compared in an independent cohort of 100 patients who underwent THA (50 with early PFF and 50 controls matched by femoral component, age, sex, body mass index, and surgical approach).

RESULTS

On the independent cohort, the algorithm generated 1,710 unique measurements for 95 images (5% lesser trochanter identification failure) in 22 minutes. Medullary canal width, femoral cortex width, canal flare index, morphological cortical index, canal bone ratio, and canal calcar ratio had good-to-excellent correlation with surgeon measurements (Pearson's correlation coefficient: 0.76 to 0.96). Canal calcar ratios (0.43 ± 0.08 versus 0.40 ± 0.07) and canal bone ratios (0.39 ± 0.06 versus 0.36 ± 0.06) were higher (P < .05) in the PFF cohort when comparing the automated parameters.

CONCLUSIONS

Deep-learning automated parameters demonstrated differences in patients who had and did not have early PFF after cementless primary THA. This algorithm has the potential to complement and improve patient-specific PFF risk-prediction tools.

Medical Imaging Applications Developed Using Artificial Intelligence Demonstrate High Internal Validity Yet Are Limited in Scope and Lack External Validation

PURPOSE

To (1) review definitions and concepts necessary to interpret applications of deep learning (DL; a domain of artificial intelligence that leverages neural networks to make predictions on media inputs such as images) and (2) identify knowledge and translational gaps in the literature to provide insight into specific areas for improvement as adoption of this technology continues.

METHODS

A comprehensive search of the literature was performed in December 2023 for articles regarding the use of DL in sports medicine. For each study, information regarding the joint of focus, specific anatomic structure/pathology to which DL was applied, imaging modality utilized, source of images used for model training and testing, data set size, model performance, and whether the DL model was externally validated was recorded. A numerical scale was used to rate each DL model's clinical impact, with 1 corresponding to proof-of-concept studies with little to no direct clinical impact and 5 corresponding to practice-changing clinical impact and ready for clinical deployment.

RESULTS

Fifty-five studies were identified, all of which were published within the past 5 years, while 82% were published within the past 3 years. Of the DL models identified, 84% were developed for classification tasks, 9% for automated measurements, and 7% for segmentation. A total of 62% of studies utilized magnetic resonance imaging as the imaging modality, 25% radiographs, and 7% ultrasound, while 1 study each used computed tomography, arthroscopic images, or arthroscopic video. Sixty-five percent of studies focused on the detection of tears (anterior cruciate ligament [ACL], rotator cuff [RC], and meniscus). The diagnostic performance of ACL tears, as determined by the area under the receiver operator curve (AUROC), ranged from 0.81 to 0.99 for ACL tears (excellent to near perfect), 0.83 to 0.94 for RC tears (excellent), and from 0.75 to 0.96 for meniscus tears (acceptable to excellent). In addition, 3 studies focused on detection of cartilage lesions had AUROC ranging from 0.90 to 0.92 (excellent performance). However, only 4 (7%) studies externally validated their models, suggesting that they may not be generalizable or may not perform well when applied to populations other than that used to develop the model. Finally, the mean clinical impact score was 2 (range, 1-3) on scale of 1 to 5, corresponding to limited clinical applicability.

CONCLUSIONS

DL models in orthopaedic sports medicine show generally excellent performance (high internal validity) but require external validation to facilitate clinical deployment. In addition, current models have low clinical applicability and fail to advance the field due to a focus on routine tasks and a narrow conceptual framework.

LEVEL OF EVIDENCE

Level IV, scoping review of Level I to IV studies.

Combination of external fixation using digital six-axis fixator and internal fixation to treat severe complex knee deformity

BACKGROUND

Severe knee valgus/varus or complex multiplanar deformities are common in clinic. If not corrected in time, cartilage wear will be aggravated and initiate the osteoarthritis due to lower limb malalignment. Internal fixation is unable to correct severe complex deformities, especially when combined with lower limb discrepancy (LLD). Based on the self-designed digital six-axis external fixator Q spatial fixator (QSF), which can correct complex multiplanar deformities without changing structures, accuracy of correction can be improved significantly.

METHODS

This retrospective study included 24 patients who suffered from complex knee deformity with LLD treated by QSF and internal fixation at our institution from January 2018 to February 2021. All patients had a closing wedge distal femoral osteotomy with internal fixation for immediate correction and high tibia osteotomy with QSF fixation for postoperative progressive correction. Data of correction prescriptions were computed by software from postoperative CT scans.

RESULTS

Mean discrepancy length of operative side was 2.39 ± 1.04 cm (range 0.9-4.4 cm) preoperatively. The mean difference of lower limb was 0.32 ± 0.13 cm (range 0.11-0.58 cm) postoperatively. The length of limb correction had significant difference (p < 0.05). The mean MAD and HKA decreased significantly (p < 0.05), and the mean MPTA and LDFA increased significantly (p < 0.05). There were significant increase (p < 0.05) in the AKSS-O, AKSS-F and Tegner Activity Score. The lower limb alignment was corrected (p < 0.05). The mean time of removing external fixator was 112.8 ± 17.9 days (range 83-147 days).

CONCLUSIONS

Complex knee deformity with LLD can be treated by six-axis external fixator with internal fixation without total knee arthroplasty. Lower limb malalignment and discrepancy can be corrected precisely and effectively by this approach.