Background: Accurate preoperative planning for total knee arthroplasty (TKA) is crucial. Computed tomography (CT)-based preoperative planning offers more comprehensive information and can also be used to design patient-specific instrumentation (PSI), but it requires well-reconstructed and segmented images, and the process is complex and time-consuming. This study aimed to develop an artificial intelligence (AI) preoperative planning and PSI system for TKA and to validate its time savings and accuracy in clinical applications. Methods: The 3D-UNet and modified HRNet neural network structures were used to develop the AI preoperative planning and PSI system (AIJOINT). Forty-two patients who were scheduled for TKA underwent both AI and manual CT processing and planning for component sizing, 20 of whom had their PSIs designed and applied intraoperatively. The time consumed and the size and orientation of the postoperative component were recorded. Results: The Dice similarity coefficient (DSC) and loss function indicated excellent performance of the neural network structure in CT image segmentation. AIJOINT was faster than conventional methods for CT segmentation (3.74 ± 0.82 vs. 128.88 ± 17.31 min, p < 0.05) and PSI design (35.10 ± 3.98 vs. 159.52 ± 17.14 min, p < 0.05) without increasing the time for size planning. The accuracy of AIJOINT in planning the size of both femoral and tibial components was 92.9%, while the accuracy of the conventional method in planning the size of the femoral and tibial components was 42.9% and 47.6%, respectively (p < 0.05). In addition, AI-based PSI improved the accuracy of the hip–knee–ankle angle and reduced postoperative blood loss (p < 0.05). Conclusion: AIJOINT significantly reduces the time needed for CT processing and PSI design without increasing the time for size planning, accurately predicts the component size, and improves the accuracy of lower limb alignment in TKA patients, providing a meaningful supplement to the application of AI in orthopaedics.

Background: Lumbar spondylolisthesis (LS) is a common spinal disorder characterized by the forward displacement of the vertebra. Early detection is challenging due to asymptomatic presentation in the early stages. This study develops and validates an AI-based deep learning model for the early, high-precision diagnosis of LS using lumbar X-ray images. Methods: A total of 3300 lateral lumbar X-ray images were collected from Beijing Tongren Hospital, and an external dataset of 1100 images was used for validation. The images were randomly divided into the training, validation, and test sets. The model uses semantic segmentation to precisely segment vertebral bodies and calculate distances between vertebrae to identify and grade LS using the Meyerding classification. Model performance was compared to other algorithms and clinical experts. Results: The model achieved F1 Scores of 0.92 and 0.91 on the hospital and external datasets, respectively, outperforming other methods. It showed diagnostic accuracies of 96.1% and 94.4%, exceeding the performance of physicians (90.6% and 89.3%). These results highlight the potential of AI in improving diagnostic accuracy and clinical decision-making. Conclusions: Our deep learning model demonstrates high accuracy and reliability in diagnosing LS, providing a valuable tool for early detection and better patient outcomes. Future work will involve expanding the dataset and validating the model in clinical settings.

Background: The goals of this study were to develop an artificial intelligence (AI)-driven automated preoperative planning system for anterior cruciate ligament (ACL) reconstruction by integrating deep learning with computed tomography (CT)-magnetic resonance imaging (MRI) image fusion and segmentation, and to evaluate its accuracy. Methods: Structures on CT and MRI scans of 200 knee joints from patients with an intact ACL (aged 18 to 50 years, 81.0% male, all ethnic Chinese) were manually annotated. Fusion of the CT and MRI images was performed using a Dual-UNet registration architecture incorporating multiscale information fusion, enabling dynamic 3D reconstruction of the fused images for ACL insertion site identification and isometry assessment. A deep-learning framework was trained to analyze the fused image to precisely optimize ACL tunnel positioning, including identifying the entrances and exits of the femoral and tibial tunnels. Criteria in the automated planning included proximity to the ideal point, coverage of the anatomical footprint area, and isometric length variation of <2 mm. The accuracy of the AI system was then validated in 36 ACL reconstructions performed in bone models by comparing the drilled femoral and tibial tunnel lengths and graft length between the tunnels with the planned values. Finally, clinical feasibility was tested in 36 patients undergoing ACL reconstruction surgery using 3D-printed patient-specific guides derived from the AI planning, with 36 conventional surgeries as controls. Deviation of tunnel positions from the planned positions was compared between the 2 groups. Results: CT-MRI image fusion was able to generate an individualized 3D model with high segmentation accuracy (Dice coefficient = 0.864). The AI planning required 192 ± 90.2 seconds per case. In the bone model validation, the mean deviation between the planned and executed values was 0.05). In the clinical testing, the AI-guided group demonstrated significantly smaller deviations from the ideal point compared with the conventional group in the deep-to-shallow (D-S), high-to-low (H-L), medial-to-lateral (M-L), and anterior-to-posterior (A-P) directions (all p < 0.05). Conclusions: The AI-driven segmentation of CT-MRI fusion images and automatic preoperative ACL reconstruction planning demonstrated the capability to automatically, precisely, and reproducibly generate plans for nearly ideal tunnel entry and exit points with isometric, anatomical, and individualization characteristics. This technology is expected to hold clinical potential for ACL reconstruction, including reduced complication and revision rates and enhanced postoperative function.

Objective: To develop and externally validate a dual-mechanism deep learning (DL) model that integrates vertebral segmentation and lesion detection for automated evaluation of lumbar degeneration and structured report generation on plain radiographs.Methods: In this retrospective study, 5,964 patients who underwent standing anteroposterior and lateral lumbar radiographs at a single institution and 600 patients from a public dataset (BUU-Spine) were included. Vertebral corners from T11–L5 (and S1 on lateral views) and 7 degenerative findings (scoliosis, straightened/preserved lordosis, spondylolisthesis, disc space narrowing, osteophytes, vertebral compression, and abdominal aortic calcification) were annotated by 3 spine surgeons. Two independently trained, parallel networks were developed, including a ResNet-based segmentation network and a YOLOv8-based detection network. A rule-based integration strategy reconciled both outputs and generated structured diagnostic reports. Segmentation accuracy, quantitative measurement agreement, diagnostic performance, and clinical acceptability of reports were evaluated.Results: Intra- and interobserver landmark distances within 3 mm reached 96% and >95%, respectively. On the internal test set, the percentage of correct keypoints within 3 mm was 95.7%–98.6%, with intraclass correlation coefficients of 0.84–0.89 and Pearson correlation coefficient (r) of 0.90–0.94 for key radiographic parameters. The segmentation- and detection-based models achieved precision of 92.2%–96.9% and 91.7%–95.5%, and recall of 91.6%–94.8% and 93.3%–95.2%, respectively. Under the dual-positive condition, the integrated model yielded the highest precision (93.8%–97.3%), whereas the any-positive condition achieved the highest recall (94.1%–97.6%). Of 596 automatically generated structured reports, 557 (93.4%) were deemed clinically acceptable.Conclusion: The proposed dual-mechanism DL framework enables accurate, multilesion assessment of lumbar degeneration and generation of clinically acceptable structured reports from plain radiographs, supporting workflow optimization in lumbar spine imaging.