Bone segmentation and 3D reconstruction are crucial for total knee arthroplasty (TKA) surgical planning with Personalized Surgical Instruments (PSIs). Traditional semi-automatic approaches are time-consuming and operator-dependent, although they provide reliable outcomes. Moreover, the recent expansion of artificial intelligence (AI) tools towards various medical domains is transforming modern healthcare. Accordingly, this study introduces an automated AI-based pipeline to replace the current operator-based tibia and femur 3D reconstruction procedure enhancing TKA preoperative planning. Leveraging an 822 CT image dataset, a novel patch-based method and an improved segmentation label generation algorithm were coupled to a Combined Edge Loss UNet (CEL-UNet), a novel CNN architecture featuring an additional decoding branch to boost the bone boundary segmentation. Root Mean Squared Errors and Hausdorff distances compared the predicted surfaces to the reference bones showing median and interquartile values of 0.26 (0.19–0.36) mm and 0.24 (0.18–0.32) mm, and of 1.06 (0.73–2.15) mm and 1.43 (0.82–2.86) mm for the tibia and femur, respectively, outperforming previous results of our group, state-of-the-art, and UNet models. A feasibility analysis for a PSI-based surgical plan revealed sub-millimetric distance errors and sub-angular alignment uncertainties in the PSI contact areas and the two cutting planes. Finally, operational environment testing underscored the pipeline’s efficiency. More than half of the processed cases complied with the PSI prototyping requirements, reducing the overall time from 35 min to 13.1 s, while the remaining ones underwent a manual refinement step to achieve such PSI requirements, performing the procedure four to eleven times faster than the manufacturer standards. To conclude, this research advocates the need for real-world applicability and optimization of AI solutions in orthopedic surgical practice.

Objective: Recent advancements in augmented reality led to planning and navigation systems for orthopedic surgery. However little is known about mixed reality (MR) in orthopedics. Furthermore, artificial intelligence (AI) has the potential to boost the capabilities of MR by enabling automation and personalization. The purpose of this work is to assess Holoknee prototype, based on AI and MR for multimodal data visualization and surgical planning in knee osteotomy, developed to run on the HoloLens 2 headset. Methods: Two preclinical test sessions were performed with 11 participants (eight surgeons, two residents, and one medical student) executing three times six tasks, corresponding to a number of holographic data interactions and preoperative planning steps. At the end of each session, participants answered a questionnaire on user perception and usability. Results: During the second trial, the participants were faster in all tasks than in the first one, while in the third one, the time of execution decreased only for two tasks (“Patient selection” and “Scrolling through radiograph”) with respect to the second attempt, but without statistically significant difference (respectively p =0.14 and p =0.13, p <0.05). All subjects strongly agreed that MR can be used effectively for surgical training, whereas 10 (90.9%) strongly agreed that it can be used effectively for preoperative planning. Six (54.5%) agreed and two of them (18.2%) strongly agreed that it can be used effectively for intraoperative guidance. Discussion/Conclusion: In this work, we presented Holoknee, the first holistic application of AI and MR for surgical planning for knee osteotomy. It reported promising results on its potential translation to surgical training, preoperative planning, and surgical guidance.

Bone metastasis (BM) is a leading cause of morbidity and mortality in patients with prostate and renal cancer. The complex and dynamic biological processes driving its progression present significant challenges for both understanding and treating this disease. Although current in vivo research provides valuable insights, it is often limited by the inability to fully capture the intricate and multifactorial nature of BM. Thus, complementing existing in vivo models with multiscale computational approaches is crucial for dissecting the complex interactions between tumor cells and the bone microenvironment to advance our understanding of the metastatic process and therapy response. Accordingly, we developed a series of in vivo-inspired, spatially explicit, multicellular agent-based models of BM that effectively recapitulate key aspects of tumor progression, including angiogenesis and bone resorption. The digital twins were rigorously calibrated using in vivo data from prostate and kidney tumors. The models have utility for evaluating therapy response, as verified by the simulation of both the antiangiogenic effects of cabozantinib and the antiresorptive effects of zoledronic acid. These results highlight the predictive character of the agent-based models of BM in anticipating therapeutic outcomes and increasing our understanding of the complex dynamics of BM. Significance: The model offers a transformative tool for evaluating treatment strategies, including combination therapies, with the potential to accelerate the development of highly effective, targeted interventions for bone metastatic cancers. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI .

Goal: Effective preoperative planning for shoulder joint replacement requires accurate glenohumeral joint (GH) digital surfaces and reliable clinical staging. Methods: xCEL-UNet was designed as a dual-task deep network for humerus and scapula bone reconstruction in CT scans, and assessment of three GH joint clinical conditions, namely osteophyte size (OS), joint space reduction (JS), and humeroscapular alignment (HSA). Results: Trained on a dataset of 571 patients, the model optimized segmentation and classification through transfer learning. It achieved median root mean squared errors of 0.31 and 0.24 mm, and Hausdorff distances of 2.35 and 3.28 mm for the humerus and scapula, respectively. Classification accuracy was 91 for OS, 93 for JS, and 85% for HSA. GradCAM-based activation maps validated the network's interpretability. Conclusions: this framework delivers accurate 3D bone surface reconstructions and dependable clinical assessments of the GH joint, offering robust support for therapeutic decision-making in shoulder arthroplasty.

Prediction of the therapy response after withdrawal in silico and in vivo verification

BV parameters, quantification at the edge vs core, in PC3 and RENCA tumors