Mixed Reality and Artificial Intelligence: A Holistic Approach to Multimodal Visualization and Extended Interaction in Knee Osteotomy.

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 [Formula: see text] = 0.14 and [Formula: see text] = 0.13, [Formula: see text]). 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. Clinical and Translational Impact Statement - Holoknee can be helpful to support surgeons in the preoperative planning of knee osteotomy. It has the potential to impact positively the training of the future generation of residents and aid surgeons in the intraoperative stage.

Use of oxygen-ozone therapy to improve the effectiveness of antibiotic treatment on infected arthroplasty: protocol for a superiority, open-label, multicentre, randomised, parallel trial.

INTRODUCTION: Surgical site infections still remain a major public health challenge and have become an increasing universal risk, especially for the implantation of orthopaedic devices.Unfortunately, the discovery and increasingly widespread use (especially the misuse) of antibiotics have led to the rapid appearance of antibiotic-resistant strains today; more and more infections are caused by microorganisms that fail to respond to conventional treatments.Oxygen-ozone therapy has been extensively used and studied for decades across various potential medical applications and has provided consistent effects with minimal side effects.This study aims to determine the superiority of oxygen-ozone therapy in combination with oral antibiotic therapy in patients with wound infections after an orthopaedic device implantation when compared with antibiotic therapy alone. METHODS AND ANALYSIS: This is an open-label, multicentre, randomised, parallel-group study that aims to assess the efficacy and safety of oxygen-ozone therapy in combination with oral antibiotic therapy to treat infections in patients (male or female aged ≥18 years) having undergone surgery for the implant of an orthopaedic device. Patients must have at least one (but no more than three) postoperative wounds in the site of surgery (ulcers, eschars and sores) and at least one symptom (pain, burning, redness and malodour) and at least one sign (erythema, local warmth, swelling and purulent secretion) of infection of at least moderate intensity (score ≥2) in the target lesion at the screening visit (patients with wounds without signs of localised infection or with undermining wounds will be excluded).Patients (n=186) will be recruited from five Italian hospitals and studied for 7 weeks. All will be assigned to one of the two treatment groups according to a web-based, centralised randomisation procedure and placed into either the (1) intervention: oxygen-ozone therapy 2-3 times a week for 6 weeks (for a maximum of 15 sessions) simultaneously with an appropriate oral antibiotic therapy prescribed at baseline or (2) control: oral antibiotic therapy prescribed at baseline.The primary outcome is the efficacy and superiority of the treatment (ozone and oral antibiotic therapies); secondary outcomes include the resolution of signs and symptoms, modifications in lesion size and the treatment's safety and tolerability. ETHICS AND DISSEMINATION: This study has been reviewed and approved by the responsible Independent Ethics Committee (IEC) of COMITATO ETICO CAMPANIA NORD, located at 'Azienda Ospedaliera San Giuseppe Moscati di Avellino'.After completion of the study, the project coordinator will prepare a draft manuscript containing the final results of the study on the basis of the statistical analysis. The manuscript will be derived by the co-authors for comments, and after revision, it will be sent to a major scientific journal. Findings will be disseminated via online and print media, events and peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT04787575.

Combined Edge Loss UNet for Optimized Segmentation in Total Knee Arthroplasty Preoperative Planning.

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.