Tablet-based Augmented reality and 3D printed templates in fully guided Microtia Reconstruction: a clinical workflow.

BACKGROUND: Microtia is a congenital malformation of the auricle that affects approximately 4 of every 10,000 live newborns. Radiographic film paper is traditionally employed to bidimensionally trace the structures of the contralateral healthy ear in a quasi-artistic manner. Anatomical points provide linear and angular measurements. However, this technique proves time-consuming, subjectivity-rich, and greatly dependent on surgeon expertise. Hence, it's susceptible to shape errors and misplacement. METHODS: We present an innovative clinical workflow that combines 3D printing and augmented reality (AR) to increase objectivity and reproducibility of these procedures. Specifically, we introduce patient-specific 3D cutting templates and remodeling molds to carve and construct the cartilaginous framework that will conform the new ear. Moreover, we developed an in-house AR application compatible with any commercial Android tablet. It precisely guides the positioning of the new ear during surgery, ensuring symmetrical alignment with the healthy one and avoiding time-consuming intraoperative linear or angular measurements. Our solution was evaluated in one case, first with controlled experiments in a simulation scenario and finally during surgery. RESULTS: Overall, the ears placed in the simulation scenario had a mean absolute deviation of 2.2 ± 1.7 mm with respect to the reference plan. During the surgical intervention, the reconstructed ear was 3.1 mm longer and 1.3 mm wider with respect to the ideal plan and had a positioning error of 2.7 ± 2.4 mm relative to the contralateral side. Note that in this case, additional morphometric variations were induced from inflammation and other issues intended to be addressed in a subsequent stage of surgery, which are independent of our proposed solution. CONCLUSIONS: In this work we propose an innovative workflow that combines 3D printing and AR to improve ear reconstruction and positioning in microtia correction procedures. Our implementation in the surgical workflow showed good accuracy, empowering surgeons to attain consistent and objective outcomes.

Is a greater degree of constraint really harmful? Clinical biomechanical comparative study between condylar constrained knee and rotating hinge prosthesis.

BACKGROUND: The real degree of constraint of rotating hinge knee and condylar constrained prostheses is a matter of discussion in revision knee arthroplasty. The objective of this study is to compare the tibial rotation between implants in the clinical settings. METHODS: An investigator blinded experimental study was designed including 20 patients: in 10 of them a rotating hinge knee prosthesis (Endomodel®, LINK) was implanted and in the remaining 10 a constrained condylar knee prosthesis (LCCK®, Zimmer) was used. A medial parapatellar approach was performed and implantation was performed according to conventional surgical technique. Tibial rotation was measured with two accelerometers in full extension and at 30°, 60° and 90° of flexion. Pre and postoperative Knee Injury and Osteoarthritis Outcome Score was recorded. FINDINGS: Both groups were homogenous in age (73.4 years in rotating hinge knee prosthesis vs 74 years in constrained condylar knee group), sex, laterality and preoperative Knee Injury and Osteoarthritis Outcome Score (p > 0.05). The postoperative Knee Injury and Osteoarthritis Outcome Score was significantly higher in the rotating hinge knee prosthesis group (80.98 vs 76.28). The degrees of tibial rotation measured by inertial sensors in the rotating hinge knee prosthesis group were also significantly higher than those measured in the constrained condylar knee group (5.66° vs 2.1°) with p = 0.001. INTERPRETATION: Rotating hinge knee prosthesis appears to represent a lower rotational constraint degree than constrained condylar knee systems in clinical practice and it may increase the clinical satisfaction. The clinical significance: Rotating hinge knee prosthesis appears to represent a lower constraint degree than constrained condylar knee systems in clinical practice.

Selective sparing of bladder and rectum sub-regions in radiotherapy of prostate cancer combining knowledge-based automatic planning and multicriteria optimization.

Background and Purpose: The association between dose to selected bladder and rectum symptom-related sub-regions (SRS) and late toxicity after prostate cancer radiotherapy has been evidenced by voxel-wise analyses. The aim of the current study was to explore the feasibility of combining knowledge-based (KB) and multi-criteria optimization (MCO) to spare SRSs without compromising planning target volume (PTV) dose delivery, including pelvic-node irradiation. Materials and Methods: Forty-five previously treated patients (74.2 Gy/28fr) were selected and SRSs (in the bladder, associated with late dysuria/hematuria/retention; in the rectum, associated with bleeding) were generated using deformable registration. A KB model was used to obtain clinically suitable plans (KB-plan). KB-plans were further optimized using MCO, aiming to reduce dose to the SRSs while safeguarding target dose coverage, homogeneity and avoiding worsening dose volume histograms of the whole bladder, rectum and other organs at risk. The resulting MCO-generated plans were examined to identify the best-compromise plan (KB + MCO-plan). Results: The mean SRS dose decreased in almost all patients for each SRS. D1% also decreased in the large majority, less frequently for dysuria/bleeding SRS. Mean differences were statistically significant (p < 0.05) and ranged between 1.3 and 2.2 Gy with maximum reduction of mean dose up to 3-5 Gy for the four SRSs. The better sparing of SRSs was obtained without compromising PTVs coverage. Conclusions: Selectively sparing SRSs without compromising PTV coverage is feasible and has the potential to reduce toxicities in prostate cancer radiotherapy. Further investigation to better quantify the expected risk reduction of late toxicities is warranted.

Real-time integration between Microsoft HoloLens 2 and 3D Slicer with demonstration in pedicle screw placement planning.

PURPOSE: Up to date, there has been a lack of software infrastructure to connect 3D Slicer to any augmented reality (AR) device. This work describes a novel connection approach using Microsoft HoloLens 2 and OpenIGTLink, with a demonstration in pedicle screw placement planning. METHODS: We developed an AR application in Unity that is wirelessly rendered onto Microsoft HoloLens 2 using Holographic Remoting. Simultaneously, Unity connects to 3D Slicer using the OpenIGTLink communication protocol. Geometrical transform and image messages are transferred between both platforms in real time. Through the AR glasses, a user visualizes a patient's computed tomography overlaid onto virtual 3D models showing anatomical structures. We technically evaluated the system by measuring message transference latency between the platforms. Its functionality was assessed in pedicle screw placement planning. Six volunteers planned pedicle screws' position and orientation with the AR system and on a 2D desktop planner. We compared the placement accuracy of each screw with both methods. Finally, we administered a questionnaire to all participants to assess their experience with the AR system. RESULTS: The latency in message exchange is sufficiently low to enable real-time communication between the platforms. The AR method was non-inferior to the 2D desktop planner, with a mean error of 2.1 ± 1.4 mm. Moreover, 98% of the screw placements performed with the AR system were successful, according to the Gertzbein-Robbins scale. The average questionnaire outcomes were 4.5/5. CONCLUSIONS: Real-time communication between Microsoft HoloLens 2 and 3D Slicer is feasible and supports accurate planning for pedicle screw placement.

Deep learning-based segmentation of prostatic urethra on computed tomography scans for treatment planning.

Background and purpose: The intraprostatic urethra is an organ at risk in prostate cancer radiotherapy, but its segmentation in computed tomography (CT) is challenging. This work sought to: i) propose an automatic pipeline for intraprostatic urethra segmentation in CT, ii) analyze the dose to the urethra, iii) compare the predictions to magnetic resonance (MR) contours. Materials and methods: First, we trained Deep Learning networks to segment the rectum, bladder, prostate, and seminal vesicles. Then, the proposed Deep Learning Urethra Segmentation model was trained with the bladder and prostate distance transforms and 44 labeled CT with visible catheters. The evaluation was performed on 11 datasets, calculating centerline distance (CLD) and percentage of centerline within 3.5 and 5 mm. We applied this method to a dataset of 32 patients treated with intensity-modulated radiation therapy (IMRT) to quantify the urethral dose. Finally, we compared predicted intraprostatic urethra contours to manual delineations in MR for 15 patients without catheter. Results: A mean CLD of 1.6 ± 0.8 mm for the whole urethra and 1.7 ± 1.4, 1.5 ± 0.9, and 1.7 ± 0.9 mm for the top, middle, and bottom thirds were obtained in CT. On average, 94% and 97% of the segmented centerlines were within a 3.5 mm and 5 mm radius, respectively. In IMRT, the urethra received a higher dose than the overall prostate. We also found a slight deviation between the predicted and manual MR delineations. Conclusion: A fully-automatic segmentation pipeline was validated to delineate the intraprostatic urethra in CT images.

ABANICCO: A New Color Space for Multi-Label Pixel Classification and Color Analysis.

Classifying pixels according to color, and segmenting the respective areas, are necessary steps in any computer vision task that involves color images. The gap between human color perception, linguistic color terminology, and digital representation are the main challenges for developing methods that properly classify pixels based on color. To address these challenges, we propose a novel method combining geometric analysis, color theory, fuzzy color theory, and multi-label systems for the automatic classification of pixels into 12 conventional color categories, and the subsequent accurate description of each of the detected colors. This method presents a robust, unsupervised, and unbiased strategy for color naming, based on statistics and color theory. The proposed model, "ABANICCO" (AB ANgular Illustrative Classification of COlor), was evaluated through different experiments: its color detection, classification, and naming performance were assessed against the standardized ISCC-NBS color system; its usefulness for image segmentation was tested against state-of-the-art methods. This empirical evaluation provided evidence of ABANICCO's accuracy in color analysis, showing how our proposed model offers a standardized, reliable, and understandable alternative for color naming that is recognizable by both humans and machines. Hence, ABANICCO can serve as a foundation for successfully addressing a myriad of challenges in various areas of computer vision, such as region characterization, histopathology analysis, fire detection, product quality prediction, object description, and hyperspectral imaging.

Clinical feasibility of combining intraoperative electron radiation therapy with minimally invasive surgery: a potential for electron-FLASH clinical development.

BACKGROUND: Local cancer therapy by combining real-time surgical exploration and resection with delivery of a single dose of high-energy electron irradiation entails a very precise and effective local therapeutic approach. Integrating the benefits from minimally invasive surgical techniques with the very precise delivery of intraoperative electron irradiation results in an efficient combined modality therapy. METHODS: Patients with locally advanced disease, who are candidates for laparoscopic and/or thoracoscopic surgery, received an integrated multimodal management. Preoperative treatment included induction chemotherapy and/or chemoradiation, followed by laparoscopic surgery and intraoperative electron radiation therapy. RESULTS: In a period of 5 consecutive years, 125 rectal cancer patients were treated, of which 35% underwent a laparoscopic approach. We found no differences in cancer outcomes and tolerance between the open and laparoscopic groups. Two esophageal cancer patients were treated with IOeRT during thoracoscopic resection, with the resection specimens showing intense downstaging effects. Two oligo-recurrent prostatic cancer patients (isolated nodal progression) had a robotic-assisted surgical resection and post-lymphadenectomy electron boost on the vascular and lateral pelvic wall. CONCLUSIONS: Minimally invasive and robotic-assisted surgery is feasible to combine with intraoperative electron radiation therapy and offers a new model explored with electron-FLASH beams.

Practice-oriented solutions integrating intraoperative electron irradiation and personalized proton therapy for recurrent or unresectable cancers: Proof of concept and potential for dual FLASH effect.

Background: Oligo-recurrent disease has a consolidated evidence of long-term surviving patients due to the use of intense local cancer therapy. The latter combines real-time surgical exploration/resection with high-energy electron beam single dose of irradiation. This results in a very precise radiation dose deposit, which is an essential element of contemporary multidisciplinary individualized oncology. Methods: Patient candidates to proton therapy were evaluated in Multidisciplinary Tumor Board to consider improved treatment options based on the institutional resources and expertise. Proton therapy was delivered by a synchrotron-based pencil beam scanning technology with energy levels from 70.2 to 228.7 MeV, whereas intraoperative electrons were generated in a miniaturized linear accelerator with dose rates ranging from 22 to 36 Gy/min (at Dmax) and energies from 6 to 12 MeV. Results: In a period of 24 months, 327 patients were treated with proton therapy: 218 were adults, 97 had recurrent cancer, and 54 required re-irradiation. The specific radiation modalities selected in five cases included an integral strategy to optimize the local disease management by the combination of surgery, intraoperative electron boost, and external pencil beam proton therapy as components of the radiotherapy management. Recurrent cancer was present in four cases (cervix, sarcoma, melanoma, and rectum), and one patient had a primary unresectable locally advanced pancreatic adenocarcinoma. In re-irradiated patients (cervix and rectum), a tentative radical total dose was achieved by integrating beams of electrons (ranging from 10- to 20-Gy single dose) and protons (30 to 54-Gy Relative Biological Effectiveness (RBE), in 10-25 fractions). Conclusions: Individual case solution strategies combining intraoperative electron radiation therapy and proton therapy for patients with oligo-recurrent or unresectable localized cancer are feasible. The potential of this combination can be clinically explored with electron and proton FLASH beams.

Deep Learning-Based Segmentation of Head and Neck Organs-at-Risk with Clinical Partially Labeled Data.

Radiotherapy is one of the main treatments for localized head and neck (HN) cancer. To design a personalized treatment with reduced radio-induced toxicity, accurate delineation of organs at risk (OAR) is a crucial step. Manual delineation is time- and labor-consuming, as well as observer-dependent. Deep learning (DL) based segmentation has proven to overcome some of these limitations, but requires large databases of homogeneously contoured image sets for robust training. However, these are not easily obtained from the standard clinical protocols as the OARs delineated may vary depending on the patient's tumor site and specific treatment plan. This results in incomplete or partially labeled data. This paper presents a solution to train a robust DL-based automated segmentation tool exploiting a clinical partially labeled dataset. We propose a two-step workflow for OAR segmentation: first, we developed longitudinal OAR-specific 3D segmentation models for pseudo-contour generation, completing the missing contours for some patients; with all OAR available, we trained a multi-class 3D convolutional neural network (nnU-Net) for final OAR segmentation. Results obtained in 44 independent datasets showed superior performance of the proposed methodology for the segmentation of fifteen OARs, with an average Dice score coefficient and surface Dice similarity coefficient of 80.59% and 88.74%. We demonstrated that the model can be straightforwardly integrated into the clinical workflow for standard and adaptive radiotherapy.

Intra-Operative Electron Radiation Therapy: An Update of the Evidence Collected in 40 Years to Search for Models for Electron-FLASH Studies.

INTRODUCTION: The clinical practice and outcome results of intraoperative electron radiation therapy (IOeRT) in cancer patients have been extensively reported over 4 decades. Electron beams can be delivered in the promising FLASH dose rate. METHODS AND MATERIALS: Several cancer models were approached by two alternative radiobiological strategies to optimize local cancer control: boost versus exclusive IOeRT. Clinical outcomes are revisited via a bibliometric search performed for the elaboration of ESTRO/ACROP IORT guidelines. RESULTS: In the period 1982 to 2020, a total of 19,148 patients were registered in 116 publications concerning soft tissue sarcomas (9% of patients), unresected and borderline-resected pancreatic cancer (22%), locally recurrent and locally advanced rectal cancer (22%), and breast cancer (45%). Clinical outcomes following IOeRT doses in the range of 10 to 25 Gy (with or without external beam fractionated radiation therapy) show a wide range of local control from 40 to 100% depending upon cancer site, histology, stage, and treatment intensity. Constraints for normal tissue tolerance are important to maintain tumor control combined with acceptable levels of side effects. CONCLUSIONS: IOeRT represents an evidence-based approach for several tumor types. A specific risk analysis for local recurrences supports the identification of cancer models that are candidates for FLASH studies.

HoloLens 1 vs. HoloLens 2: Improvements in the New Model for Orthopedic Oncological Interventions.

This work analyzed the use of Microsoft HoloLens 2 in orthopedic oncological surgeries and compares it to its predecessor (Microsoft HoloLens 1). Specifically, we developed two equivalent applications, one for each device, and evaluated the augmented reality (AR) projection accuracy in an experimental scenario using phantoms based on two patients. We achieved automatic registration between virtual and real worlds using patient-specific surgical guides on each phantom. They contained a small adaptor for a 3D-printed AR marker, the characteristic patterns of which were easily recognized using both Microsoft HoloLens devices. The newest model improved the AR projection accuracy by almost 25%, and both of them yielded an RMSE below 3 mm. After ascertaining the enhancement of the second model in this aspect, we went a step further with Microsoft HoloLens 2 and tested it during the surgical intervention of one of the patients. During this experience, we collected the surgeons' feedback in terms of comfortability, usability, and ergonomics. Our goal was to estimate whether the improved technical features of the newest model facilitate its implementation in actual surgical scenarios. All of the results point to Microsoft HoloLens 2 being better in all the aspects affecting surgical interventions and support its use in future experiences.

UltrARsound: in situ visualization of live ultrasound images using HoloLens 2.

PURPOSE: Augmented Reality (AR) has the potential to simplify ultrasound (US) examinations which usually require a skilled and experienced sonographer to mentally align narrow 2D cross-sectional US images in the 3D anatomy of the patient. This work describes and evaluates a novel approach to track retroreflective spheres attached to the US probe using an inside-out technique with the AR glasses HoloLens 2. Finally, live US images are displayed in situ on the imaged anatomy. METHODS: The Unity application UltrARsound performs spatial tracking of the US probe and attached retroreflective markers using the depth camera integrated into the AR glasses-thus eliminating the need for an external tracking system. Additionally, a Kalman filter is implemented to improve the noisy measurements of the camera. US images are streamed wirelessly via the PLUS toolkit to HoloLens 2. The technical evaluation comprises static and dynamic tracking accuracy, frequency and latency of displayed images. RESULTS: Tracking is performed with a median accuracy of 1.98 mm/1.81[Formula: see text] for the static setting when using the Kalman filter. In a dynamic scenario, the median error was 2.81 mm/1.70[Formula: see text]. The tracking frequency is currently limited to 20 Hz. 83% of the displayed US images had a latency lower than 16 ms. CONCLUSIONS: In this work, we showed that spatial tracking of retroreflective spheres with the depth camera of HoloLens 2 is feasible, achieving a promising accuracy for in situ visualization of live US images. For tracking, no additional hardware nor modifications to HoloLens 2 are required making it a cheap and easy-to-use approach. Moreover, a minimal latency of displayed images enables a real-time perception for the sonographer.

Evaluation of optical tracking and augmented reality for needle navigation in sacral nerve stimulation.

BACKGROUND AND OBJECTIVE: Sacral nerve stimulation (SNS) is a minimally invasive procedure where an electrode lead is implanted through the sacral foramina to stimulate the nerve modulating colonic and urinary functions. One of the most crucial steps in SNS procedures is the placement of the tined lead close to the sacral nerve. However, needle insertion is very challenging for surgeons. Several x-ray projections are required to interpret the needle position correctly. In many cases, multiple punctures are needed, causing an increase in surgical time and patient's discomfort and pain. In this work we propose and evaluate two different navigation systems to guide electrode placement in SNS surgeries designed to reduce surgical time, minimize patient discomfort and improve surgical outcomes. METHODS: We developed, for the first alternative, an open-source navigation software to guide electrode placement by real-time needle tracking with an optical tracking system (OTS). In the second method, we present a smartphone-based AR application that displays virtual guidance elements directly on the affected area, using a 3D printed reference marker placed on the patient. This guidance facilitates needle insertion with a predefined trajectory. Both techniques were evaluated to determine which one obtained better results than the current surgical procedure. To compare the proposals with the clinical method, we developed an x-ray software tool that calculates a digitally reconstructed radiograph, simulating the fluoroscopy acquisitions during the procedure. Twelve physicians (inexperienced and experienced users) performed needle insertions through several specific targets to evaluate the alternative SNS guidance methods on a realistic patient-based phantom. RESULTS: With each navigation solution, we observed that users took less average time to complete each insertion (36.83 s and 44.43 s for the OTS and AR methods, respectively) and needed fewer average punctures to reach the target (1.23 and 1.96 for the OTS and AR methods respectively) than following the standard clinical method (189.28 s and 3.65 punctures). CONCLUSIONS: To conclude, we have shown two navigation alternatives that could improve surgical outcome by significantly reducing needle insertions, surgical time and patient's pain in SNS procedures. We believe that these solutions are feasible to train surgeons and even replace current SNS clinical procedures.

Corrigendum: Practice-oriented solutions integrating intraoperative electron irradiation and personalized proton therapy for recurrent or unresectable cancers: Proof of concept and potential for dual FLASH effect.

[This corrects the article DOI: 10.3389/fonc.2022.1037262.].

Augmented Reality as a Tool to Guide PSI Placement in Pelvic Tumor Resections.

Patient-specific instruments (PSIs) have become a valuable tool for osteotomy guidance in complex surgical scenarios such as pelvic tumor resection. They provide similar accuracy to surgical navigation systems but are generally more convenient and faster. However, their correct placement can become challenging in some anatomical regions, and it cannot be verified objectively during the intervention. Incorrect installations can result in high deviations from the planned osteotomy, increasing the risk of positive resection margins. In this work, we propose to use augmented reality (AR) to guide and verify PSIs placement. We designed an experiment to assess the accuracy provided by the system using a smartphone and the HoloLens 2 and compared the results with the conventional freehand method. The results showed significant differences, where AR guidance prevented high osteotomy deviations, reducing maximal deviation of 54.03 mm for freehand placements to less than 5 mm with AR guidance. The experiment was performed in two versions of a plastic three-dimensional (3D) printed phantom, one including a silicone layer to simulate tissue, providing more realism. We also studied how differences in shape and location of PSIs affect their accuracy, concluding that those with smaller sizes and a homogeneous target surface are more prone to errors. Our study presents promising results that prove AR's potential to overcome the present limitations of PSIs conveniently and effectively.

Effectiveness of Automatic Planning of Fronto-orbital Advancement for the Surgical Correction of Metopic Craniosynostosis.

BACKGROUND: The surgical correction of metopic craniosynostosis usually relies on the subjective judgment of surgeons to determine the configuration of the cranial bone fragments and the degree of overcorrection. This study evaluates the effectiveness of a new approach for automatic planning of fronto-orbital advancement based on statistical shape models and including overcorrection. METHODS: This study presents a planning software to automatically estimate osteotomies in the fronto-orbital region and calculate the optimal configuration of the bone fragments required to achieve an optimal postoperative shape. The optimal cranial shape is obtained using a statistical head shape model built from 201 healthy subjects (age 23 ± 20 months; 89 girls). Automatic virtual plans were computed for nine patients (age 10.68 ± 1.73 months; four girls) with different degrees of overcorrection, and compared with manual plans designed by experienced surgeons. RESULTS: Postoperative cranial shapes generated by automatic interventional plans present accurate matching with normative morphology and enable to reduce the malformations in the fronto-orbital region by 82.01 ± 6.07%. The system took on average 19.22 seconds to provide the automatic plan, and allows for personalized levels of overcorrection. The automatic plans with an overcorrection of 7 mm in minimal frontal breadth provided the closest match (no significant difference) to the manual plans. CONCLUSIONS: The automatic software technology effectively achieves correct cranial morphometrics and volumetrics with respect to normative cranial shapes. The automatic approach has the potential to reduce the duration of preoperative planning, reduce inter-surgeon variability, and provide consistent surgical outcomes.

Real-Time Tool Detection for Workflow Identification in Open Cranial Vault Remodeling.

Deep learning is a recent technology that has shown excellent capabilities for recognition and identification tasks. This study applies these techniques in open cranial vault remodeling surgeries performed to correct craniosynostosis. The objective was to automatically recognize surgical tools in real-time and estimate the surgical phase based on those predictions. For this purpose, we implemented, trained, and tested three algorithms based on previously proposed Convolutional Neural Network architectures (VGG16, MobileNetV2, and InceptionV3) and one new architecture with fewer parameters (CranioNet). A novel 3D Slicer module was specifically developed to implement these networks and recognize surgical tools in real time via video streaming. The training and test data were acquired during a surgical simulation using a 3D printed patient-based realistic phantom of an infant's head. The results showed that CranioNet presents the lowest accuracy for tool recognition (93.4%), while the highest accuracy is achieved by the MobileNetV2 model (99.6%), followed by VGG16 and InceptionV3 (98.8% and 97.2%, respectively). Regarding phase detection, InceptionV3 and VGG16 obtained the best results (94.5% and 94.4%), whereas MobileNetV2 and CranioNet presented worse values (91.1% and 89.8%). Our results prove the feasibility of applying deep learning architectures for real-time tool detection and phase estimation in craniosynostosis surgeries.

Conceptual evolution of 3D printing in orthopedic surgery and traumatology: from "do it yourself" to "point of care manufacturing".

BACKGROUND: 3D printing technology in hospitals facilitates production models such as point-of-care manufacturing. Orthopedic Surgery and Traumatology is the specialty that can most benefit from the advantages of these tools. The purpose of this study is to present the results of the integration of 3D printing technology in a Department of Orthopedic Surgery and Traumatology and to identify the productive model of the point-of-care manufacturing as a paradigm of personalized medicine. METHODS: Observational, descriptive, retrospective and monocentric study of a total of 623 additive manufacturing processes carried out in a Department of Orthopedic Surgery and Traumatology from November 2015 to March 2020. Variables such as product type, utility, time or materials for manufacture were analyzed. RESULTS: The areas of expertise that have performed more processes are Traumatology, Reconstructive and Orthopedic Oncology. Pre-operative planning is their primary use. Working and 3D printing hours, as well as the amount of 3D printing material used, vary according to the type of product or material delivered to perform the process. The most commonly used 3D printing material for manufacturing is polylactic acid, although biocompatible resin has been used to produce surgical guides. In addition, the hospital has worked on the co-design of customized implants with manufacturing companies. CONCLUSIONS: The integration of 3D printing in a Department of Orthopedic Surgery and Traumatology allows identifying the conceptual evolution from "Do-It-Yourself" to "POC manufacturing".

Point-of-care manufacturing: a single university hospital's initial experience.

BACKGROUND: The integration of 3D printing technology in hospitals is evolving toward production models such as point-of-care manufacturing. This study aims to present the results of the integration of 3D printing technology in a manufacturing university hospital. METHODS: Observational, descriptive, retrospective, and monocentric study of 907 instances of 3D printing from November 2015 to March 2020. Variables such as product type, utility, time, or manufacturing materials were analyzed. RESULTS: Orthopedic Surgery and Traumatology, Oral and Maxillofacial Surgery, and Gynecology and Obstetrics are the medical specialties that have manufactured the largest number of processes. Working and printing time, as well as the amount of printing material, is different for different types of products and input data. The most common printing material was polylactic acid, although biocompatible resin was introduced to produce surgical guides. In addition, the hospital has worked on the co-design of custom-made implants with manufacturing companies and has also participated in tissue bio-printing projects. CONCLUSIONS: The integration of 3D printing in a university hospital allows identifying the conceptual evolution to "point-of-care manufacturing."

Combining Augmented Reality and 3D Printing to Improve Surgical Workflows in Orthopedic Oncology: Smartphone Application and Clinical Evaluation.

During the last decade, orthopedic oncology has experienced the benefits of computerized medical imaging to reduce human dependency, improving accuracy and clinical outcomes. However, traditional surgical navigation systems do not always adapt properly to this kind of interventions. Augmented reality (AR) and three-dimensional (3D) printing are technologies lately introduced in the surgical environment with promising results. Here we present an innovative solution combining 3D printing and AR in orthopedic oncological surgery. A new surgical workflow is proposed, including 3D printed models and a novel AR-based smartphone application (app). This app can display the patient's anatomy and the tumor's location. A 3D-printed reference marker, designed to fit in a unique position of the affected bone tissue, enables automatic registration. The system has been evaluated in terms of visualization accuracy and usability during the whole surgical workflow. Experiments on six realistic phantoms provided a visualization error below 3 mm. The AR system was tested in two clinical cases during surgical planning, patient communication, and surgical intervention. These results and the positive feedback obtained from surgeons and patients suggest that the combination of AR and 3D printing can improve efficacy, accuracy, and patients' experience.