Patient-specific Models Research Articles

The central neck in 3 dimensions: a digital model derived from radiology, peer-reviewed literature, and medical illustration

This project aims to create a 3-dimensional (3D) digital model of central neck anatomy, including muscles, neurovasculature, and viscera. The neck was segmented from a CT angiogram of a healthy 29-year-old female, and then transferred into 3D illustration software after removing imaging artifact. To enhance anatomical accuracy, details not captured by imaging or unclear from the study were added and refined with relevant peer-reviewed literature. These structures were later incorporated into the head and neck model. Following segmentation, the 3D models was refined in 3D CAD software. Structures not identified by segmentation were designed by a medical illustrator and all structures were refined from peer-reviewed anatomic literature. Where discrepancies were found in the literature, the size, source, and methodology of the studies were considered to determine the most common variants of each structure. Finally, the 3D model was uploaded to MedReality for online viewing. Technological progress in illustration software, patient specific anatomic modeling, and precise 3D CAD modeling has allowed the most accurate 3D surgical anatomy model to be created when combined with existing literature. The associated 3D model and manuscript will serve as a helpful tool for medical professionals regardless of academic level. To our knowledge, this literature-based model of the central neck combined with 3D anatomic modeling from real patient data is unprecedented and should aid in surgical anatomy education.

Coronary Access and PCI after Transcatheter Aortic Valve Replacement With Different Self-Expanding Platforms in Failed Surgical Valves

BackgroundCoronary access (CA) and percutaneous coronary intervention (PCI) might be challenging after valve-in-valve (ViV) transcatheter aortic valve replacement (TAVR) with supra-annular self-expanding valves (SS-TAVs) in surgical aortic valves (SAVs). Our study aim was to compare feasibility, predictors, and techniques of CA and PCI following ViV-TAVR with ACURATE neo2 (Boston Scientific, Marlborough, MA) and Evolut PRO+ (Medtronic, Minneapolis, MN). MethodsFifteen computed tomography (CT)-based patient-specific aortic models were 3-dimensionally (3D) printed and implanted with specific SAVs and with the 2 SS-TAVs with commissural alignment. Two operators attempted CA (n = 120) and PCI (n = 120) of each coronary artery in a pulsatile-flow-simulator, under real catheterization laboratory conditions. The primary endpoints were the rate of successful CA and PCI. Outcomes with different SS-TAVs were directly compared. An internally mounted borescope camera was used to assess procedures. CT of the models was obtained. ResultsACURATE neo2 showed significantly higher rates of successful CA (96.7% vs 75%, P = 0.001) and PCI (98.3% vs 85%, P = 0.008) and was associated with a shorter procedural time compared with Evolut PRO+. Independent predictors of unsuccessful CA and PCI were smaller SAV size and Evolut PRO+. The advantage of ACURATE neo2 was mediated by a larger valve-to-anatomy distance at the top of the leaflet plane (11.3 mm vs 4.8 mm), facilitating more often an external cannulation approach for both CA (36.7% vs 15%, P < 0.001) and PCI (36.7% vs 21.7%, P = 0.013). ConclusionsThe rate of successful CA and PCI following ViV-TAVR was higher with ACURATE neo2 compared with Evolut PRO+. The differences in SS-TAVs design affected the cannulation approach and subsequent procedural outcomes.

Sublabial transmaxillary approach to the inferior aspect of the orbit.

The objective was to demonstrate the surgical steps and outcomes of the sublabial transmaxillary microsurgical approach with endoscopic assistance to treat lesions in the inferior aspect of the orbit, as well as to describe the use of patient-specific 3D models to facilitate surgical preparation and improve experience with the technique. The authors' study evaluated data from patients who underwent an endoscope-assisted sublabial transmaxillary approach for inferior orbital lesions. For 2 patients, 3D models were created for preoperative planning and assessment of the approach. Surgical steps comprised osteotomy to access the maxillary sinus, bony resection of the orbital floor, opening of the periorbital fascia, and dissecting and removing the lesion, followed by closure. The neuroendoscope was used to inspect the surgical cavity between each step. The study included 5 patients with varying visual field defects and proptosis who underwent the sublabial transmaxillary microsurgical approach with endoscopic assistance. Complete resection was achieved in all, and all patients reported improvement in visual field defects and proptosis after the procedure. No complications were observed except for transient unilateral maxillary edema noted around the incision site in 3 patients during the early postoperative period, which resolved within a few days. Histopathological examination confirmed the diagnosis of cavernous malformation in all patients. The sublabial transmaxillary approach is a direct and safe method to resect cavernous malformations at the inferior aspect of the orbit. It reduces the risk of complications associated with lateral, transcranial, and transnasal approaches that may cross critical structures. The microsurgical approach provides the benefit of two-handed dissection for lesions embedded in orbital fat, which can be challenging because of adhesions to surrounding tissues. The use of 3D models can facilitate surgical planning and enhance familiarity with the approach.

Computer-Assisted Hip Arthroscopic Surgery for Secondary Femoroacetabular Impingement After Rotational Acetabular Osteotomy Using Capsular Takedown Techniques

Secondary femoroacetabular impingement (FAI) is a severe complication observed after acetabular osteotomy; however, the diagnostic and treatment strategies of FAI have not been well established, especially with respect to arthroscopic techniques. We here describe hip arthroscopic osteochondroplasty for secondary FAI using computer-assisted techniques. The main features and tips of our technique are preoperative computed tomography (CT)-based surgical planning and the intraoperative capsule takedown method. Computer modeling produces a patient-specific 3-dimensional CT bone model. Subsequently, we identify the impingement point between the acetabulum and the femoral neck using dynamic simulation. The excess bony bumps are resected through computer surgical simulation, and pre- and postoperative 3-dimensional CT bone models are combined to identify the appropriate resection area. For the surgical technique, it is important to detach the capsule to visualize the acetabulum bony excess. Once the resection area has been sufficiently visualized, the bone resection is performed. Finally, the capsule is reattached to the excavated acetabulum, and the delaminated labrum is sewn up with the capsule in a round bale shape.

Deep-learning-based segmentation using individual patient data on prostate cancer radiation therapy.

Organ-at-risk segmentation is essential in adaptive radiotherapy (ART). Learning-based automatic segmentation can reduce committed labor and accelerate the ART process. In this study, an auto-segmentation model was developed by employing individual patient datasets and a deep-learning-based augmentation method for tailoring radiation therapy according to the changes in the target and organ of interest in patients with prostate cancer. Two computed tomography (CT) datasets with well-defined labels, including contoured prostate, bladder, and rectum, were obtained from 18 patients. The labels of the CT images captured during radiation therapy (CT2nd) were predicted using CT images scanned before radiation therapy (CT1st). From the deformable vector fields (DVFs) created by using the VoxelMorph method, 10 DVFs were extracted when each of the modified CT and CT2nd images were deformed and registered to the fixed CT1st image. Augmented images were acquired by utilizing 110 extracted DVFs and spatially transforming the CT1st images and labels. An nnU-net autosegmentation network was trained by using the augmented images, and the CT2nd label was predicted. A patient-specific model was created for 18 patients, and the performances of the individual models were evaluated. The results were evaluated by employing the Dice similarity coefficient (DSC), average Hausdorff distance, and mean surface distance. The accuracy of the proposed model was compared with those of models trained with large datasets. Patient-specific models were developed successfully. For the proposed method, the DSC values of the actual and predicted labels for the bladder, prostate, and rectum were 0.94 ± 0.03, 0.84 ± 0.07, and 0.83 ± 0.04, respectively. We demonstrated the feasibility of automatic segmentation by employing individual patient datasets and image augmentation techniques. The proposed method has potential for clinical application in automatic prostate segmentation for ART.

Ex-vivo 3D cellular models of pancreatic ductal adenocarcinoma: from embryonic development to precision oncology.

Pancreas is a vital gland of gastro-intestinal system with exocrine and endocrine secretory functions, interweaved into essential metabolic circuitries of the human body. Pancreatic ductal adenocarcinoma (PDAC) represents one of the most lethal malignancies, with a five-year survival rate of 11%. This poor prognosis is primarily attributed to the absence of early symptoms, rapid metastatic dissemination, and the limited efficacy of current therapeutic interventions. Despite recent advancements in understanding the etiopathogenesis and treatment of PDAC, there remains a pressing need for improved individualized models, the identification of novel molecular targets, and the development of unbiased predictors of disease progression. Here we aim to explore the concept of precision medicine utilizing three-dimensional, patient-specific cellular models of pancreatic tumors and discuss their potential applications in uncovering novel druggable molecular targets and predicting clinical parameters for individual patients.

Patient-specific vascularized tumor model: Blocking monocyte recruitment with multispecific antibodies targeting CCR2 and CSF-1R

Tumor-associated inflammation drives cancer progression and therapy resistance, often linked to the infiltration of monocyte-derived tumor-associated macrophages (TAMs), which are associated with poor prognosis in various cancers. To advance immunotherapies, testing on immunocompetent pre-clinical models of human tissue is crucial. We have developed an in vitro model of microvascular networks with tumor spheroids or patient tissues to assess monocyte trafficking into tumors and evaluate immunotherapies targeting the human tumor microenvironment. Our findings demonstrate that macrophages in vascularized breast and lung tumor models can enhance monocyte recruitment via CCL7 and CCL2, mediated by CSF-1R. Additionally, a multispecific antibody targeting CSF-1R, CCR2, and neutralizing TGF-β (CSF1R/CCR2/TGF-β Ab) repolarizes TAMs towards an anti-tumoral M1-like phenotype, reduces monocyte chemoattractant protein secretion, and blocks monocyte migration. This antibody also inhibits monocyte recruitment in patient-specific vascularized tumor models. In summary, this vascularized tumor model recapitulates the monocyte recruitment cascade, enabling functional testing of innovative therapeutic antibodies targeting TAMs in the tumor microenvironment.

Reverse cannulation method as a strategy for aortic aneurysm surgery: A computational fluid dynamics study on minimizing neurological risks

ObjectivesTo evaluate the blood flow velocity and wall shear stress in total arch replacement with a “shaggy” aorta, using computational fluid dynamics, and determine the optimal cannulation method. MethodsA patient-specific aortic arch aneurysm model was constructed by using computed tomography scans. Three cannulas were assessed, as follows: dispersive with a steep angle, dispersive with a gentle angle, and the endo-hole type. The cannula tips were oriented toward the aortic arch (standard direction) and aortic root (reversed direction), with an ideal angle (base orientation: 0°), tip orientations rotated 20° clockwise and counterclockwise from the base orientation. The variables of interest included the blood flow velocity, streamlines, wall shear stress, and flow distribution. ResultsThe standard direction resulted in variable accelerated flow and wall shear stress locations based on cannula tip orientation, leading to unstable cerebral branch flow. Minor deviation in the cannula tip angle and cannula type led to significant alterations in flow distribution. Conversely, in the reverse direction for all cannulas, no accelerated blood flow was observed in the proximal aortic arch or cerebral vessel ostia even with angular adjustments, helping maintain a stable cerebral branch flow. Minimal variation in blood flow distribution was observed across all cannula types and angles. ConclusionsOur simulations indicate that, irrespective of the cannula type or orientation, directing the cannula tip toward the aortic root (reversed direction) prevents accelerated blood flow in critical areas, suggesting its potential as an optimal approach for aortic arch surgery in “shaggy” aorta cases.

Computational framework for the generation of one-dimensional vascular models accounting for uncertainty in networks extracted from medical images.

One-dimensional (1D) cardiovascular models offer a non-invasive method to answer medical questions, including predictions of wave-reflection, shear stress, functional flow reserve, vascular resistance and compliance. This model type can predict patient-specific outcomes by solving 1D fluid dynamics equations in geometric networks extracted from medical images. However, the inherent uncertainty in in vivo imaging introduces variability in network size and vessel dimensions, affecting haemodynamic predictions. Understanding the influence of variation in image-derived properties is essential to assess the fidelity of model predictions. Numerous programs exist to render three-dimensional surfaces and construct vessel centrelines. Still, there is no exact way to generate vascular trees from the centrelines while accounting for uncertainty in data. This study introduces an innovative framework employing statistical change point analysis to generate labelled trees that encode vessel dimensions and their associated uncertainty from medical images. To test this framework, we explore the impact of uncertainty in 1D haemodynamic predictions in a systemic and pulmonary arterial network. Simulations explore haemodynamic variations resulting from changes in vessel dimensions and segmentation; the latter is achieved by analysing multiple segmentations of the same images. Results demonstrate the importance of accurately defining vessel radii and lengths when generating high-fidelity patient-specific haemodynamics models. KEY POINTS: This study introduces novel algorithms for generating labelled directed trees from medical images, focusing on accurate junction node placement and radius extraction using change points to provide haemodynamic predictions with uncertainty within expected measurement error. Geometric features, such as vessel dimension (length and radius) and network size, significantly impact pressure and flow predictions in both pulmonary and aortic arterial networks. Standardizing networks to a consistent number of vessels is crucial for meaningful comparisons and decreases haemodynamic uncertainty. Change points are valuable to understanding structural transitions in vascular data, providing an automated and efficient way to detect shifts in vessel characteristics and ensure reliable extraction of representative vessel radii.

Three-dimensional-printing-guided preoperative planning of upper and lower extremity pediatric orthopedic surgeries: A systematic review of surgical outcomes.

Three-dimensional printing has evolved into a cost-effective and accessible tool. In orthopedic surgery, creating patient-specific anatomical models and instrumentation improves visualization and surgical accuracy. In pediatric orthopedics, three-dimensional printing reduces operating time, radiation exposure, and blood loss by enhancing surgical efficacy. This review compares outcomes of three-dimensional printing-assisted surgeries with conventional surgeries for upper and lower extremity pediatric surgeries. A complete search of medical literature up to August 2023, using Ovid Medline, EMBASE, Scopus, Web of Science, and Cochrane Library was conducted in compliance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Broad search terms included "pediatrics," "orthopedic," and "3D-printing." Eligible studies were assessed for intraoperative time, blood loss, and fluoroscopy exposure. Out of 3299 initially identified articles, 14 articles met inclusion criteria. These studies included 409 pediatric patients, with ages averaging 9.51 years. The majority were retrospective studies (nine), with four prospective and one experimental study. Studies primarily utilized three-dimensional printing for navigation templates and implants. Results showed significant reductions in operative time, blood loss, and radiation exposure with three-dimensional printing. Complication occurrences were generally lower in three-dimensional printing surgeries, but there was no statistical significance. Three-dimensional printing is an emerging technology in the field of orthopedics, and it is primarily used for preoperative planning. For pediatric upper and lower extremity surgeries, three-dimensional printing leads to decreased operating room time, decreased intraoperative blood loss, and reduced radiation exposure. Other uses for three-dimensional printing include education, patient communication, the creation of patient-specific instrumentation and implants. Level III.

Evaluating the value of individualized 3D printed models for examination, diagnosis and treatment planning of cervical cancer

Background3D printing holds great potential of improving examination, diagnosis and treatment planning as well as interprofessional communication in the field of gynecological oncology. In the current manuscript we evaluated five individualized, patient-specific models of cervical cancer FIGO Stage I-III, created with 3D printing, concerning their value for translational oncology.MethodsMagnetic resonance imaging (MRI) of the pelvis was performed on a 3.0 Tesla MRI, including a T2-weighted isotropic 3D sequence. The MRI images were segmented and transferred to virtual 3D models via a custom-built 3D-model generation pipeline and printed by material extrusion. The 3D models were evaluated by all medical specialties involved in patient care of cervical cancer, namely surgeons, radiologists, pathologists and radiation oncologists. Information was obtained from evaluated profession-specific questionnaires which were filled out after inspecting all five models. The questionnaires included multiple-select questions, questions based on Likert scales (1 = „strongly disagree “ or „not at all useful “ up to 5 = „strongly agree “ or „extremely useful “) and dichotomous questions (“Yes” or “No”).ResultsSurgeons rated the models as useful during surgery (4.0 out of 5) and for patient communication (4.7 out of 5). Furthermore, they believed that the models had the potential to revise the patients’ treatment plan (3.7 out of 5). Pathologists evaluated with mean ratings of 3.0 out of 5 for the usefulness of the models in diagnostic reporting and macroscopic evaluation. Radiologist acknowledged the possibility of providing additional information compared to imaging alone (3.7 out of 5). Radiation oncologists strongly supported the concept by rating the models highly for understanding patient-specific pathological characteristics (4.3 out of 5), assisting interprofessional communication (mean 4.3 out of 5) and communication with patients (4.7 out of 5). They also found the models useful for improving radiotherapy treatment planning (4.3 out of 5).ConclusionThe study revealed that the 3D printed models were generally well-received by all medical disciplines, with radiation oncologists showing particularly strong support. Addressing the concerns and tailoring the use of 3D models to the specific needs of each medical speciality will be essential for realizing their full potential in clinical practice.

Modelling systematic anatomical uncertainties of head and neck cancer patients during fractionated radiotherapy treatment

Objective. Head and neck cancer patients experience systematic as well as random day to day anatomical changes during fractionated radiotherapy treatment. Modelling the expected systematic anatomical changes could aid in creating treatment plans which are more robust against such changes. Approach. Inter- patient correspondence aligned all patients to a model space. Intra- patient correspondence between each planning CT scan and on treatment cone beam CT scans was obtained using diffeomorphic deformable image registration. The stationary velocity fields were then used to develop B-Spline based patient specific (SM) and population average (AM) models. The models were evaluated geometrically and dosimetrically. A leave-one-out method was used to compare the training and testing accuracy of the models. Main results. Both SMs and AMs were able to capture systematic changes. The average surface distance between the registration propagated contours and the contours generated by the SM was less than 2 mm, showing that the SM are able to capture the anatomical changes which a patient experiences during the course of radiotherapy. The testing accuracy was lower than the training accuracy of the SM, suggesting that the model overfits to the limited data available and therefore, also captures some of the random day to day changes. For most patients the AMs were a better estimate of the anatomical changes than assuming there were no changes, but the AMs could not capture the variability in the anatomical changes seen in all patients. No difference was seen in the training and testing accuracy of the AMs. These observations were highlighted in both the geometric and dosimetric evaluations and comparisons. Significance. In this work, a SM and AM are presented which are able to capture the systematic anatomical changes of some head and neck cancer patients over the course of radiotherapy treatment. The AM is able to capture the overall trend of the population, but there is large patient variability which highlights the need for more complex, capable population models.

Artificial intelligence in total and unicompartmental knee arthroplasty

The application of Artificial intelligence (AI) and machine learning (ML) tools in total (TKA) and unicompartmental knee arthroplasty (UKA) emerges with the potential to improve patient-centered decision-making and outcome prediction in orthopedics, as ML algorithms can generate patient-specific risk models. This review aims to evaluate the potential of the application of AI/ML models in the prediction of TKA outcomes and the identification of populations at risk.An extensive search in the following databases: MEDLINE, Scopus, Cinahl, Google Scholar, and EMBASE was conducted using the PIOS approach to formulate the research question. The PRISMA guideline was used for reporting the evidence of the data extracted. A modified eight-item MINORS checklist was employed for the quality assessment. The databases were screened from the inception to June 2022.Forty-four out of the 542 initially selected articles were eligible for the data analysis; 5 further articles were identified and added to the review from the PUBMED database, for a total of 49 articles included. A total of 2,595,780 patients were identified, with an overall average age of the patients of 70.2 years ± 7.9 years old. The five most common AI/ML models identified in the selected articles were: RF, in 38.77% of studies; GBM, in 36.73% of studies; ANN in 34.7% of articles; LR, in 32.65%; SVM in 26.53% of articles.This systematic review evaluated the possible uses of AI/ML models in TKA, highlighting their potential to lead to more accurate predictions, less time-consuming data processing, and improved decision-making, all while minimizing user input bias to provide risk-based patient-specific care.

Evaluation of enhanced external counterpulsation for diabetic foot based on a patient-specific 0D-1D cardiovascular system model

Background and objectiveDiabetic foot (DF) complications often lead to severe vascular issues. This study investigated the effectiveness of enhanced external counterpulsation (EECP) and its derived innovative compression strategies in addressing poor perfusion in DF. Although developing non-invasive and efficient treatment methods for DF is critical, the hemodynamic alterations during EECP remain underexplored despite promising outcomes in microcirculation. This research sought to address this gap by developing a patient-specific 0D-1D model based on clinical ultrasound data to identify potentially superior compression strategies that could substantially enhance blood flow in patients with DF complications. MethodsData were gathered from 10 patients with DF utilizing ultrasound for blood flow rate and computed tomography angiography (CTA) to identify lower limb conditions. Clinical measurements during standard EECP, with varying cuff pressures, facilitated the creation of a patient-specific 0D-1D model through a two-step parameter estimation process. The accuracy of this model was verified via comparison with the clinical measurements. Four compression strategies were proposed and rigorously evaluated using this model: EECP-Simp-I (removing hip cuffs), EECP-Simp-II (further removing the cuffs around the lower leg), EECP-Impr-I (removing all cuffs around the affected side), and EECP-Impr-II (building a loop circulation from the healthy side to the affected side). ResultsThe predicted results under the rest and standard EECP states were generally closely aligned with clinical measurements. The patient-specific 0D-1D model demonstrated that EECP-Simp-I and EECP-Impr-I contributed similar enhancement to perfusion in the dorsal artery (DA) and were comparable to standard EECP, while EECP-Simp-II had the least effect and EECP-Impr-II displayed the most significant enhancement. Pressure at the aortic root (AO) remained consistent across strategies. ConclusionsEECP-Simp-I is recommended for patients with DF, emphasizing device simplification. However, EECP-Simp-II is discouraged as it significantly diminished blood perfusion in this study, except in cases of limb fragility. EECP-Impr-II showed superior enhancement of blood perfusion in DA to all other strategies but required a more complex EECP device. Despite increased AO pressure in all the proposed compression strategies, safety could be guaranteed as the pressue remained within a safe range.

Whole-brain simulation of interictal epileptic discharges for patient-specific interpretation of interictal SEEG data

In patients with refractory epilepsy, the clinical interpretation of stereoelectroencephalographic (SEEG) signals is crucial to delineate the epileptogenic network that should be targeted by surgery. We propose a pipeline of patient-specific computational modeling of interictal epileptic activity to improve the definition of regions of interest. Comparison between the computationally defined regions of interest and the resected region confirmed the efficiency of the pipeline. This result suggests that computational modeling can be used to reconstruct signals and aid clinical interpretation.

Neural patient-specific 3D-2D registration in laparoscopic liver resection.

Augmented reality guidance in laparoscopic liver resection requires the registration of a preoperative 3D model to the intraoperative 2D image. However, 3D-2D liver registration poses challenges owing to the liver's flexibility, particularly in the limited visibility conditions of laparoscopy. Although promising, the current registration methods are computationally expensive and often necessitate manual initialisation. The first neural model predicting the registration (NM) is proposed, represented as 3D model deformation coefficients, from image landmarks. The strategy consists in training a patient-specific model based on synthetic data generated automatically from the patient's preoperative model. A liver shape modelling technique, which further reduces time complexity, is also proposed. The NM method was evaluated using the target registration error measure, showing an accuracy on par with existing methods, all based on numerical optimisation. Notably, NM runs much faster, offering the possibility of achieving real-time inference, a significant step ahead in this field. The proposed method represents the first neural method for 3D-2D liver registration. Preliminary experimental findings show comparable performance to existing methods, with superior computational efficiency. These results suggest a potential to deeply impact liver registration techniques.

On the Importance of Including Cohesive Zone Models in Modelling Mixed-Mode Aneurysm Rupture.

The precise mechanism of rupture in abdominal aortic aneurysms (AAAs) has not yet been uncovered. The phenomenological failure criterion of the coefficient of proportionality between von Mises stress and tissue strength does not account for any mechanistic foundation of tissue fracture. Experimental studies have shown that arterial failure is a stepwise process of fibrous delamination (mode II) and kinking (mode I) between layers. Such a mechanism has not previously been considered for AAA rupture. In the current study we consider both von Mises stress in the wall, in addition to interlayer tractions and delamination using cohesive zone models. Firstly, we present a parametric investigation of the influence of a range of AAA anatomical features on the likelihood of elevated interlayer traction and delamination. We observe in several cases that the location of peak von Mises stress and tangential traction coincide. Our simulations also reveal however, that peak von Mises and intramural tractions are not coincident for aneurysms with Length/Radius less than 2 (short high-curvature aneurysms) and for aneurysms with symmetric intraluminal thrombus (ILT). For an aneurysm with (L/R = 2.0), the peak moves slightly towards the origin while the peak is near the peak bulge with a separation distance of ~ 17mm. Additionally, we present three patient-specific AAA models derived directly from CT scans, which also illustrate that the location of von Mises stress does not correlate with the point of interlayer delamination. This study suggests that incorporating cohesive zone models into clinical based FE analyses may capture a greater proportion of ruptures in-silico.

A 3D-printed, dynamic, patient-specific knee simulator

Purpose3D-printed devices proved their efficacy across different clinical applications, helping personalize medical treatments. This paper aims to present the procedure for the design and production of patient-specific dynamic simulators of the human knee. The scope of these simulators is to improve surgical outcomes, investigate the motion and load response of the human knee and standardize in-vitro experiments for testing orthopedic devices through a personalized physical representation of the patient’s joint.Design/methodology/approachThis paper tested the approach on three volunteers. For each, a patient-specific mathematical joint model was defined from an magnetic resonance imaging (MRI) of the knee. The model guided the CAD design of the simulators, which was then realized through stereolithography printing. Manufacturing accuracy was tested by quantifying the differences between 3D-printed and CAD geometry. To assess the simulator functionality, its motion was measured through a stereophotogrammetric system and compared with the natural tibio-femoral motion of the volunteers, measured as a sequence of static MRI.FindingsThe 3D-printing accuracy was very high, with average differences between ideal and printed parts below ± 0.1 mm. However, the assembly of different 3D-printed parts resulted in a higher average error of 0.97 mm and peak values of 2.33 mm. Despite that, the rotational and translational accuracy of the simulator was about 5° and 4 mm, respectively.Originality/valueAlthough improvements in the production process are needed, the proposed simulators successfully replicated the individual articular behavior. The proposed approach is general and thus extendible to other articulations.

Patient-specific deep learning for 3D protoacoustic image reconstruction and dose verification in proton therapy.

Protoacoustic (PA) imaging has the potential to provide real-time 3D dose verification of proton therapy. However, PA images are susceptible to severe distortion due to limited angle acquisition. Our previous studies showed the potential of using deep learning to enhance PA images. As the model was trained using a limited number of patients' data, its efficacy was limited when applied to individualpatients. In this study, we developed a patient-specific deep learning method for protoacoustic imaging to improve the reconstruction quality of protoacoustic imaging and the accuracy of dose verification for individualpatients. Our method consists of two stages: in the first stage, a group model is trained from a diverse training set containing all patients, where a novel deep learning network is employed to directly reconstruct the initial pressure maps from the radiofrequency (RF) signals; in the second stage, we apply transfer learning on the pre-trained group model using patient-specific dataset derived from a novel data augmentation method to tune it into a patient-specific model. Raw PA signals were simulated based on computed tomography (CT) images and the pressure map derived from the planned dose. The reconstructed PA images were evaluated against the ground truth by using the root mean squared errors (RMSE), structural similarity index measure (SSIM) and gamma index on 10 specific prostate cancer patients. The significance level was evaluated by t-test with the p-value threshold of 0.05 compared with the results from the groupmodel. The patient-specific model achieved an average RMSE of 0.014 ( ), and an average SSIM of 0.981 ( ), out-performing the group model. Qualitative results also demonstrated that our patient-specific approach acquired better imaging quality with more details reconstructed when comparing with the group model. Dose verification achieved an average RMSE of 0.011 ( ), and an average SSIM of 0.995 ( ). Gamma index evaluation demonstrated a high agreement (97.4% [ ] and 97.9% [ ] for 1%/3 and 1%/5mm) between the predicted and the ground truth dose maps. Our approach approximately took 6 s to reconstruct PA images for each patient, demonstrating its feasibility for online 3D dose verification for prostate protontherapy. Our method demonstrated the feasibility of achieving 3D high-precision PA-based dose verification using patient-specific deep-learning approaches, which can potentially be used to guide the treatment to mitigate the impact of range uncertainty and improve the precision. Further studies are needed to validate the clinical impact of thetechnique.

Increased prevalence of valgus constitutional alignment subtypes in a South African arthritic population group using the coronal plane alignment of the knee (CPAK) classification

BackgroundKnee alignment philosophies and patient specific models to improve patient reported outcomes are gaining increasing attention. The coronal plane alignment of the knee (CPAK) classification describes nine knee phenotypes and then proposes surgical alignment strategies to achieve constitutional alignment. The CPAK classification has been validated in Australian, European, Asian and North American population groups. To date no African data has been analyzed using CPAK. MethodsA total of 344 arthritic patients (608 knees) with appropriate long leg radiographs were classified based on the CPAK type. Measurements included mechanical hip-knee-angle(mHKA), medial proximal tibial angle (mMPTA) and lateral distal femoral angle (mLDFA) and the derived calculations of joint line obliquity (JLO) and arithmetic hip-knee-angle (aHKA). ResultsThe sample population was 77.9% (n = 268) female with a mean age of 68.4 ± 9.2 years. The most common CPAK types in order were type 3 (n = 174; 28.6%), type 2 (n = 155; 25.5%), type 1 (n = 94; 15.5%) and type 6 (n = 80; 13.2%). The most common limb alignment types were valgus (CPAK types 3,6,9; 41.8%). ConclusionThis study, which investigated arthritic patients from a single institution in South Africa, shows a divergence of CPAK phenotypic knee patterns relative to other international studies, with much higher proportions of valgus phenotypes (3 and 6). This regional difference should be further investigated in other South African and African population samples and used to adapt the surgical strategies employed by local surgeons.