Methods for medical applications

Background: In recent years, the growing adoption of Virtual Reality (VR) and 3D printing technologies has revolutionized surgical training by providing innovative opportunities for hands-on education. This study investigates the combined use of VR and 3D printed personalized anatomical models and cutting guides within the field of oral and maxillofacial oncologic surgery. Materials and methods: A mandibular tumour case was developed using the proposed approach, integrating both virtual and physical tools. Feedback was gathered from twelve surgical residents regarding their understanding of the case, the effectiveness of the immersive and three-dimensional technologies, and their overall satisfaction with the training experience. Results: Participants reported enhanced comprehension of complex surgical scenarios and valued the practical utility of the VR simulator combined with 3D printed models. The immersive environment facilitated skill acquisition in a risk-free setting. Conclusion: The findings underscore the significant added value of integrating VR and 3D printing technologies in surgical training, preparation, and simulation. This approach offers a safe, effective training platform that improves readiness for complex procedures in oncologic surgery and has the potential to be extended to other branches of maxillofacial surgery.

Keywords: fibula free flap , mandibular reconstruction , maxillofacial surgery , surgical education , three-dimensional printing , user-centered design , virtual reality

Articulated total body models have proved to be a useful tool for multibody analyses focused on human body behaviour. Historically, they have found wide applications in vehicle crash analyses, while the respective use in forensic biomechanics is less common and is hampered by the use of generic, simplified geometries and mechanical properties. The article introduces one advancement in these models aimed to replicate the outer geometry more faithfully as this aspect is crucial whenever the expected scenario includes one or more impacts between the human body and the environment. This accurate replication of the outer geometry has been made possible by the development of statistical shape models of the human body which can be shaped according to a limited set of anthropometric measurements. The anthropomorphic model was the basis to create a subject-specific articulated total body android, whose behaviour was validated against experimental tests obtained on cadavers, simulating pedestrian-to-car impacts. The correspondence between numerical and experimental data was checked making use of metrics (EARTH and EEARTH) specifically developed for time histories comparison. This activity has led to the optimization of contact parameters, differentiating the behaviour of the head versus all other body segments. On the whole, a validated model for forensic applications has been set up. This model is characterised by a high geometric accuracy and can be used, for example, to analyse fall scenarios. Further effort will be dedicated to set up a methodology for subject-specific parametrization of joint properties in terms of range of movement and passive stiffness.

Keywords: Articulated Total Body (ATB) , Contact properties , Crash tests , Forensic biomechanics , Multibody analysis , Time histories

Background and objective Three-dimensional (3D) augmented reality (AR) and artificial intelligence (AI) technologies have recently been introduced to enhance guidance during robot-assisted radical prostatectomy (RARP). By overlaying virtual and real-time images, this approach helps accurately localize hidden lesions during surgery, enabling the execution of tailored procedures. This study aimed to evaluate whether 3D-AI-AR guidance reduces positive surgical margins (PSMs) compared with standard tw0-dimensional (2D) magnetic resonance imaging (MRI)-based interventions. Methods In this prospective, multicenter randomized controlled trial (NCT06318559), 133 patients with extracapsular extension or bulging at preoperative MRI were enrolled and randomized (2:1) to either 2D MRI-guided ( n = 84) or 3D-AI-AR–guided RARP ( n = 49). All the patients underwent nerve-sparing RARP. Intraoperative selective biopsies were then performed at the level of the preserved neurovascular bundle (NVB): cognitive in the MRI group and AR guided in the 3D group. The primary outcomes included PSM rate. Prostate-specific antigen (PSA) levels, continence, and potency recovery were assessed during the 12 mo of follow-up. The use of postoperative radiotherapy was recorded. Biochemical recurrence (BCR) was defined as PSA >0.4 ng/ml. All the analyses were conducted with SAS Statistics Software v.9.4. Key findings and limitations Baseline and intraoperative characteristics were similar between the groups. While PSMs on prostate surface were comparable ( p = 0.8), 3D-guided excisional biopsies had a significantly higher positivity rate (52% vs 13%; p = 0.001), allowing an improved margin control. The 3D group had a lower overall PSM rate (22% vs 39%; p = 0.047), required less postoperative RT (18% vs 35%; p = 0.046), and showed higher continence at 12 mo (91% vs 71%; p = 0.03). Potency and BCR rates were similar. Conclusions and clinical implications The execution of a 3D-AI-AR–guided biopsy at the level of preserved NVBs during nerve-sparing RARP allows correct identification of the tumor with subsequent improvement of margin control. Longer follow-up is required to assess the functional and long-term oncological outcomes of this approach.

Keywords: Artificial intelligence , Augmented reality , Prostate cancer , Radical prostatectomy , Three-dimensional model

Mandibular reconstruction, particularly with temporomandibular joint (TMJ) involvement, is a challenging procedure where precise evaluation of condylar displacement is critical for assessing functional and anatomical outcomes. The complexity of this evaluation stems from anatomical variability, surgical techniques, and post-surgical changes, making consistent and reliable assessments difficult. To address this issue, we developed an innovative, standardized procedure for the evaluation of condylar displacement following mandibular reconstruction. The proposed procedure starts from biomedical image analysis and integrates precise segmentation with an innovative standardized alignment technique, ensuring reproducibility across different reconstructive approaches, regardless of the surgical method used. This procedure was validated through a retrospective study of 18 patients, employing a consistent 3D model reconstruction and analysis workflow. The results demonstrate that the developed procedure provides a reliable framework for comparing different reconstructive methods, regardless of whether they employ CAD/AM techniques or conventional approaches, offering a repeatable and adaptable approach for assessing mandibular reconstruction outcomes. This methodology lays the groundwork for future improvements in surgical planning and post-operative evaluation, ultimately contributing to the optimization of both functional and aesthetic results in mandibular reconstruction.

Keywords: 3D model reconstruction , condylar displacement evaluation , mandibular reconstruction , standardized alignment procedure

Background: Pediatric surgical tracheotomy is a high-risk procedure demanding specialized training due to the unique anatomical and physiological characteristics of children. A key challenge in surgical education is the limited availability of high-fidelity simulators that provide a safe environment for skill development. Methods: We developed an innovative pediatric task trainer for surgical tracheotomy using 3D printing and silicone casting. This simulator features a modular design with a replaceable cartridge replicating critical anatomical structures, including the trachea, thyroid gland, cervical fascia, vessels, and musculature. To enhance realism, it also simulates bleeding upon incision. Results: Expert physicians evaluated the trainer's anatomical accuracy, procedural realism, and overall training effectiveness. Feedback was highly positive, confirming that the device closely replicates pediatric tracheotomy conditions and offers a realistic training experience. Conclusion: The positive expert feedback highlights the simulator's potential as a valuable tool for improving surgical proficiency and reducing the risk of complications in live procedures. This task trainer represents a significant advancement in pediatric airway management training and an essential asset for surgical simulation.

Keywords: 3D printing , CAD modeling , Clinical simulation , Pediatric task trainer , Surgical tracheotomy

This paper presents the design and preliminary evaluation of a mobile application developed to support home-based rehabilitation for patients recovering from cardiac surgery. The application uses the smartphone's built-in RGB camera in conjunction with MediaPipe's pose landmark detection to track body articulations and monitor exercise execution. A rule-based approach, driven by landmark positions, was implemented to evaluate user poses during the exercises. Based on this evaluation, the system tracks progression through a predefined sequence of states that represent correct execution. This method enables automatic repetition counting and the detection of common execution errors. Real-time visual and auditory feedback is provided, effectively simulating a virtual coach that guides patients through their rehabilitation routines. The application was tested with a specific rehabilitation exercise and demonstrated good reliability in both repetition counting and error detection. Although some limitations related to depth estimation by the smartphone camera were observed, the system shows promise as an accessible tool for guided rehabilitation, without the need for specialized equipment or clinical supervision, and has the potential to enhance patient engagement and adherence to rehabilitation protocols.

Keywords: Cardiac Rehabilitation , MediaPipe , Motion Capture , Pose Detection , Telemedicine

Background: Mandibular reconstruction, with temporomandibular joint (TMJ) involvement, is a challenging procedure where precise evaluation of condylar displacement is critical for assessing functional and aesthetic outcomes. The complexity of this evaluation stems from anatomical variability, surgical techniques, and post-surgical changes, making consistent and reliable assessments difficult. This study assessed condylar displacement following mandibular reconstruction using a standardized protocol for reproducibility. Methods: A retrospective analysis of patients treated at AOU Careggi, Florence (2019–2023), was conducted, with preoperative, immediate postoperative, and long-term CT scans analyzed across four groups: customized microvascular bone flaps, conventional bone flaps, customized plate implants, and soft tissue-only reconstructions. Results: Customized bone flaps and plate implants minimized condylar displacement, while conventional bone flaps had initial misalignment, stabilizing over time. Soft tissue-only reconstructions led to complete condylar resorption after two years. Conclusion: The study introduced a standardized protocol for assessing condylar shifts, highlighting the advantages of CAD/CAM-based techniques in preserving TMJ integrity, and suggesting further research for refining reconstructive strategies.

Keywords: CAD-CAM technology , Condylar displacements , Fibula free flap , Mandibular reconstruction , Temporomandibular joint

Background: Public Speaking Anxiety (PSA) is a form of social anxiety spread among university students, negatively affecting both academic performance and overall psychological well-being. Virtual Reality (VR)–based interventions combined with Cognitive Behavioral Therapy (CBT) represent a promising approach for treating PSA, as they enable gradual and controlled exposure to feared social situations by simulating realistic audiences and environments. The primary aim of this study is to evaluate whether the use of VR as a support to standard psychotherapy is effective in managing anxiety and PSA in university students who access the University Psychological Counseling Service (UPCS). Methods: This article outlines the study protocol for a randomized controlled trial (RCT). Participants will be divided into two groups: (1) a control group receiving psychological counseling, and (2) an experimental group that receives psychological counseling integrated with VR interventions based on 360° video scenarios. The intervention will include two VR-based modules: a VR–Exposure and Response Prevention (ERP) module, designed to provide graded immersive exposure to anxiety-provoking scenarios, and a VR–Acceptance and Commitment Training (ACT) module, aimed at enhancing mindfulness and psychological flexibility through guided experiential practice. Psychological and physiological data collected during the sessions will be analyzed to compare the effectiveness of VR-integrated psychotherapy with traditional counseling alone. Discussion: This study will introduce a novel VR-based ERP and ACT training protocol designed to reduce public speaking anxiety in university students by integrating innovative, immersive VR scenarios within a psychological counseling context. The proposed approach may be particularly relevant for university settings, as it offers a scalable, standardized, and controllable VR-supported counseling protocol aimed at reducing PSA, enhancing students' academic functioning (e.g., oral exams and presentations), and strengthening the overall effectiveness of university psychological counseling services. Trial registration: This protocol was registered at the Clinical Trials.gov (Identifier: NCT07351409) on January 16, 2026.

Keywords: Clinical Trial , Psychological Counseling , Public Speaking Anxiety , User studies , Virtual Reality

Purpose: To compare semiquantitative striatal indices produced by two widely used quantification platforms — BasGanV2™ and NeuroTrans3D (Oasis

Keywords: BasGanV2 , Dopamine transporters , Movement disorders , Neurology , Oasis© , Parkinson's disease , Personalized medicine , Quantitative analysis , Rehabilitation , ¹²³I-Ioflupane

As the global population continues to age, fall prevention has become a critical priority for preserving autonomy and quality of life among older adults. In response to the growing need for effective risk assessment tools, this study presents a methodological framework for the development of an interpretable and statistically robust fall risk score targeted at community-dwelling elderly individuals. The approach integrates multimodal data including clinical, stabilometric, and anamnestic information, and applies factor analysis to extract eight latent factors that capture the underlying structure of fall-related variables. Subsequently, Linear Discriminant Analysis (LDA) was employed to identify the combinations of latent factors most effective in classifying fallers and non-fallers. The best classification performance, considering a set of N = 26 older adults, was achieved using Factors 2, 5, 6, and 8, yielding an accuracy of 88%, sensitivity of 67%, and specificity of 95%. Protective factors against fall risk include mobility and balance capacity, younger age, and absence of respiratory problems or smoking habits. Conversely, factors contributing to increased fall risk include impaired balance and postural control, reduced muscle mass (e.g., sarcopenia), and physical conditions that affect gait quality without significantly affecting functional balance, such as in the early stages of Parkinson's disease. These results enabled the formulation of a continuous composite fall risk score that combines predictive performance with clinical interpretability, thus overcoming a common limitation of current sensor-based and machine learning models. Despite its promising accuracy and high specificity, the tool exhibits moderate sensitivity, indicating that it should not replace a comprehensive clinical assessment during the physiatry visit. Instead, it is intended as a complementary support tool for clinicians, serving as an initial screening indicator to help identify people who may benefit from more in-depth evaluation. Future research will focus on expanding the dataset and incorporating additional sensing modalities, such as inertial measurement units, to enhance model generalizability and sensitivity.

Keywords: Fall prediction , Healthy and active ageing , Longitudinal follow-up , Older adults , Sensors , Statistical analysis

In tumor resection surgery, an approach to the reconstruction of the bone anatomy, after removal of the tumor, is represented by allografts. A good match between the donor and recipient is crucial in enhancing integration and minimizing failure rate. However, the recipient bone is often altered by the tumor, making the comparison unreliable. Moreover, evaluating multiple donors is often a complex and time-consuming process. Thus, in this work, an algorithm is proposed that is able to automatically reconstruct the virtual healthy bone anatomy of the recipient and to use this reconstruction to select the most suitable donor. The proposed algorithm first reconstructs the healthy anatomy by aligning the bone to the mirrored contralateral using the Iterative Closest Point (ICP) algorithm. Then, to select the optimal donor, the algorithm queries a digital bone bank by aligning the reconstructed template to donor candidates using a localized search strategy. Finally, L1-Chamfer Distance and a newly devised metric called Surface Distance are computed to evaluate the similarity and assess the optimal donor. The algorithm was validated on a dataset of 49 healthy femurs by creating 980 simulated resections. A retrospective study on two real cases was also conducted. The algorithm achieved a median L1-Chamfer Distance of 0.7217 mm and a Surface Distance of 0.3225 mm for the reconstruction of the healthy anatomy and 0.8621 mm and 0.5338 mm, respectively, for the best donor. The retrospective studies showed how the algorithm was able to outperform the manual procedure in both time and accuracy of the allograft. These case studies highlighted how the algorithm performs efficiently even in real case scenarios, enhancing the shape matching between the recipient and donor bone and thus possibly improving post-operative outcomes.

Additive manufacturing (AM) enables customized, efficient restorative workflows, though the accuracy of 3D-printed restorations may be compromised by polymerization, sintering shrinkage, and post-processing. This study evaluated the geometric accuracy of 3D-printed partial restorations compared with the computer-aided design (CAD) reference. The null hypothesis stated that no significant differences would be found between Varseo Smile Crownplus (by BEGO, Italy) and IRIXMax (by DWS System, Italy) materials, which are printed and cured with different technologies. A model was prepared for an overlay and designed with a 1.5 mm uniform thickness. Restorations were produced in two groups with two different printing processes: DLP (digital light processing)-printed Varseo Smile Crownplus and SLA (stereolithography)-printed IRIXMax. Six samples per group were printed at 90° orientation and scanned. Meshes were aligned to the master geometry via pre-alignment and ICP (Iterative Closest Point) registration. Deviations were quantified in CloudCompare using mean, standard deviation (SD), and 90th percentile values. IRIXMax showed the lowest deviations from the ideal geometry, while Varseo Smile Crownplus exhibited greater variability. Pairwise comparisons found IRIXMax significantly more accurate than Varseo Smile Crownplus. Color maps confirmed material-specific deviation patterns. IRIXMax provided the highest geometric accuracy. Material-specific calibration is essential for reliable 3D-printed definitive restorations.

Keywords: 3D printing , dental restorations , deviation analysis geometric accuracy , internal gap , marginal fit , resin-based composites , trueness

This study investigates the role of Smart Home technologies in promoting personal autonomy among elderly people, with a focus on Ambient Assisted Living (AAL) systems. Adopting a multidimensional view of autonomy that integrates physical, cognitive, emotional, and social domains, the paper synthesizes findings from systematic reviews and meta-analyses published between 2020 and 2025. The review explores five key research questions: the impact of home automation on daily functioning (ADL/IADL), methodologies for measuring autonomy, types of data collected, benefits and limitations of smart systems, and the extent to which research reflects a holistic understanding of autonomy. The primary contribution of this study lies in synthesizing current review literature and identifying patterns, consistencies, and methodological limitations. Findings indicate that while smart technologies effectively support basic daily activities and enhance safety and independence, their effectiveness depends on personalization, inclusivity, and alignment with users\' real-life contexts. However, current research often overlooks the emotional and relational dimensions of aging, and many studies reveal inconsistent assessment methods, due to a lack of standardized tools, and limited incorporation of usercentered, cultural, or experiential data.

Keywords: ADL/IADL , Ambient assisted living , Assistive technologies , Disability first section , Elderly , Home automation system , Personal autonomy , Sensor-based monitoring

The Fused Deposition Modeling (FDM) process often produces parts with high surface roughness, limiting their end-use applications, especially in the biomedical field. This paper presents an experimental study on improving the surface finish of 3D-printed polycaprolactone (PCL) samples using a robotic burnishing process. A key innovation is the development of a low-cost sensorless setup using a 5-DOF manipulator, which controls the applied force by correlating a precise robotic displacement with the known stiffness of springs via Hooke's law. Ten PCL samples were tested using two burnishing directions: 90° (perpendicular) and 0° (parallel) relative to the printing orientation. The as-printed samples showed a highly anisotropic surface. The 90° trajectory (group 1) proved to be more effective in reducing primary roughness ((Formula presented.)), lowering the mean (Formula presented.) from (Formula presented.) to (Formula presented.) (a mean reduction of 29.9%). In contrast, the 0° trajectory (group 2) was more effective in reducing roughness (Formula presented.), lowering its mean (Formula presented.) from (Formula presented.) to (Formula presented.) (a mean reduction of 34.0%). The results demonstrate that the proposed sensorless system is a valid method for surface post-processing of FDM parts when the required forces fall below a specific threshold, ensuring a significant reduction in roughness without damaging the samples. The lower surface roughness obtained with the proposed post-processing strategy may represent a promising approach for improving the surface characteristics of FDM-fabricated polymer scaffolds intended for biomedical applications.

Keywords: fused deposition modeling , indirect measurement , robotic burnishing

Hollow Fiber Membrane Bioreactors (HFMBs) support mammalian cell culture at high cell density by enabling effective transport of nutrients, gases and metabolites to/from cells and by offering high membrane surface area-to-bioreactor volume ratios, scalability and flexibility in design. A recent study showed that shell-and-tube HFMBs with cells in the extracapillary space (ECS) for medicine may be designed to mimic the bone architecture for bone tissue engineering (TE). A more transport-efficient HFMB, the BRx-HFMB, consists of a 3D stack of alternating cross-woven mats of microporous and gas-permeable hollow fiber membranes with cells in the ECS and medium flowing inside the membranes. This design was shown to support the culture of a large mass of densely packed cells with high oxygen and nutrient metabolic demands similar to bone cells. Its biomimicry and rationale for good performance are poorly characterized, hindering exploitation of its characteristics for bone TE. Herein, we report the architectural and biomimetic characterization of pore/void distribution in the ECS of laboratory BRx-HFMBs using non-invasive non-destructive micro-computed tomography and advanced image analysis to minimize artifacts. The results suggest that the ECS pore architecture and specific surface area of BRx-HFMBs mimic those of bone tissue, favoring cell migration around, and adhesion on, membranes at clinical cell densities. The membrane network architecture enables cell perfusion with medium, and membranes act as spatially distributed oxygen sources promoting oxygen transport through the cell construct over longer distances than in static bioreactors. This supports the use of BRx-HFMBs to develop cellular models of bone tissue for drug testing and precision medicine. This journal is

Non-invasive assessment of spinal morphology traditionally relies on subjective manual palpation or data-driven deep learning models that often lack metrological interpretability. This study presents a fully automated, purely geometric framework for the localization and morphometric characterization of spinous processes directly from 3D dorsal surface scans. Leveraging continuous differential geometry and the scale-invariant properties of the shape index, the proposed algorithm operates independently of training data, physical markers, and ionizing radiation. Validation against expert clinical palpation demonstrated high spatial reliability, yielding a mean spatial deviation of 3.7 mm and a 98.6% Confidence Index in matching manually identified landmarks. Notably, the computational framework exhibited heightened morphological sensitivity, consistently extracting mathematically valid geometric signatures in spinal regions conservatively bypassed by the clinician. This resulted in a theoretical algorithmic False Negative Rate bounded between 13.5% and 14.6%, compared to a 33.1% manual rate. Furthermore, the system autonomously evaluated morphometric parameters, such as basal width, symmetry index, and local apical prominence, successfully classifying 327 apophyses into “prominent” (19%) and “masked” (81%) morphotypes across complex, soft-tissue-mediated topographies. While these findings highlight the method's capability to standardize and objectify dorsal surface assessment, definitive anatomical fidelity requires future radiological correlation. Ultimately, this robust topographic profiling lays the foundation for autonomously quantifying structural asymmetries in clinical spinal deformities.

Keywords: 3D surface topography , Automated detection , Differential geometry , Morphometrics , Non-invasive spinal assessment , Spinous process localization

Binder Jetting is an innovative Additive Manufacturing process that was employed to produce 17-4PH steel components for orthopedic surgery applications, starting from pre-alloyed powders. Poor surface finishing of as-printed components often requires post-processing to comply with biomedical requirements. Mass finishing is a widely applied technology to lower roughness by means of mechanical abrasion. This study optimized a centrifugal disk machine mass finishing process for 17-4PH components produced by Binder Jetting. Surface printing orientation, abrasive paste composition, and precipitation hardening thermal treatment effects were investigated. Sample morphology was characterized by optical and scanning electron microscopy, roughness was measured by contact and confocal profilometry, material removal rate was estimated by mass loss measurements, and surface chemical composition was investigated by energy-dispersive X-ray spectroscopy. SiC, Al2O3, and SiO2 containing abrasive pastes effectively reduced roughness across all printing orientations. Abrasive smoothening effectiveness was correlated with the material removal rate. SiC-based paste resulted in the highest roughness reduction, reaching an average Ra of 0.62 ± 0.25 μm after 4 h of treatment. Traces of mass finishing abrasive pastes were observed on the polished surfaces, not precluding biomedical compatibility for orthopedic surgical applications. Upon precipitation hardening treatment, Vickers hardness increased by 42%. As a consequence, the material removal rate and roughness reduction were slowed down. Experimental results indicated that performing the mass finishing process before thermal aging allows roughness requirements to be met in a shorter time.

Keywords: Additive manufacturing , Binder jetting , Mass finishing , Surgical instruments

The fabrication of porous scaffolds with continuous pore size gradients is growing in interest in tissue engineering, as graded architectures better mimic the structural heterogeneity of native interfacial tissues. Conventional strategies based on monolayer scaffolds fail to reproduce this heterogeneity, while multilayer constructs introduce interfaces that do not ensure continuous transitions in structural and mechanical properties. Although most gradient fabrication approaches rely on multistep or post-processing techniques, limited attention has been focused to procedures allowing to obtain directly the final scaffold. Thermally induced phase separation is a versatile method for regulating pore morphology in scaffold by controlling the thermal history. This work investigates a TIPS-based strategy to generate continuous pore size gradients by tailoring heat transfer directionality. Poly-L-lactic acid scaffolds were produced using two process configurations differing in the presence or absence of thermal insulation and analysed using a combined experimental and numerical approach. Scaffold morphology was characterised by scanning electron microscopy, while thermal transient simulations were performed to investigate temperature evolution during phase separation and freezing. The analysis highlights differences in cooling behaviour between the two configurations, correlating these with variations in pore size distribution across the scaffold thickness. The results demonstrated that it is possible to obtain scaffold with a pore size gradient through a one-step TIPS procedure.

Background and Objective: Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, accounting for approximately 20.5 million deaths annually, nearly one-third of all global deaths. Despite its central role in cardiology, echocardiographic assessment remains subject to significant inter- and intra-observer variability, particularly based on manual frame selection and segmentation. This limitation has driven increasing interest in Deep Learning (DL) solutions capable of enabling more objective, reproducible, and efficient analyses. Methods: This paper introduces CardioSmartAssist, a deep learning framework for automatic left ventricular segmentation and multi-cycle ejection fraction estimation relying solely on echocardiographic videos. The system integrates frame-by-frame segmentation visualisation, anomaly detection, and volume tracking to enhance clinical usability. Moreover, a key feature is its continuous learning mechanism, which allows clinician-corrected segmentations to be stored and used for progressive model refinement. Results: The framework is based on a MultiResUNet architecture, trained on public (EchoNet-Dynamic) and proprietary (CardioSmartSet) datasets, achieving Dice Coefficient Scores of 0.9328 and 0.9189, respectively. On the held-out test set, the EF estimated by the system showed a mean absolute difference of 10% compared with clinically reported EF values, which is lower than the typical inter-operator variability of approximately 13%. Conclusion: CardioSmartAssist resulted to be a promising tool for consistent cardiac evaluations, improving access to diagnostics, and enhancing clinical decision-making through smart assistance.

Keywords: AI-assisted diagnosis , Artificial intelligence , Clinical decision support systems , Computer-aided diagnosis , Deep learning , Left ventricular ejection fraction

Augmented Reality (AR) is increasingly being adopted in surgical procedures. Identifying accurate and reliable tracking systems can enhance the effectiveness of AR assisted surgery. The study presents a benchmarking platform to evaluate the performance of different optical surgical tools tracking systems for AR applications, addressing the need for standardized comparison of tracking systems' accuracy, repeatability, and reliability. A custom-built, cost-effective benchmarking platform was developed, and different quantitative evaluation metrics were employed to assess performances of Marker-Based (MB) tracking systems. Two types of measurements were performed: static, where metrics such as Target Registration Error (TRE) and tip stability as a measure of jitter were estimated, and dynamic, involving a measure of the deviation between the tracked tooltip trajectory and its GT. Three AR MB-tracking methods were tested: method 1, mono-RGB sensor tracking a planar image target; method 2, RGB-depth sensor tracking passive spherical markers; and method 3, mono-IR sensor tracking IR active markers. Significant performance differences were observed. Method 3 achieved the lowest TRE value of Method 1 exhibited the best tip stability with a jitter value of Method 2 obtained the minimum trajectory distance in dynamic tests. The benchmarking platform demonstrated its effectiveness in evaluating and comparing different AR tracking methods. This study enables the optimization of tracking system selection for use in the operating room, and facilitating the integration of AR as a supportive technology in surgical practice.

Keywords: Augmented reality , Augmented surgery , Marker-based , Pose estimation , RealSense , Tooltip tracking