Methods for medical applications

Immune checkpoint inhibitors (ICIs) have substantially improved outcomes in advanced melanoma but are frequently linked to immune-related adverse events (irAEs). Vitiligo is a common cutaneous irAE and has been consistently associated with improved patient outcome, including prolonged progression-free and overall survival. It also represents significant visual stigma, particularly when the face is involved. Traditional treatment comprises topical steroids, calcineurin inhibitors, laser, and phototherapy which often have insufficient effects. Since 2023, the first approved drug for non-segmental vitiligo (NSV) with facial involvement, the topical Janus kinase inhibitor ruxolitinib, has been available. However, experience with its use in ICI-induced vitiligo remains limited. In this exploratory analysis, three patients who developed facial vitiligo following ICI therapy applied 1.5% ruxolitinib cream to affected facial areas twice daily. After six (two patients), and twelve months (one patient), extensive repigmentation was observed, quantified at 95.7%, 78.9%, and 99.1% using a novel semi-automatic tool. Quality-of-life questionnaires showed mean reductions of 57.6% (Vitiligo DLQI) and 68.2% (Vitiligo-specific Quality of Life) in disease burden. Treatment was associated with substantial repigmentation without observed side effects. Further evaluation in larger, prospective cohorts is warranted to better define treatment effects, clinical applicability, and long-term safety.

Keywords: immune checkpoint inhibitor , immune-related adverse event , JAK inhibitor , melanoma , ruxolitinib , vitiligo

Effective pediatric airway management, particularly in patients with congenital anomalies, is a significant clinical challenge, complicated by the scarcity of realistic pathological training models. This study describes the design, development, and validation of a high-fidelity pediatric difficult airway simulator for training in rigid and flexible bronchoscopy. Developed by the T3Ddy Laboratory with the University of Florence and Meyer Children\'s Hospital—IRCCS, the mannequin was based on CT data from a 19-month-old patient with complex airway anomalies, using reverse engineering and additive manufacturing. The simulator adopts a modular design with interchangeable anatomical components, enabling replication of diverse clinical scenarios, including rare congenital tracheal conditions. Key features include a flexible cervical spine for dynamic head positioning, simulated lubrication and hemorrhage systems, and anatomically precise upper airway structures. The airway underwent iterative redesign to balance anatomical fidelity and manufacturability. Production combined fused deposition modeling with silicone casting in custom molds incorporating functional details. Validation occurred during a pediatric bronchoscopy training course, where structured questionnaires assessed realism, usability, and educational value. Participants reported high satisfaction, particularly with rigid bronchoscopy, while suggesting improvements in haptic feedback and fluid simulation. This work demonstrates that advanced design and manufacturing can create customizable, reusable simulators that strengthen pediatric airway management training and fill critical gaps in pathological model availability.

Keywords: 3D printing , clinical simulation , high-fidelity medical training , pediatric airway management

Percutaneous tracheotomy represents a critical airway procedure, yet training opportunities in pediatric patients remain limited due to the low incidence and high risk associated with the procedure. Simulation-based training plays a key role in enabling physicians to develop procedural skills in a safe environment. This study presents the design, fabrication, and validation of a high-fidelity physical simulator for pediatric percutaneous tracheotomy using the Frova screw technique. The simulator reproduces the anatomy of an eight-year-old patient in a hyperextended neck position, allowing rigid bronchoscope insertion, and includes relevant anatomical landmarks. The presence of a blood pocket enhances procedural realism. The modular design, with a reusable base and disposable cartridge, enables rapid reset between sessions while minimizing maintenance costs. Soft tissues were reproduced through silicone molding, while rigid components were fabricated using fused deposition modeling 3D printing. Validation was performed by 39 physicians using a structured five-point Likert scale questionnaire. The simulator achieved a mean score of 4.2/5, with item scores ranging from 3.6 to 4.7, indicating a high level of perceived realism, procedural fidelity, and educational value, as well as highlighting potential areas of improvement. These findings suggest that the proposed simulator is a useful tool for simulation-based education, with the potential to improve operator confidence and patient safety in complex airway emergencies.

Keywords: 3D printing , CAD modeling , Frova screw technique , pediatric simulator , percutaneous tracheotomy

Background: Adherence to movement precautions following sternotomy is essential for sternal healing, but patients often find it difficult to maintain the correct behavior. Methods: This study introduces a real-time computer vision-based system to evaluate movement compliance with the post-sternotomy precautions protocol. A YOLOv11 object detection model was trained using a dataset comprising real images and AI-generated images. A secondary monitoring algorithm was developed to use YOLO inference results to classify the whole action rather than a single frame. Results: The integration of synthetic data significantly enhanced YOLO model performance, achieving a mAP50 of 80.3%. In real-time validation, the monitoring algorithm correctly classified 85% of non-compliant actions without misclassifying any compliant actions. Conclusion: This work demonstrates the feasibility of a low-cost, non-invasive solution for monitoring post-sternotomy precautions. Furthermore, the use of Generative AI proved effective in overcoming data scarcity.

Keywords: Cardiac Rehabilitation , Generative Artificial Intelligence , Sternotomy

Background: Serious games (SGs) with hand-tracking solutions are increasingly adopted in neurological rehabilitation. The Leap Motion Controller (LMC) is widely used in clinical and research contexts, while software-based systems, such as MediaPipe (MP), are emerging. Objectives: This study compared the usability of MP- and LMC-based SGs. Methods: Seven SGs were developed to evaluate Grasp and Pinch gestures and tested with fifty healthy volunteers. Usability was assessed with the System Usability Scale (SUS) after each device, and an ad-hoc comparative questionnaire (LMC, MP, Neutral) collected preferences on several aspects. Results: Both systems achieved excellent SUS scores (MP: 88/100; LMC: 90/100), with no statistical difference in usability. The comparative questionnaire showed a stronger preference for LMC (63%) versus MP (17%), with 20% neutral. Conclusion: Although both solutions demonstrated high usability, LMC was more frequently preferred in direct comparison. The SUS results of MP encourage further investigation, including large-scale tests with neurological patients.

Keywords: Comparative Study , Exergaming , Hand , Motion Capture , Neurological Rehabilitation

In this retrospective study, the diagnostic performance of an individualized apparent diffusion coefficient (ADC) signal intensity (SI) scale for differentiating prostate cancer (PCa) from benign simplified Prostate Imaging Reporting and Data System (S-PI-RADS) 4 lesions detected on biparametric magnetic resonance imaging (bpMRI) was evaluated. A total of 102 patients with biopsy-confirmed PCa and 79 patients with benign lesions (inflammation, HGPIN, ASAP) presenting as S-PI-RADS 4 were included. An individualized ADC SI scale was generated for each patient using maximum and minimum ADC values from the bladder and femoral head, respectively. The midpoint (ADC_mean) and the percentage difference (PD) between ADC_mean and lesion intensity (ADC_lesion) were calculated. Differences and diagnostic performance were assessed using Mann-Whitney U tests and receiver operating characteristic (ROC) analysis. PCa lesions demonstrated significantly lower ADC values and higher PDs compared with benign lesions (P<0.001). ROC analysis of PD yielded an area under the curve (AUC) of 0.798. Using a PD cut-off of 14.67%, sensitivity and specificity for detecting PCa were 73.5% and 94.9%, respectively. Intra-reader repeatability was high [intraclass correlation coefficient (ICC) 0.92–0.94], and PD values remained robust across variations in ADC fitting. The individualized ADC SI scale provides a patient-specific quantitative measure that effectively distinguishes malignant from benign S-PI-RADS 4 lesions. This approach complements visual bpMRI assessment, demonstrates high specificity, and has the potential to reduce unnecessary biopsies. Further prospective studies are warranted to validate its clinical utility.

Keywords: apparent diffusion coefficient (ADC) , Prostate cancer (PCa) , quantitative magnetic resonance imaging (quantitative MRI)

Background: Medical simulation is relevant for training medical personnel in the delivery of medical and trauma care, with benefits including quantitative evaluation and increased patient safety through reduced need to train on patients. Methods: This paper presents a prototype medical simulator focusing on ocular and craniofacial trauma (OCF), for training in management of facial and upper airway injuries. It consists of a physical, electromechanical representation of head and neck structures, including the mandible, maxillary region, neck, orbit and peri-orbital regions to replicate different craniofacial traumas. Actuation and hydraulic systems are designed to control animatronic features and flow of simulated blood, tears, and cerebrospinal fluid. Results: Experimentally validated, the OCF simulator achieves structural and functional characteristics as close as possible to those of a human body. Conclusions: The OCF Simulator can be used as a stand-alone active simulator, it can be transported and used to train surgeons in simulated real-life scenarios. Clinical Trial Registration: The authors declare that this statement is not applicable since no clinical tests have been performed.

Keywords: craniofacial trauma , medical simulators , physician training , robotic human head

This research presents the mechanical design and performance evaluation of a novel 3-Degrees-of-Freedom (DoFs) wrist exoskeleton (W-EXOS) for orthopedic and poststroke patients' upper limbs rehabilitation. The device covers the 93.3% of the human Range of Motion (RoM), simulating the pronosupination, radioulnar deviation, and flexion-extension motion. W-EXOS is actuated through electric motors via an efficient cable transmission, having high torque/weight and torque/volume ratios. Its kinematics is a serial chain of three rotational joints with nonperpendicular axes competing at the wrist rotation center. So, the device joints are coupled but the structure is compact and with good mass distribution. Theoretical modeling allowed the study of the human wrist and the device axes matching, evaluating the RoM and torques at each joint. With the W-EXOS integrated into a rehabilitation station, the performance assessment was done using: 1) a position control test, for the device RoM validation and 2) a virtual reality serious game test, to prove the device assistance strategy during wrist motion tasks performed by healthy subjects in a typical rehabilitation session. Further, the W-EXOS handle has been replaced by a hand exoskeleton and the whole system has been mounted on a 4-DoFs shoulder-elbow exoskeleton, proving the W-EXOS integrability in multiple, highly wearable, compact, and usable, bimanual, upper limb robotic setups.

Keywords: Assistance , human-centered design , mechanical design , Range of Motion (RoM) , rehabilitation robotics , tendon/wire mechanism , virtual prototyping , wearable devices , wrist exoskeleton (W-EXOS)

Developing accurate methods for differentiating benign vs. malignant pulmonary nodules on CT is crucial for the correct management of patients referred for suspicious lung cancer. In this context deep learning by convolutional neural networks (CNN) has been gaining ground as an alternative to conventional methods based on feature engineering, although the use of CNN is often hampered by the lack of sufficiently large datasets for training. Herein we explore the effectiveness of deep features from pre-trained convolutional networks ‘off-the-shelf' to discriminate benign vs. malignant lung nodules from CT images. To this end we compare three approaches (two classic, one novel) for pseudo-colour image generation which allow the grey-scale CT data to be fed into CNN models designed for and trained on colour images. The classification performance was estimated by internal and cross-validation using two independent datasets (LIDC-IDRI and LUNGx), giving four experimental conditions altogether. Conventional radiomics features were used as baseline reference. The best accuracy achieved by deep features in the four experimental conditions was respectively 88.5%, 69.6%, 68.9% and 65.2%, that obtained through conventional features 85.5%, 60.9%, 65.2% and 63.8%.

Keywords: Convolutional neural networks , Lung cancer , Pseudo-colours , Transfer learning

Computational methods represent a powerful tool to explore biophysical phenomena occurring at small scales and hence difficult to observe through experimental setups. In detail, they can provide a support to mechanobiology, with the aim of understanding the behavior of living cells interacting with the surrounding environment. To this end, lattice models can provide a simulation framework that is highly reliable and easy to implement, even for simulations involving large deformations and topological changes during time evolution. In this review article, elastic network models for studying biological molecules are described, several lattice spring models for investigating cell behaviors are discussed, and the adoption of lattice beam models for biomimetic structures design is presented. The lattice modelling approaches could be regarded as a valuable option to conduct in-silico experiments and consolidate the emergent mechanobiology research field.

Keywords: Elastic network model , Lattice beam model , Lattice model , Lattice spring model , Simulation

One of the most common issues among the older people is falling. Sensor technologies can control the risk of people to fall and make them aware of their health status. Numerous devices are available in the market and new brands are continuously deployed making challenging the choice of the ideal monitoring device. The present study aims at providing a methodology to identify older adults at risk of falls and guide the user in the choice of the best set of technology to control and prevent the fall risk. Multidimensional health data of 121 older adults were collected, pre-processed and fed to five machine learning classifiers to automatically identify people at fall risk. A positive fall risk classification triggers a knowledge-based algorithm to identify the best matching device according to five categories (type, accessory parameters, accessory functions, technical requirements, and digital effort) and considering the user's clinical and social needs. This way, the algorithm is not constrained to a specific device but is able to handle several technologies and could be generalized to a larger pool of disease by properly training the ML classification models. Unlike common recommendation systems based on user preferences and user profiling, the proposed decision-support system does not require any a priori knowledge of the user preferences. Indeed, as a future development, it is planned to consider user's opinion obtained from usability and user experience tests to optimize the personalization of the assignment and improve the user's engagement.

Keywords: Decision Support System , Fall Risk Assessment , Older Adults , Personalized Healthcare , Sensors

Background and objective: Accurately measuring cervical vertebrae dimensions is crucial for diagnosing conditions, planning surgeries, and studying morphological variations related to gender, age, and ethnicity. However, traditional manual measurement methods, due to their labour-intensive nature, time-consuming process, and susceptibility to operator variability, often fall short in providing the objectivity required for reliable measurements. This study addresses these limitations by introducing a novel computer-based method for automatically identifying the dimensional features of human cervical vertebrae, leveraging 3D geometric models obtained from CT or 3D scanning. Methods: The proposed approach involves defining a local coordinate system and establishing a set of rules and parameters to evaluate the typical dimensional features of the vertebral body, foramen, and spinous process in the sagittal and coronal planes of the high-density point cloud of the cervical vertebra model. This system provides a consistent measurement reference frame, improving the method's reliability and objectivity. Based on this reference system, the method automates the traditional standard protocol, typically performed manually by radiologists, through an algorithmic approach. Results: The performance of the computer-based method was compared with the traditional manual approach using a dataset of nine complete cervical tracts. Manual measurements were conducted following a defined protocol. The manual method demonstrated poor repeatability and reproducibility, with substantial differences between the minimum and maximum values for the measured features in intra- and inter-operator evaluations. In contrast, the measurements obtained with the proposed computer-based method were consistent and repeatable. Conclusions: The proposed computer-based method provides a more reliable and objective approach for measuring the dimensional features of cervical vertebrae. It establishes a procedural standard for deducing the morphological characteristics of cervical vertebrae, with significant implications for clinical applications, such as surgical planning and diagnosis, as well as for forensic anthropology and spinal anatomy research. Further refinement and validation of the algorithmic rules and investigations into the influence of morphological abnormalities are necessary to improve the method's accuracy.

Keywords: Biomedical engineering , Cervical vertebrae , CT measures standardization

Design optimization through the integration of multiple functions into a single part is a highly effective strategy to reduce costs, simplify assembly, improve performance, and reduce weight. Additive manufacturing facilitates the production of complex structures by allowing parts consolidation, resulting in optimized designs, where multiple functions are integrated into a single component. This study presents a design for additive manufacturing method for integrating multiple lattice structures to achieve thermal management and shock absorption functions. The method follows modeling and simulation phases for dimensioning and optimizing solutions to deliver the design functions at different macro- and mesoscale levels. Hierarchical complexity was leveraged to design the two-levels structure in a single part, each delivering a specific function. Specifically, the external layer addresses energy absorption and thermal insulation, while the internal layer acts as a thermal battery by incorporating a phase change material. The design of a container carried by an unmanned aerial vehicle for the transport of healthcare and biological materials is presented. The container is shock-resistant and can maintain the content at 4 ± 2 °C for at least 1 h. As it operates passively without the need for additional energy-consuming devices, it is easy to operate and contributes to increased flight autonomy. A flight mission experiment for urgent transport of blood bags confirmed the capability of the container to preserve blood integrity. This case study demonstrates that the two-layer lattice structure design represents a highly efficient approach to multifunctional design optimization.

Keywords: 3D printing , cellular structure , functional integration , fused deposition modeling , gyroid , impact absorption , multi-scale design , thermal insulation , triply periodic minimal surface , unmanned aerial system

Clinical simulation is fundamental for the healthcare staff to learn and enhance their procedural skills without causing harm to the patients. Despite its importance, in literature appears a deficiency of pediatric pathological mannequins, especially those simulating difficult airway management due to the obstruction of the passage of tubes, fiberscopes, or catheters. Given the importance of simulating complex scenarios in the medical staff's training, the authors decided to realize a modular high-fidelity pathological mannequin with nasal access using reverse engineering and additive manufacturing techniques within T3Ddy, a joint laboratory between Meyer Children's Hospital of Florence and the Department of Industrial Engineering of the University of Florence. The mannequin is developed from diagnostic images of a significant 30-month-old polymalformative patient also affected by Pierre-Robin syndrome modifying the tracheobronchial tree to reproduce an abnormal status. Rigid parts and silicone cast molds are manufactured using 3D-printed ABS/ASA while platinum-cure-silicones are used for the soft ones. Meyer's anesthesiologists collaborated to the realization providing feedback during design and production. The device is evaluated with a 5-point Likert scale questionnaire and results in a useful tool for the training of procedural skills related to difficult intubation as its realism, anatomical geometry, and tactic feedback are positively evaluated.

Keywords: 3D printing , mannequin , pathological airway , simulation

Digital workflows have revolutionized dentistry, especially when it comes to fabrication of complete dentures through Computer-Aided Design and Computer-Aided Manufacturing (CAD-CAM) procedures. Digital articulators manage to simulate mandibular movements and are emerging as alternatives to mechanical articulators like the Gerber semi-adjustable model. Despite being a promising tool, digital articulators require refinement in order to grant consistent functionality and effective occlusal balance. The aim of this research is to present a semi-automated MATLAB tool designed to compare trajectories from different articulator types—digital versus analog—used in dental practice. Validation of the MATLAB tool compared to existing data demonstrates its reliability and effectiveness. Sensitivity analyses assess the tool's robustness under various settings. Results suggest optimal input parameters and settings ensuring precision. Future developments may include integrating anatomically-based reference systems and advanced metrics for rotational analysis of condylar path elements (CPEs), thereby enhancing digital dentistry potentialities. Ultimately, the semi-automated MATLAB tool represents a significant step towards improving dental occlusal analysis, bridging the gap between analog and digital methodologies and enabling comparison among these tools.

Keywords: Complete dentures , Dental articulators , Intermaxillary record , Kinematic comparison , Pathway reconstruction

Oral Squamous Cell Carcinoma is characterized by significant mortality and morbidity. Dental professionals can play an important role in its early detection, thanks to the availability of embedded smart cameras for oral photos and remote screening supported by Deep Learning (DL). Despite the promising results of DL for automated detection and classification of oral lesions, its effectiveness is based on a clearly defined protocol, on the explainability of results, and on periodic cases collection. This paper proposes a novel method, combining DL and Case-Based Reasoning (CBR), to allow the post-hoc explanation of the system answer. The method uses explainability tools organized in a protocol defined in the Business Process Model and Notation (BPMN) to allow its experimental validation. A redesign of the Faster-R-CNN Feature Pyramid Networks (FPN) + DL architecture is also proposed for lesions detection and classification, fine-tuned on 160 cases belonging to three classes of oral ulcers. The DL system achieves state-of-the-art performance, i.e., 83% detection and 92% classification rate (98% for neoplastic vs. non-neoplastic binary classification). A preliminary experimentation of the protocol involved both resident and specialized doctors over selected difficult cases. The system and cases have been publicly released to foster collaboration between research centers.

Keywords: Case-based reasoning , Convolutional neural network , Explainable artificial intelligence , Medical imaging , Oral cancer , OSCC

Skin ulcers are open wounds on the skin characterized by the loss of epidermal tissue. Skin ulcers can be acute or chronic, with chronic ulcers persisting for over six weeks and often being difficult to heal. Treating chronic wounds involves periodic visual inspections to control infection and maintain moisture balance, with edge and size analysis used to track wound evolution. This condition mostly affects individuals over 65 years old and is often associated with chronic conditions such as diabetes, vascular issues, heart diseases, and obesity. Early detection, assessment, and treatment are crucial for recovery. This study introduces a method for automatically detecting and segmenting skin ulcers using a Convolutional Neural Network and two-dimensional images. Additionally, a three-dimensional image analysis is employed to extract key clinical parameters for patient assessment. The developed system aims to equip specialists and healthcare providers with an objective tool for assessing and monitoring skin ulcers. An interactive graphical interface, implemented in Unity3D, allows healthcare operators to interact with the system and visualize the extracted parameters of the ulcer. This approach seeks to address the need for precise and efficient monitoring tools in managing chronic wounds, providing a significant advancement in the field by automating and improving the accuracy of ulcer assessment.

Keywords: 3D analysis , automatic segmentation , chronic wound , clinical parameters , convolutional neural network , edge detection , interactive interface

Targeted Radionuclide Therapy (TRT) is a medical technique exploiting radionuclides to combat cancer growth and spread. TRT requires a supply of radionuclides that are currently produced by either cyclotrons or nuclear research reactors. In this context, the ISOLPHARM project investigates the production of innovative radionuclides for medical applications. This production will be based on the forthcoming SPES facility at the Legnaro National Laboratories (LNL) of the National Institute for Nuclear Physics (INFN), an ISOL facility where high-purity radioactive beams will be used to produce carrier-free radiopharmaceuticals. Previous studies demonstrated that a significant amount of 111Ag, an innovative β/γ emitter suitable for TRT with theranostic applications, can be obtained at the SPES facility. The present work describes the first imaging study on phantoms with 111Ag performed by the ISOLPHARM collaboration. This is a fundamental step to pave the way for the upcoming in vivo studies on the 111Ag-based radiopharmaceutical currently being developed. The imaging potential of this radionuclide was investigated by acquiring phantom images with Cerenkov Luminescence Imaging (CLI) and digital autoradiography

Keywords: ¹¹¹Ag , ⁶⁸Ga , Autoradiography , Cerenkov luminescence imaging , ISOLPHARM , Radiopharmaceuticals , Targeted radionuclide therapy

Additive Manufacturing (AM) of Polyether ether ketone (PEEK) via Fused Filament Fabrication (FFF) presents a versatile and effective solution for producing complex, lightweight, and customized parts with intriguing mechanical properties. PEEK, known for its high-temperature resistance, chemical stability, and biocompatibility, finds applications in diverse fields, from aerospace component manufacturing, benefiting from its lightweight nature to biomedical engineering, requiring patient-specific implants or prostheses. However, AM of PEEK via FFF poses significant challenges, and conventional low-cost 3D printers may not always be well-equipped for this demanding task. In the present study, a low-cost solution was proposed and evaluated through simulations and experimental measurements to enhance the performance of a 3D printer valued at less than $ 6.000. The solution entails designing an accessory that utilizes the printer's second nozzle, incorporating a basic aluminum plate. Through simulations and experimental measurements, it is demonstrated that this accessory provides localized heating in the printing area, thereby increasing the average temperature and improving temperature uniformity.

Keywords: Additive Manufacturing , local heating system , low-cost equipment , PEEK

Designing customized orthodontic treatments presents a significant challenge due to the diverse and heterogeneous requirements that must be met to correct misaligned teeth and malocclusions. These requirements include specific directives from orthodontists, individual patient considerations, physiological constraints, and technological limitations. The current approach mainly involves collaboration between technicians and orthodontists utilizing specialized CAD software and implementing manual methodologies. This paper outlines the formalization of an algorithm developed to facilitate the design of these medical treatments. A mathematical model based on existing knowledge is proposed to automate the generation of a patient's final dental configuration, which serves as the foundation for producing all necessary devices for the treatment plan. The mathematical model has been validated through collaboration with orthodontists and a manufacturer specialized in orthodontic devices.

Keywords: Design method , Knowledge formalization , Orthodontic treatment , Product customization