Methods for medical applications

The left ventricle (LV) constantly changes its shape and function as a response to patho-logical conditions, and this process is known as remodeling. In the presence of aortic stenosis (AS), the degenerative process is not limited to the aortic valve but also involves the remodeling of LV. Statistical shape analysis (SSA) offers a powerful tool for the visualization and quantification of the geometrical and functional patterns of any anatomic changes. In this paper, a SSA method was devel-oped to determine shape descriptors of the LV under different degrees of AS and thus to shed light on the mechanistic link between shape and function. A total of n = 86 patients underwent computed tomography (CT) for the evaluation of valvulopathy were segmented to obtain the LV surface and then were automatically aligned to a reference template by rigid registrations and transformations. Shape modes of the anatomical LV variation induced by the degree of AS were assessed by principal component analysis (PCA). The first shape mode represented nearly 50% of the total variance of LV shape in our patient population and was mainly associated to a spherical LV geometry. At Pearson's analysis, the first shape mode was positively correlated to both the end-diastolic volume (p < 0.01, R = 0.814) and end-systolic volume (p < 0.01, and R = 0.922), suggesting LV impairment in patients with severe AS. A predictive model built with PCA-related shape modes achieved better perfor-mance in stratifying the occurrence of adverse events with respect to a baseline model using clinical demographic data as risk predictors. This study demonstrated the potential of SSA approaches to detect the association of complex 3D shape features with functional LV parameters.

Keywords: Aortic valve stenosis , Left ventricle , Statistical shape analysis

Purpose: The study aims were to assess the kinematic data, Internal-External (IE) rotation, and Antero-Posterior (AP) translation of the contact points between the femoral condyles and polyethylene insert and to develop a combined dynamic RSA-FE (Radiostereometric – Finite Element) model that gives results congruent with the literature. Methods: A cohort of 15 patients who underwent cemented cruciate-retaining highly congruent mobile-bearing total knee arthroplasty were analyzed during a sit-to-stand motor task. The kinematical data from Dynamic RSA were used as input for a patient-specific FE model to calculate condylar contact points between the femoral component and polyethylene insert. Results: The femoral component showed an overall range about 4 mm of AP translation during the whole motor task, and the majority of the movement was after 40° of flexion. Concerning the IE rotation, the femoral component started from an externally rotate position (− 6.7 ± 10°) at 80° of flexion and performed an internal rotation during the entire motor task. The overall range of the IE rotation was 8.2°. Conclusions: During the sit to stand, a slight anterior translation from 40° to 0° of flexion of the femoral component with respect to polyethylene insert, which could represent a paradoxical anterior translation. Despite a paradoxical anterior femoral translation was detected, the implants were found to be stable. Dynamic RSA and FE combined technique could provide information about prosthetic component's stress and strain distribution and the influence of the different designs during the movement.

Keywords: Dynamic RSA , FE analysis , Kinematics , Mobile bearing , TKA

Home-based recovery is gradually being used to reduce health-care costs; however, with a shorter stay in the hospital, the risk of growing adverse clinical outcomes exists, mainly due to the lack of motivation in the patient and on the difficulties in performing a strict control by the doctors. This is particularly true for patients who went under knee arthroplasty or total knee replacement who should strictly follow the effective recovery protocols delivered by the doctors. The development of tools for measuring the functional recovery of the operated joint is therefore deemed crucial both for the patient to feel motivated in performing the right number of exercises, and for the doctor that can follow him/her up remotely. One of the most recognized methods for assessing the correctness of a series of recovery exercises, is to monitor the pose of the patient in real-time so as to evaluate its posture in his range of motion. Accordingly, in this paper a novel hybrid approach to 3D human pose estimation is proposed. A first estimation of 2D body pose of the patient in the scene is given, then the depth information coming from the RGB-D sensor is exploited to estimate the joints 3D coordinates. The proposed algorithm proved to overcome the main limitation of using a pure 3D skeleton tracking algorithm during physiotherapy rehabilitation.

Keywords: 3D , Body tracking , Data fusion , Joints estimation , RGB-D camera

Autologous ear reconstruction is the preferred treatment in case of partial or total absence of the external ear. The surgery can be very challenging to perform and the aesthetic result highly dependent on the surgeon's “artistic skills”. In this context a preoperative planning and simulation phase based on the patient's specific anatomy may result crucial for the surgical outcome. In this work, starting from a case study, the elements necessary for an effective simulation are identified and a strategy for their interactive design and customization is devised with a perspective of a semi-automatization of the procedure.

Keywords: Additive manufacturing , Autologous ear reconstruction , Microtia , Preoperative planning , Reverse engineering

Convolutional neural networks are increasingly used in the medical field for the automatic segmentation of several anatomical regions on diagnostic and non-diagnostic images. Such automatic algorithms allow to speed up time-consuming processes and to avoid the presence of expert personnel, reducing time and costs. The present work proposes the use of a convolutional neural network, the U-net architecture, for the segmentation of ear elements. The auricular elements segmentation process is a crucial step of a wider procedure, already automated by the authors, that has as final goal the realization of surgical guides designed to assist surgeons in the reconstruction of the external ear. The segmentation, performed on depth map images of 3D ear models, aims to define of the contour of the helix, antihelix, tragus-antitragus and concha. A dataset of 131 ear depth map was created;70% of the data are used as the training set, 15% composes the validation set, and the remaining 15% is used as testing set. The network showed excellent performance, achieving 97% accuracy on the validation test.

We present our latest research on skin characterizations by using Contact Capacitive Imaging and High-Resolution Ultrasound Imaging with Machine Learning algorithms. Contact Capacitive Imaging is a novel imaging technology based on the dielectric constant measurement principle, with which we have studied the skin water content of different skin sites and performed image classification by using pre-trained Deep Learning Neural Networks through Transfer Learning. The results show lips and nose have the lowest water content, whilst cheek, eye corner and under-eye have the highest water content. The classification yields up to 83.8% accuracy. High-Resolution Ultrasound Imaging is a state-of-the-art ultrasound technology, and can produce high-resolution images of the skin and superficial soft tissue to a vertical resolution of about 40 microns, with which we have studied the thickness of different skin layers, such as stratum corneum, epidermis and dermis, around different locations on the face and around different body parts. The results show the chin has the highest stratum corneum thickness, and the arm has the lowest stratum corneum thickness. We have also developed two feature-based image classification methods which yield promising results. The outcomes of this study could provide valuable guidelines for cosmetic/medical research, and methods developed in this study can also be extended for studying damaged skin or skin diseases. The combination of Contact Capacitive Imaging and High-Resolution Ultrasound Imaging could be a powerful tool for skin studies.

Keywords: Capacitive imaging , High-resolution ultrasound , Machine learning , Skin image analysis , Skin texture , Skin thickness , Skin water content

Computer-assisted analysis of three-dimensional imaging data (radiomics) has received a lot of research attention as a possible means to improve the management of patients with lung cancer. Building robust predictive models for clinical decision making requires the imaging features to be stable enough to changes in the acquisition and extraction settings. Experimenting on 517 lung lesions from a cohort of 207 patients, we assessed the stability of 88 texture features from the following classes: first-order (13 features), Grey-level Co-Occurrence Matrix (24), Grey-level Difference Matrix (14), Grey-level Run-length Matrix (16), Grey-level Size Zone Matrix (16) and Neighbouring Grey-tone Difference Matrix (five). The analysis was based on a public dataset of lung nodules and open-access routines for feature extraction, which makes the study fully reproducible. Our results identified 30 features that had good or excellent stability relative to lesion delineation, 28 to intensity quantisation and 18 to both. We conclude that selecting the right set of imaging features is critical for building clinical predictive models, particularly when changes in lesion delineation and/or intensity quantisation are involved.

Keywords: Computed tomography , Intensity quantisation , Lesion delineation , Lung nodules , Radiomics , Stability , Texture features

Background: Accurate segmentation of pulmonary nodules on computed tomography (CT) scans plays a crucial role in the evaluation and management of patients with suspicion of lung cancer (LC). When performed manually, not only the process requires highly skilled operators, but is also tiresome and time-consuming. To assist the physician in this task several automated and semi-automated methods have been proposed in the literature. In recent years, in particular, the appearance of deep learning has brought about major advances in the field. Methods: Twenty-four (12 conventional and 12 based on deep learning) semi-automated-'one-click'- methods for segmenting pulmonary nodules on CT were evaluated in this study. The experiments were carried out on two datasets: A proprietary one (383 images from a cohort of 111 patients) and a public one (259 images from a cohort of 100). All the patients had a positive transcript for suspect pulmonary nodules. Results: The methods based on deep learning clearly outperformed the conventional ones. The best performance [Sørensen-Dice coefficient (DSC)] in the two datasets was, respectively, 0.853 and 0.763 for the deep learning methods, and 0.761 and 0.704 for the traditional ones. Conclusions: Deep learning is a viable approach for semi-automated segmentation of pulmonary nodules on CT scans.

Keywords: Computed tomography (CT) , Deep learning , Lung cancer (LC) , Pulmonary nodules , Segmentation

Purpose: The aim of this review is to discuss the most significant contributions about the role of Artificial Intelligence (AI) techniques to support the diagnosis of movement disorders through nuclear medicine modalities. Methods: The work is based on a selection of papers available on PubMed, Scopus and Web of Sciences. Articles not written in English were not considered in this study. Results: Many papers are available concerning the increasing contribution of machine learning techniques to classify Parkinson's disease (PD), Parkinsonian syndromes and Essential Tremor (ET) using data derived from brain SPECT with dopamine transporter radiopharmaceuticals. Other papers investigate by AI techniques data obtained by 123I-MIBG myocardial scintigraphy to differentially diagnose PD and other Parkinsonian syndromes. Conclusion: The recent literature provides strong evidence that AI techniques can play a fundamental role in the diagnosis of movement disorders by means of nuclear medicine modalities, therefore paving the way towards personalized medicine.

Keywords: 123I-FP-CIT SPECT , Artificial intelligence , Artificial neural network , Machine learning , Movement disorders , Nuclear medicine techniques , Parkinson's disease

The strong impulse recently experienced by the manufacturing technologies as well as the development of innovative biocompatible materials has allowed the fabrication of high-performing scaffolds for bone tissue engineering. The design process of materials for bone tissue scaffolds represents, nowadays, an issue of crucial importance and the object of study of many researchers throughout the world. A number of studies have been conducted, aimed at identifying the optimal material, geometry, and surface that the scaffold must possess to stimulate the formation of the largest amounts of bone in the shortest time possible. This book presents a collection of 10 research articles and 2 review papers describing numerical and experimental design techniques definitively aimed at improving the scaffold performance, shortening the healing time, and increasing the success rate of the scaffold implantation process.

Keywords: Bone regeneration , Bone tissue engineering , Porous materials

Aims: The aim of the study was to show a case with a midline diastema in a patient with high periodontal risks and gingival recessions treated with clear aligners. The objective was to predict and quantify root movements using a dedicated software that extrapolates data from the Cone Beam Computed Tomography (CBCT). Case Presentation: A 31-year-old female with a mandibular midline diastema asked for an aesthetic treatment. She had vertical bone loss on the lower central incisors, so a CBCT was necessary in order to plan the root movements. The purpose of the treatment was to avoid an uncontrolled tipping of the incisors and, therefore, a vestibular movement of the roots, which could cause serious periodontal problems. Conclusion: At the end of the treatment, the complete closure of the diastema and the radiographic healing of the vertical bone loss between mandibular central incisors were achieved. The superimpositions with the virtual setup demonstrated predictability of root movements of 76%.

Keywords: Clear aligners , Cone beam computed tomography , Midline diastema , Periodontal conditions , Periodontal disease , Virtual root setup

Laryngoscopes are used as diagnostic devices for throat inspection or as an aid to intubation. Their blade must be geometrically compatible with patients' anatomy to provide a good view to doctors with minimal discomfort to patients. For this reason, this paper was aimed to investigate the feasibility of producing customized blades. The customizable blade model was developed following a feature-based approach with eight morphological parameters. The thickness of such a blade was determined through numerical simulations of ISO certification tests, where the finite element mesh was obtained by morphing a ‘standard' mesh. The following procedure was applied: the model was built from the selected parameters; the blade was tested in silico; finally, the blade was produced by additive manufacturing with an innovative biodegradable material (Hemp Bio-Plastic® -HBP-) claimed to feature superior mechanical properties. The procedure evidenced that the mechanical properties of current biodegradable materials are unsuitable for the application unless the certification norm is revised, as it is expected.

Keywords: Additive manufacturing , Biodegradable materials , Feature-based modeling , Laryngoscope blades , Mesh-morphing , Parametric drawing , Patient-specific design

Robotic devices are being employed in more and more sectors to enhance, streamline, and augment the outcomes of a wide variety of human activities. Wearable robots arise indeed as of-vital-importance tools for telerehabilitation or home assistance targeting people affected by motor disabilities. In particular, the field of “Robotics for Medicine and Healthcare” is attracting growing interest. The development of such devices is a primarily addressed topic since the increasing number of people in need of rehabilitation or assistive therapies (due to population aging) growingly weighs on the healthcare systems of the nation. Besides, the necessity to move to clinics represents an additional logistic burden for patients and their families. Among the various body parts, the hand is specially investigated since it most ensures the independence of an individual, and thus, the restoration of its dexterity is considered a high priority. In this study, the authors present the development of a fully wearable, portable, and tailor-made hand exoskeleton designed for both home assistance and telerehabilitation. Its purpose is either to assist patients during activities of daily living by running a real-time intention detection algorithm or to be used for remotely supervised or unsupervised rehabilitation sessions by performing exercises preset by therapists. Throughout the mechatronic design process, special attention has been paid to the complete wearability and comfort of the system to produce a user-friendly device capable of assisting people in their daily life or enabling recorded home rehabilitation sessions allowing the therapist to monitor the state evolution of the patient. Such a hand exoskeleton system has been designed, manufactured, and preliminarily tested on a subject affected by spinal muscular atrophy, and some results are reported at the end of the article.

Keywords: hand exoskeleton , home assistance , mechatronics design , robotics , telerehabilitation , wearable robot

Cranioplasty is a procedure performed to repair defects in the human skull bone by surgically reconstructing the shape and function of the cranium. Several complications, both intraoperative and postoperative, can affect the procedure's outcome (e.g., inaccuracies of the reconstructed shape, infections, ulcer, necrosis). Although the design of additive manufactured implants in a preoperative stage has improved the general quality of cranioplasties, potential complications remain significant, especially in the presence of critical skin tissue conditions. In this paper, an innovative procedure to improve the chances of a positive outcome when facing critical conditions in a cranioplasty is described. The proposed approach relies on a structured planning phase articulated in a series of digital analyses and physical simulations performed on personalized medical devices that guide the surgeon in defining surgical cuts and designing the implant. The ultimate goal is to improve the chances of a positive outcome and a fast recovery for the patient. The procedure, described in extenso in the paper, was positively tested on a cranioplasty case study, which presented high risk factors.

Keywords: Additive manufacturing , Cranioplasty , Patient-specific implant , Skull reconstruction

Robotics for Medicine and Healthcare is undoubtedly an important emerging sector of the newborn third millennium. There are many aspects in which this branch of robotics already operates; in this article, the focus will be on the so-called “Robotic assistive technology”. In particular, a novel electromechanical design for an assistive Hand Exoskeleton System is presented here. Since freedom of movement plays a crucial role in making actually usable an assistive device, the main point of innovation of the proposed solution lies in the complete wearability of the resulting system: including mechanics, control electronics, and power supply. From the combination of the authors' previous experience with the improvements presented in this article comes a fully standalone tailor-made assistive device.

Spine surgery is based, nowadays, on the use of cutting-edge instruments that optimize the intervention processes in the operating room, with advantages that affect the patient himself. Among these, rapid prototyping is configured as a first-rate tool, thanks to its ability to detail the diagnostic treatment according to the specific pathological case under examination. An example of this technology is represented by the generation of a drilling template, to assist the surgeon in identifying the optimal direction of insertion of the pedicle screws, capable of significantly reduce intervention times, in addition to the inevitable exposure of the patient to ionizing radiation, to which he is subjected during a normal arthrodesis intervention procedure. The design of a drilling guide requires, however, a particular attention in identifying the undercuts present on the vertebral surface, those areas of the spinous process which, reported inside the cavity of the template, involve complications at the time of extraction. In parallel, it is vitally important to carry out an evaluation of its stability during its use. In this article, starting from the analysis of the interferences present during the insertion of the template, a semi-automatic correction model is proposed for the generation of a new profile of the same, which facilitates its extraction without causing injury to the vertebral regions involved from the contact with the mask.

Keywords: Computer‐assisted surgery , Pedicle screw fixation , Spine , Surgical template insertion optimization , Undercuts

Medical images do not provide a natural visualization of 3D anatomical structures, while 3D digital models are able to solve this problem. Interesting applications based on these models can be found in the cardiovascular field. The generation of a good-quality anatomical model of the heart is one of the most complex tasks in this context. Its 3D representation has the potential to provide detailed spatial information concerning the heart's structure, also offering the opportunity for further investigations if combined with additive manufacturing. When investigated, the adaption of printed models turned out to be beneficial in complex surgical procedure planning, for training, education and medical communication. In this paper, we will illustrate the difficulties that may be encountered in the workflow from a stack of Computed Tomography (CT) to the hand-held printed heart model. An important goal will consist in the realization of a heart model that can take into account real wall thickness variability. Stereolithography printing technology will be exploited with a commercial rigid resin. A flexible material will be tested too, but results will not be so satisfactory. As a preliminary validation of this kind of approach, print accuracy will be evaluated by directly comparing 3D scanner acquisitions to the original Standard Tessellation Language (STL) files.

Keywords: 3D printing , Heart model , Patient-specific modeling , Segmentation , Stereolithography

Purpose: COVID-19 has spread rapidly worldwide since its initial appearance, creating the need for faster diagnostic methods and tools. Due to the high rate of false-negative RT-PCR tests, the role of chest CT examination has been investigated as an auxiliary procedure. The main goal of this work is to establish a well-defined strategy for 3D segmentation of the airways and lungs of COVID-19 positive patients from CT scans, including detected abnormalities. Their identification and the volumetric quantification could allow an easier classification in terms of gravity, extent and progression of the infection. Moreover, these 3D reconstructions can provide a high-impact tool to enhance awareness of the severity of COVID-19 pneumonia. Methods: Segmentation process was performed utilizing a proprietary software, starting from six different stacks of chest CT images of subjects with and without COVID-19. In this context, a comparison between manual and automatic segmentation methods of the respiratory system was conducted, to assess the potential value of both techniques, in terms of time consumption, required anatomical knowledge and branch detection, in healthy and pathological conditions. Results: High-quality 3D models were obtained. They can be utilized to assess the impact of the pathology, by volumetrically quantifying the extension of the affected areas. Indeed, based on the obtained reconstructions, an attempted classification for each patient in terms of the severity of the COVID-19 infection has been outlined. Conclusions: Automatic algorithms allowed for a substantial reduction in segmentation time. However, a great effort was required for the manual identification of COVID-19 CT manifestations. The developed automated procedure succeeded in obtaining sufficiently accurate models of the airways and the lungs of both healthy patients and subjects with confirmed COVID-19, in a reasonable time.

Keywords: COVID-19 , CT , Lungs , Respiratory system , Segmentation

Objectives: Implantoplasty (IP) is a treatment option for peri-implantitis. Mechanical concerns were raised on fracture resistance of implants subjected to this procedure. This study aimed to compare two methods of IP in terms of implant wear and fracture resistance, and of surface topography. Material and methods: Eighteen cylindrical screw-shaped dental implants (4 mm diameter, 13 mm length) with an external hexagonal connection were used. IP was performed on the first 6-mm implant surface with a sequence of burs or diamond sonic tips, both followed by an Arkansas finishing. IP duration and implant weight variation were recorded. Micro-computed tomography (micro-CT) was used to evaluate material loss. Implant fracture resistance was assessed by static compression test. Surface topography analysis was performed with a stylus profilometer. Scanning electron microscopy–energy dispersive X-ray spectroscopy (SEM-EDS) was applied for implant surface morphology and elemental characterization. Results: Micro-CT showed less material loss in sonic compared to burs. No statistically significant difference was found between the mean fracture resistance values reached in bur and sonic, both followed by Arkansas, and with respect to control. IP performed with burs led to a smoother surface compared to sonic. Equivalent final surface roughness was found after Arkansas in both IP procedures. SEM-EDS showed a deburring effect associated to sonic and revealed carbon and aluminum peaks attributable to contamination with sonic diamond tips and Arkansas bur, respectively. Conclusions: IP with sonic diamond tips was found to be more conservative in terms of structure loss. This could have a clinical relevance in case of narrow-diameter implants.

Keywords: bone implant interactions , CT imaging , surface chemistry